Smart Vehicle Technology as a Transit Solution Commercial Considerations for Planning and Deployment

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CUTRICCRITUC

SMART VEHICLE TECHNOLOGY AS A TRANSIT SOLUTION COMMERCIAL CONSIDERATIONS FOR PLANNING AND DEPLOYMENT IN CANADA

AUTHORS

TITASH CHOUDHURY, SOCIAL SCIENTIST LOW CARBON SMART MOBILITY

MARIA PINTO, PROJECT MANAGER: RAIL INNOVATION AND SMART MOBILITY

DR. JOSIPA PETRUNIĆ, PRESIDENT & CEO

COPYRIGHT © 2023

Information in this document is to be considered the intellectual property of the Canadian Urban Transit Research and Innovation Consortium (CUTRIC) in accordance with Canadian copyright law. The material in it reflects CUTRIC’s best judgment, considering the information available to it at the time of preparation. Any use that a third party makes of this report, or any reliance on or decisions to be made based on it, are the responsibility of such third parties. CUTRIC accepts no responsibility of such third parties. CUTRIC accepts no responsibility for damages, if any, suffered by any third party as a result of decisions made or actions based on this report.

CUTRIC-CRITUC
Knowledge Series in Low Carbon Smart Mobility Innovation. Volume 4, No. 1 (2023).

1. Background

2. Goal & Objectives

3. Overview of autonomous shuttle transit application considerations

6. Data Collection Methodology

7. Standardization of (V2V) and (V2I) communication protocols & standards

7.1 Autonomous sensor communication: (V2I), (V2V), (V2X)

7.2 LIDAR, RADAR and camera communication standardization

7.3 Overview

8. Central operations systems for multiple manufacturers

8.1 Standardization of DSRC units with other road-side infrastructure across multiple manufacturers

8.2 Operations and control room standardization

9. Ridership growth strategy and planning

9.1 Passenger safety

9.2 Increase in accessibility

10. Standardization of cyber security protocols

11. Charging infrastructure standardization

1 4 5 6 7 8 8 17 17 20 23 28 28 28 28 35 35 35 36 Contents
List of figures List of acronyms Executive summary
1
37 TABLE OF CONTENTS

12. Predictive performance modelling of electric low-speed autonomous shuttles 12.1

38 38 39 39 40 44 45 46 48 49
Modelling
and
Modelling methodology
Speed and topography profile generation
Results of feasibility study 13. Challenges 14. The Way Forward 15. Recommendations 16. Conclusions References TABLE OF CONTENTS 2
inputs
assumptions 12.2
12.3
12.4

LIST OF TABLES

TABLE 1: TABLE 2: TABLE 3: TABLE 4: TABLE 5: TABLE 6: TABLE 7: TABLE 8: TABLE 9: TABLE10:

TABLE 11: TABLE 12: TABLE 13: TABLE 14: TABLE 15: TABLE 16: TABLE 17:

STANDARDIZATION OF COMMUNICATION: (V2I)

STANDARDIZATION OF COMMUNICATION: (V2V)

STANDARDIZATION OF COMMUNICATION: (V2X)

STANDARDIZATION OF COMMUNICATION: NAVIGATING WITH INFRASTRUCTURE IMPEDIMENTS

STANDARDIZATION AND DEPLOYMENT OF E-LSA INFRASTRUCTURE

RIDERSHIP GROWTH STRATEGY: DEPLOYMENT OF E-LSAS AS RIDERSHIP-INCREASING MECHANISMS

CYBER SECURITY: HACKABILITY OF ONBOARD UNITS (OBUS) ON SHUTTLES AND THE V2I AND V2V CONNECTIONS TO ROAD-SIDE UNITS (RSUS) (OBU-RSU AND OBU-OBU)

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AUTONOMY
OF AUTONOMOUS SYSTEMS GENERAL OPERATIONS OPERATIONAL SAFETY PASSENGER SAFETY ASSET MANAGEMENT AND MAINTENANCE PILOT IMPLEMENTATION AND PLANNING PROCESS ENERGY EFFICIENCY (KWH/KM) OF THE VEHICLE ROUND TRIP ENERGY CONSUMPTION (KWH) MAXIMUM NUMBER OF TRIPS WITH ONLY-DEPOT CHARGING TOTAL OPERATIONAL TIME (IN MINUTES) 11 10 11 12 12 13 13 14 14 15 16 41 16 16 40 42 42
STRENGTH: DISTANCE AND ROBUSTNESS
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LIST OF FIGURES

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4 FIGURE 1. AUTONOMOUS SHUTTLE TRANSIT SYSTEM NETWORK 10 FIGURE 2: CUTRIC’S PILLARS FOR THE KNOWLEDGE SERIES IN 2022 11 FIGURE 3. ENERGY EFFICIENCIES 30 FIGURE 4. ENERGY CONSUMPTION BY THE CHARGER 32 8 9 40 41

LIST OF ACRONYMS

5G - FIFTH GENERATION

ACATS - (PROGRAM TO) ADVANCE CONNECTIVITY AND AUTOMATION IN THE TRANSPORTATION SYSTEM

AD - ACCELERATE DEVELOPMENT

AV - AUTONOMOUS VEHICLES

BEB – BATTERY ELECTRIC BUS

CUTRIC – CANADIAN URBAN TRANSIT RESEARCH & INNOVATION CONSORTIUM

DRT - DURHAM REGION TRANSIT

DSRC – DEDICATED SHORT RANGE COMMUNICATION

E-LSA - ELECTRIC LOW-SPEED AUTONOMOUS SHUTTLES

EV - ELECTRIC VEHICLES

FCEB – FUEL CELL ELECTRIC BUS

LIDAR - LIGHT DETECTION AND RANGING

MTO – MINISTRY OF TRANSPORTATION

RADAR - RADIO DETECTION AND RANGING

ODD – OPERATIONAL DESIGN DOMAIN

OSPE - ONTARIO SOCIETY OF PROFESSIONAL ENGINEERS

RAT - RADIO ACCESS TECHNOLOGY

ROW – RIGHT OF WAY

SCMS- SECURITY CREDENTIAL MANAGEMENT SYSTEMS

SME - SUBJECT MATTER EXPERTS

TC – TRANSPORT CANADA

TTC - TORONTO TRANSIT COMMISSION

UN-ECE - UNITED NATIONS-ECONOMIC COMMISSION FOR EUROPE

YRT – YORK REGION TRANSIT

ZEB – ZERO EMISSION BUSES

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EXECUTIVE SUMMARY

This report, entitled “Smart Vehicle Shuttle Technology as a Transit Solution: Commercial Considerations for Planning and Deployment in Canada”, is part of CUTRIC’s digital journal, Knowledge Series in Low Carbon Smart Mobility Innovation.

The report is informed by in-depth qualitative interviews with industry and transit stakeholders and a global literature review. This report assesses the feasibility of smart vehicle shuttle technology as part of the Canadian transit system and mechanisms for how smart autonomous shuttles should be integrated across Canadian cities.

This report identifies a comprehensive list of thematic areas to address the initial stages of deploying electric low-speed autonomous shuttles (e-LSAs) – also referred to as “autonomous shuttles” or “autonomous vehicles” (AVs) – in support of technical, industrial, governance, operational and social planning. The report summarizes CUTRIC’s technical and predictive modelling using Rout∑.i™ 2.0. from a sample feasibility study of e-LSA shuttle deployment in the Canadian landscape. This modelling establishes the energy performance of shuttles under different duty cycles.

The report also includes lessons learned from autonomous vehicle (AV) shuttle projects recently cancelled in Ontario (i.e., in Durham Region and Toronto, respectively) to guide transit agencies in understanding the challenges associated with e-LSA shuttle deployments.

Over the last four years, CUTRIC’s experience leading the National Smart Vehicle Demonstration and Integration Trial project has enabled collaboration with stakeholders from industry, academia and all levels of government. The knowledge collected showcases lessons learned regarding AV deployments in Canadian municipal and transit contexts. This report is part of a living document that supports CUTRIC’s National Smart Vehicle Demonstration and Integration Trial project planning efforts. Stakeholders such as Transdev, the City of Montreal, the City of Toronto, Durham Region Transit, and AutoGuardian have been essential to delivering insights and knowledge contained in this report.

Outlining a pathway forward, this report provides technical specifications and identifies common challenges and opportunities faced by agencies wanting to deploy smart vehicle shuttle technologies in Canada. In-depth interview results with transit stakeholders and industry leaders along with results from a global literature review demonstrate that autonomous shuttles are a viable transit solution in Canada.

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1. BACKGROUND

Electric low-speed autonomous shuttles (e-LSAs) constitute a type of “smart shuttle” that presents significant opportunities for Canadian cities as they aim to shift away from fossil fuel intensive technologies toward clean energy.

Two years ago, the Canadian Urban Transit Research and Innovation Consortium (CUTRIC) was one of 15 organizations awarded a research contract by Transport Canada through its Advance Connectivity and Automation in the Transportation System (ACATS) program. With this funding, CUTRIC has completed the following research to demonstrate opportunities and challenges associated with e-LSA shuttle deployments as part of the Canadian transit network:

A detailed energy consumption feasibility analysis for electric low-speed automated shuttle deployment on nine first-kilometre/last-kilometre routes;

A detailed transit ridership impact analysis based on e-LSA deployment along three selected first-kilometre/last-kilometre routes;

An overview of communication software and hardware systems and applicable standards;

An overview of cybersecurity software and hardware risks, vulnerabilities, and standards; and

A qualitative overview of consultations with CUTRIC’s long-standing manufacturer and transit agency partners and members from 2018 to 2021 to advance planning for a National Smart Vehicle Demonstration and Integration Trial. [1]

Over the last four years, CUTRIC’s experience leading the National Smart Vehicle Demonstration and Integration Trial has enabled collaboration with stakeholders from industry, academia and all levels of government. The knowledge collected includes lessons learned regarding AV deployments in Canadian municipal and transit contexts. This report – entitled Smart Vehicle Shuttle Technology as a Transit Solution: Commercial Considerations for Planning and Deployment in Canada – is part of a living document that supports CUTRIC’s National Smart Vehicle Demonstration and Integration Trial project with Canadian transit agencies.

Stakeholders such as Transdev, the City of Montreal, the City of Toronto, Durham Region Transit, and AutoGuardian have been essential to delivering insights and knowledge contained in this report.

1 The ACATS program distributed C$2.9 million across 15 di erent organizations to support research, studies and technology evaluations; the development of codes, standards and guidance materials; and capacity-building and knowledge-sharing activities.

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1. 2. 3. 4. 5.
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2. GOAL & OBJECTIVES

This report aims to identify core themes concerning the planning and implementation of e-LSAs in Canada. The objectives of this report are as follows:

To identify core thematic technological areas of concern regarding CUTRIC’s National Smart Vehicles Demonstration and Integration Trial.

To provide technical outputs of modelling work using CUTRIC’s Rout∑.i™ 2.0 electric bus and electric shuttle predictive analysis toolkit to analyze autonomous shuttle performance under different duty cycles on proposed transit or public mobility routes.

To provide a comprehensive market scan of autonomous shuttles as part of Canada’s transit solution toolkit.

To enable transit agencies to plan for AV deployments within their transit systems.

To answer technical queries related to AV deployments based on vehicle interactions with roadway and community infrastructure.

To assess findings and best practices emerging from recently deployed and cancelled AV shuttle projects (i.e., in Durham Region and Toronto) to address unexpected events better and overcome similar situations in other transit communities.

3. OVERVIEW OF AUTONOMOUS SHUTTLE TRANSIT APPLICATION CONSIDERATIONS

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Figure 1. Autonomous shuttle transit system network Infrastructure Stations & Routes Policies, Legislation & Procedure e-LSA Transit System Smart Vehicles Passengers

The planning process for autonomous shuttle technology includes several opportunities and challenges within the system. Today, planning for electric low speed autonomous shuttle deployments as transit solutions gives rise to numerous questions regarding the trustworthiness of the technology as a transit solution.

From 2019 to 2021, as part of the data collection process informing this report, CUTRIC conducted focus group consultations and in-depth interviews with transit agencies, industry stakeholders and government entities regarding the opportunities, challenges and solutions allied to autonomous shuttle technology deployments in public fleet contexts [2].

The conclusion of these consultations and interviews is that a roadmap for autonomous shuttle integration into transit systems in Canada must include planning considerations associated with vehicle and infrastructure communications, vehicle and infrastructure operations, vehicle and infrastructure cyber security, passenger safety, ridership strategies, and asset management and maintenance.

Communication Protocols

Standardization of Vehicle -to-Vehicle (V2V), Vehicle-to-Infrastructure (V2I), Vehicle-to-Everything (V2X)

Original Equipment Manufacturer (OEMs)

Centralized operating system for diverse OEMs

Cybersecurity Protocols

Standardization of cyber management procedures

Ridership Growth Strategy

Strategy and planning to increase passnger volumes

(Charging) Infrarstructure

Interoperability of autonomous shuttle charging

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Figure 2: CUTRIC’s pillars for the Knowledge Series in 2022

Tables 1 to 12 below showcase thematic questions regarding smart vehicle deployments that CUTRIC researchers use as a guideline for the global literature review and in-depth interviews with stakeholders carried out between 2021 to 2022 to update earlier consultation outcomes obtained from 2019 to 2021.

Table 1. Standardization of communication: vehicle-to-infrastructure (V2I)

Standardization of communication: vehicle-to-infrastructure (V2I)

1. Can vehicles manufactured by different manufacturers communicate with standardized dedicated short-range communication (DSRC) units?

2. What is the latency period of communication between e-LSA shuttles and DSRC units?

3. How does the e-LSA shuttle handle the latency when there are multiple sensor inputs?

4. How do shuttles navigate bus loops?

• Operations within busy bus terminals or terminals with dynamic platform assignments?

• Single lane vs. multi-lane roundabouts?

5. How do shuttles navigate roundabouts (partially or fully)?

6. Can the e-LSA shuttle operate on a road with one northbound lane and one southbound lane?

7. How would the e-LSA shuttle navigate and operate along road portions with raised bicycle lanes?

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Table 2. Standardization of communication: vehicle-to-vehicle (V2V)

Standardization of communication: vehicle-to-vehicle (V2V)

1. How is a communication protocol developed in the case of an emergency?

2. What is the present latency period to capture V2V signals?

3. How will the e-LSA shuttles interact with emergency vehicles responding to emergencies?

4. If an attack or medical emergency were to occur on an autonomous shuttle, and the on-board operator could not respond to the incident, could control room operators force an immediate stop and signal an emergency alert on the autonomous shuttle to warn surrounding vehicles?

Table 3. Standardization of communication: vehicle-to-everything (V2X)

Standardization of communication: vehicle-to everything (V2X)

1. How will e-LSA shuttles operate and/or interact with pedestrians and bicycles, such as pedestrian crossings and bicycle lanes/markings at roundabouts along a route, or pedestrian crossings within transit facilities?

2. During the summer season, restaurants can extend their patio for outdoor dining in some jurisdictions. How will this affect e-LSA shuttle operations?

4. How does the e-LSA shuttle operate around small inanimate and animate objects?

5. How will the AV avoid hitting people with disabilities who are unable to get out of the e-LSA shuttle path?

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Table 4. Standardization of communication: navigating with infrastructure impediments

Standardization of communication: navigating with infrastructure impediments

1. How will the e-LSA shuttle navigate/operate at railway crossings?

2. How will the e-LSA shuttle operate with on-street parking?

3. Will the e-LSA shuttle stop along the route to load/unload passengers at designated stops?

4. What is the required minimum/maximum distance between each AV designated stop?

5. Can the e-LSA shuttle stop at existing bus stops?

6. Are there any modifications required to existing stops/ infrastructure/ amenities? Can the e-LSA shuttle adjust stopping locations due to temporary obstructions (construction, winter weather/plow casts, etc.)?

Table 5. Standardization and deployment of E-LSA infrastructure

Standardization and deployment of autonomous shuttle infrastructure

1. What is the biggest challenge facing the deployment of charging infrastructure?

2. What is the approach to standardizing e-LSA shuttle charging infrastructure?

3. What is the approach to standardizing the DSRC units?

4. What is the approach to standardizing the WIFI and network?

5. What is the biggest challenge in deploying DSRC units?

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Table 6. Ridership growth strategy: deployment of E-LSAs as ridership increasing mechanisms

Deployment of E-LSAs as Ridership-increasing Mechanisms

1. What is your ridership engagement plan?

2. How do you best select points of interest to accommodate and support the achievement of ridership targets (shuttle stops and routes)?

3. What is the impact of e-LSA shuttles on ridership (including both positive and negative insights)?

Table 7. Cyber security: hackability of onboard units (OBUs) on shuttles and the V2I and V2V connections to road-side units (RSUs) (OBU-RSU and OBU-OBU)

Cyber security: Hackability of onboard units (OBUs) on shuttles and the V2I and V2V Connections to Road-side Units (OBU-RSU and OBU-OBU)

1. What is your ridership engagement plan?

2. How do you best select points of interest to accommodate and support the achievement of ridership targets (shuttle stops and routes)?

3. What is the impact of e-LSA shuttles on ridership (including both positive and negative insights)?

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Table 8. Autonomy strength: distance and robustness of autonomous systems (LIDAR, RADAR, cameras)

Autonomy strength: distance and robustness of autonomous systems (LIDAR, RADAR, cameras)

1. How does the e-LSA shuttle recover or operate when there is an obstruction to the sensory systems?

2. What is the range in which the sensing technology can operate?

3. How do you standardize sensing technology?

4. How does the sensing technology operate under diverse and extreme weather conditions (ex., heavy snowfall, rainfall, wind and heat waves)?

Table 9. General operations

General operations

1. Can the e-LSA shuttle operate on a road with one northbound lane and one southbound lane?

2. Who can redirect the shuttle/monitor?

3. What is the latency period of communication between the control room and the e-LSA shuttle?

4. Can the e-LSA shuttle function without a dedicated shuttle lane?

5. Can the route length exceed 3 km?

6. Can the e-LSA shuttle operate on dynamic routings?

7. Can the e-LSA shuttle operate on a centre turning lane?

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Table 10. Operational safety

Operational safety

1. Can the e-LSA shuttle operate at uncontrolled/unsignalized intersections?

2. Can the e-LSA shuttle make a left turn at an uncontrolled intersection?

3. Can the e-LSA shuttle operate in mixed traffic?

4. Can the e-LSA shuttle make left turns? How many?

5. How does the e-LSA shuttle function within transit facilities (i.e., pulling out of sawtooth bays)?

6. Will the e-LSA shuttle replace/compete with existing public transit service?

7. Can the e-LSA shuttle operate going up or down a hill?

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Passenger safety

1. How should an agency mitigate passenger infractions in cases when the passenger does not follow on-board protocols? How will the e-LSA shuttle recognize these issues and handle them?

2. How should an agency make it accessible for children, infants, older adults, and/or people with disabilities ride the e-LSA shuttle?

3. What are the policies and procedures addressing the onboarding and offboarding of passengers safely?

4. What is the strategy for the safety of passenger communications?

Asset management and maintenance

1. How will vandalism of assets be avoided?

2. Is there any asset management or protection plan for the e-LSA shuttle, charging infrastructure and sensing technologies?

Pilot implementation and planning process

1. Is there a standardized checklist that has been developed to successfully complete the pilot?

2. Is there a liability policy that will assist the stakeholders in maintaining safety protocols?

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Table 13. Pilot implementation and planning process

6. DATA COLLECTION METHODOLOGY

The section below contains the outcomes of a global literature review that aims to address the questions posed above. The literature review includes journal articles, white papers, workshop publications, case studies, world congresses, and news articles. It also includes the outcomes of a series of semi-structured interviews with manufacturers and transit agencies aimed at identifying answers to the queries posed above. Interviews took place over Zoom video calls and follow-up questionnaires were provided in an email with respondents emailing qualitative responses. Lastly, researchers integrate the outcomes from additional focus group consultation sessions conducted with manufacturers and subject matter experts in cybersecurity and autonomous shuttle technology, specifically. Focus groups were also conducted over Zoom video calls with several manufacturers in group format. During these interviews and consultation sessions, participants provided verbal consent to use qualitative data collected as a primary source in the report. Interview results have been anonymized, aggregated, and analyzed below.

7. STANDARDIZATION OF VEHICLE-TO-VEHICLE (V2V) & VEHICLE-TO-INFRASTRUCTURE (V2I) COMMUNICATION PROTOCOLS AND STANDARDS

Canada is already taking steps to develop standards for autonomous shuttles as transit solutions. In its Testing Automated Driving Systems in Canada report (Version 2.0) [3], Transport Canada (TC) raises questions about autonomous shuttle manoeuverability and operations under different scenarios insofar as they are affected by variations in road geometry, challenges in adverse weather conditions, and interactions between the vehicle and general traffic, cars, cyclists, and pedestrians.

Transport Canada’s guidelines document (first published in 2018 and updated in subsequent years) integrates consultations with provincial and territorial representatives of the Canadian Council for Motor Transport Administrators (CCMTA). The report provides a preliminary baseline of national best practices to help Canadian transit agencies safely conduct trials involving vehicles equipped with autonomous and connected technologies.

Transport Canada has already reported that infrastructure, including roadway and other building or sidewalk infrastructure, should be modified to support test routes and ensure the highest safety levels. Adding supplementary infrastructure at crossings on road intersections with V2I connectivity or other appropriately redundant sensors can improve navigation and safe vehicle deployment. Transport Canada also recommends that trials and pilots should ensure infrastructure modifications are made to test routes so that negative safety impacts for vulnerable and regular road users [3] do not become a systemic problem.

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In 2021, CUTRIC published a report on autonomous shuttle technologies in which it offers a detailed history of various organizations involved in standardizing vehicle-to-vehicle and vehicle-to-infrastructure communications for AVs [2]. That report outlines the United States’ Department of Transportation Security Credential Management Systems (SCMS), which certifies electronic devices and enables legitimate messages to be sent that contain an SCMS certificate digital signature.

The SCMS recognizes the importance of prioritizing the standardization of autonomous shuttle communications to enable vehicles to share safety and mobility information.

In tandem, the OmniAir Consortium has established a dedicated short-range communication (DSRC) V2V, V2I and cellular-V2X (vehicle-to-everything) (“C-V2X”) device certification procedure that helps ensure compliance with technical standards that certify the interoperability of the device.

Lastly, the Canadian Standards Association (CSA) Group, which received funding through Transport Canada’s ACATS program to assess gaps in the AV standards landscape, developed a roadmap to strengthen connected and autonomous vehicle codes and standards across Canada and the United States through its Connected and Automated Vehicle Advisory Council (CAVAC) [2]. That roadmap indicates several gaps in standards that transit agencies must overcome individually when planning for and deploying autonomous shuttle technologies. The CSA concludes more standards for V2V and V2I need to be developed in Canada. Pilot demonstration and deployment projects can help to achieve this goal. All of these reports point to a similar conclusion – standards that create common V2I, V2V, and V2X communications and common best practices to navigate infrastructural impediments are still largely missing from the Canadian transit landscape with relation to autonomous shuttles.

Yet, they are essential elements to consider when designing vehicle deployments on city streets that best support transit. Specific guidance recommendations issued by Transport Canada should be incorporated into the design of AV systems for transit, while certification procedures, such as those developed by the OmniAir consortium, should be pursued to ensure all components of the system are deployed as safely as possible in their interaction with existing infrastructure.

Vehicle-to-infrastructure (V2I) communication is vital to successfully deploying autonomous shuttles in Canada. Research into V2I standardization in the global landscape is growing, but much of the current research focuses on the effectiveness of V2I communication using fifth-generation (5G) networks [4-6]. In Canada today, AV shuttles will not have access to 5G communication networks. Though the future of 5G shows promise, CUTRIC recommends beginning deployment projects with currently available and already standardized DSRC technology [7].

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As part of the research completed for this study, CUTRIC interviewed autonomous shuttle manufacturers and operators to determine how shuttles have historically navigated bus loops, busy bus terminals, and terminals with dynamic platform assignments and/or single lane versus multi-lane applications. According to manufacturers interviewed here, current shuttle technology only works on a predetermined pathway that is commissioned ahead of time. Vehicles cannot and should not deviate from the predetermined path in autonomous mode. An onboard operator who can take over and manually operate the shuttle during uncertain instances when there are road obstructions along the route (i.e., a bus blocking a station or a temporary signal on the road) should always be available.

Additionally, a relevant feature of autonomous shuttles that manufacturers and operators point to is their bidirectionality. Most shuttles are bi-directional vehicles. The front and rear are always mirror images of one another, but the front is clearly distinguished. Due to this feature, the shuttles can easily turn around to face the opposite direction and turn to use an opposing lane when it is necessary or safe to do so. However, a shuttle must be programmed ahead of time to know how to shift laterally.

The vehicles can also operate within a single-lane environment. In these cases, the shuttle can switch its rear to serve as its front and vice versa to use a single lane in both directions. A single-lane situation that uses the bidirectional functionality of the shuttles may be difficult to navigate when multiple shuttles are operating along the same lane. In these cases, short passing lanes or pull-over spaces are required to allow shuttles to pass one another in a single-lane environment.

Manufacturers and operators have confirmed that autonomous shuttles (as mobility systems) have been operating on public roads over the past half-decade. Some worldwide pilot projects have even included two lanes with two directions of travel in full AV mode. Such complex operations require complicated software programming for the shuttles. Any AV shuttle route that consists of a northbound and southbound lane or eastbound and westbound lane requires the shuttle to be programmed appropriately to navigate such environments. At the end of each route allocation, shuttles also need enough space for the vehicle to turn around and enter the opposing lane by making a U-turn. Similarly, a partial or full roundabout can be programmed into an autonomous shuttle system. The shuttle can be automatically programmed to navigate these intersections safely based on route information and infrastructure mapping.

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7.1. Autonomous sensor communication: vehicle-to-infrastructure (V2I), vehicle-to-vehicle (V2V), vehicle-to everything (V2X)

Shuttle sensor communication enables vehicles to share important safety and mobility information. It includes vehicle-to-infrastructure (V2I), vehicle-to-vehicle (V2V), vehicle-to everything (V2X), as well as details about navigating infrastructural impediments. Interactions between sensors and the autonomous shuttles that help to guide, track and safeguard the shuttles are considered integral parts of any deployment project.

As already noted in this report, autonomous shuttles can be programmed to operate in different scenarios and within differing roadway environments. For these shuttles to operate smoothly, they require direct links to sensors, and the communications pathways must be unhindered by infrastructure or other potential impediments.

The following section explains the role of sensors in effective autonomous shuttle applications.

Over the past decade, several studies have predicted how autonomous shuttles will navigate roadways using software to simulate traffic operations combined with vehicle capabilities [1]. A model developed in Columbus, Ohio merges traffic simulations with autonomous shuttle hardware platform data to see how a shuttle might process information and/or make a decision in real-time traffic conditions [8]. The researchers combined traffic simulation data with three-dimensional LIDAR data and camera data. The model assesses various scenarios, including route changes, varied sensor parameters, differing percentages of autonomous vehicles in traffic, altering traffic lights, and different types of autonomous shuttle makes and models.

The study shows that predictive modelling can help agencies and fleet operators foresee whether a shuttle will function or fail in various complex situations before deployment. Predictive modelling that includes energy consumption assessments and charging times, route complexity and traffic interaction scenarios is essential for transit systems planning successful deployments.

Another pilot project in Oslo, Norway studies autonomous shuttle interactions with other transportation modes using video analysis data [9]. The study demonstrates different levels of risk associated with autonomous shuttles and identifies measures to address those risks. The Norwegian government has identified an interest in introducing autonomous shuttles into its transportation system, but challenges have hindered the deployment of these devices. The Oslo pilot project examines various human concerns, such as a cyclist’s response when an autonomous shuttle approaches and passengers’ responses when the shuttle stops unexpectedly.

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The study offers an opportunity to explore various interactions with autonomous shuttles, including live video analysis of encounters at “T” intersections on the road. The outcome of this study points to the importance of using pilot deployments to monitor pedestrians, cyclists, passengers/riders and other driver reactions to shuttle deployments to modify routes, speeds, or vehicle behaviour in real-time post-deployment to optimize its operations within a community setting, while also serving community members. Sensors that monitor human engagement with autonomous shuttles (e.g., onboard cameras) should be integrated into autonomous shuttle deployments to enable data flow.

Research allied to autonomous shuttles also point to design needs when building a shared bicycle and autonomous shuttle lane as a symbiotic transportation system. Combined or dual spaces and laneways can help advance active transportation and low-carbon mobility solutions [7]. However, there is no current “optimal” form of a shared road design for cyclists and shuttles making it difficult for transit agencies or cities to know how best to co-design cycling with shuttles. This assessment suggests that transit agencies should consider a complete street approach incorporating active transportation networks in their design plans for autonomous shuttle deployments. Doing so requires innovation and a global standard for design efforts. Sensors that monitor bicycle usage on AV shuttle laneways may help to generate the type of robust data sets needed for improved vehicle designs and roadway designs for AVs in the future. Additionally, onboard sensors can help shuttles identify cyclists and alter speed or positioning to enable cyclists to pass shuttles while on the route and in real-time when operating on shared laneways and spaces.

Through interviews carried out for this report, CUTRIC found that if bicycle lanes are to form part of the operational laneway for an AV shuttle route, the vehicle’s sensors must determine the cyclist's location. Based on current sensor technologies, the shuttle can achieve this task; in such an instance, it would slow down and follow the cyclist until it is no longer in front of the shuttle and no longer causes an impediment. If the cyclist in front of the shuttle comes to a stop, the autonomous shuttle would also stop. However, interview participants also note that if there is roadway space for the shuttle to manoeuvre manually around cyclists at a stopping point, then an onboard operator could take over controls for the vehicle and manually move around the cyclist before engaging in autonomous mode again. Shuttles today are not programmed to perform this type of action independently.

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Intelligent transportation systems (ITS) architecture in Canada can map physical and functional objects within the built environment through cameras and in-road sensors [10]. For autonomous shuttles, the self-driving software determines the route and path of the vehicle through perception, decisions, and actions. Sensor configuration and calibration are optimized for external object detections and positioning. Autonomous shuttle sensors today can identify pedestrians, cyclists, and vehicles as moving objects when they are within the direct path of the shuttle. Shuttles will treat all objects as moving objects travelling at specific speeds. Shuttle sensors can identify the speed of external objects and impediments and follow along at the same or lessened speed, or stop behind the object if it stops, regardless of whether it is a pedestrian, cyclist or vehicle. In a busy or complex environment, however, an operator should take control of the vehicle to avoid collisions. Some shuttles and allied sensors can be pre-programmed to specify priority zones or conditions within which an operator must take control. For example, vehicles can be programmed to sense a busy environment, allowing an operator in a control room to watch pedestrian crossings and monitor intersections (like single stop sign intersections).

7.2 LIDAR, RADAR and camera communication standardization

This section of the report reviews sensing technologies (ex., LIDAR, RADAR, camera, and ultrasonic sensors), which can support and enable maximum levels of safety and system redundancy through a process called “sensor fusion.” In its 2021 report, CUTRIC reviewed various sensory technologies, their purpose, functionalities and challenges (49-56) [2]. A high-level review of these findings is offered below.

Light detection and ranging technology (LIDAR) is critical to autonomous shuttle communications.

This sensory technology detects objects, shapes, locations, and distances using three-dimensional (3D) mapping of a vehicle’s surroundings. Based on information collected from interviews, manufacturers of sensor technology identify that the quality of this technology differs based on its make and model. Different manufacturers produce different types of LIDAR technology. The average range detected by communication via different makes and models of LIDAR technology is approximately 200 metres.

An ongoing lack of standardization across sensory systems is an issue that needs to be addressed in demonstration projects going forward. Multiple shuttles operating in the same environment using differing sensory technologies may result in unsafe practices and a lack of communication overall.

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Transit agencies and fleet operators are faced with the difficult decision as to which standardized sensory system to deploy within their environments. This decision needs to be made at an early stage of project planning. Without ongoing LIDAR testing in demonstration environments, this decision will remain a complex and difficult one for transit agencies to navigate in future deployments.

CUTRIC’s market scan of LIDAR technology shows the cost of LIDAR systems has dropped over the past few years [2]. For example, Velodyne’s industry-leading LIDAR systems ranged from an estimated US$100,000 for its 128-laser spinning LIDAR model to approximately US$16,000 for its six laser spinning model [2]. As of 2021, Velodyne is in the process of offering a solid-state LIDAR model, expected to cost less than US$1,000. Currently, the model is on the market, costing US$500 [11].

However, this lower cost aligns with lower-end performance regarding the spinning LIDAR systems. LIDARs manufactured by Velodyne are widely used in autonomous shuttles and are known unofficially as the “standard” in the industry.

According to the data collected, alternative sensor technologies, such as cameras, also have an average detection range of up to 150 metres.

A 2016 study published in the Journal of Sensor Technology shows the design and shape of a sensor are also essential for optimizing V2I and V2V detection and measurement [12]. In the study, researchers in Mexico analyze data through wireless interconnectivity using specific algorithms during sensor prototype development and testing. The study highlights detection system capabilities with the curvature that enhances conditions in the transmission and reception process obtained by the sensor movement.

Autonomous shuttle navigation around small and large objects

Autonomous shuttles are highly sensitive to their surroundings. In theory, an autonomous shuttle should be able to function along a wide path to manoeuvre around a small inanimate object. However, current technology requires a lane width twice as wide as a standard operating lane. When the shuttle detects an object in its path, it will stop and require an operator to manually take over and drive the vehicle to navigate the object. The shuttle is designed to show alert signals when it identifies moving animate and inanimate objects. At present, autonomous shuttles in the marketplace can identify objects as wide as one foot or larger. The shuttles also possess emergency stop mechanisms in case the sensors do not detect anything unusual. The onboard safety operator has an interface inside the vehicle to request the shuttle to continue its service. If an obstacle remains on the route, the safety operator can bypass the obstacle in manual mode.

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Adverse weather conditions

When extreme weather conditions are at play, the quality of sensor communication can degrade.

Leading manufacturers of autonomous shuttles confirm that existing technologies could not operate well in heavy rain, snow, or fog. Snow dunes and snow piles on the side of roads can disrupt the smooth functioning of shuttles by disrupting their perception of their environment. In these cases, Radio Detection and Ranging (RADAR) technology can be effective. RADAR systems can work in various weather conditions [2]. However, bad atmospheric conditions such as temperature inversion, moisture variations, water droplets and dust particles can affect functionality. Water droplets and dust particles can cause RADAR energy to diffuse through reflection, absorption, and scattering, which reduces the available energy striking the target in a smaller return echo. Higher frequency RADAR systems are more affected by adverse weather conditions than lower frequency radar systems.

With extreme weather conditions in Canada, autonomous shuttle operations are assumed to be adversely affected. However, a study funded by CUTRIC at the Université du Québec à Trois-Rivières (UQTR) has demonstrated that AV shuttles may be better at stopping during slippery road conditions compared to manual drivers due to their rapid response times and sensory receptors [13, 14]. This study seeks to assess “slipping” parameters for AVs by using a set of Internet of Things (IoT) sensory systems to detect and estimate slipping. Two systems are used: an artificial neural network (ANN) and a model base. Both methods have limitations, but the study recommends an anti-lock braking system (ABS) for improved control. The research team at UQTR continues experimenting with AV sensors to determine the best results and intends to test its predictive results on roads in actual weather-based trials.

Another team at the Université du Québec à Trois-Rivières researches in-vehicle smartphone-based position estimates for lane departures on urban roads using road-level GIS information [15]. The study shows that autonomous shuttles can stay on the right side of the road and avoid deviations. It also demonstrates the use of in-vehicle smartphones overcoming LIDAR and GPS limitations and mitigates the deviation of vehicles on urban roads while determining the permissible lateral distance for departure warnings. The addition of in-vehicle smartphone positioning data may improve the performance and safety of autonomous shuttles in transit operations in the future. A critical aspect of deploying design using in-vehicle smartphone data is receiving consent from riders/passengers to access and use their phone positioning data. If transit agencies can obtain this consent, the rich data provided by smartphones held by passengers onboard vehicles could be highly valuable in the future scaled deployment of transit-based smart autonomous shuttles.

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Vehicle-to-vehicle (V2V) communication standards

According to interview participants, no sufficient technology exists for AV shuttles to communicate with other vehicles. The vehicles communicate position and telemetry back to the Roadside Unit (RSU). The shuttles do not receive or communicate anything aside from the state of the traffic light. Regulatory agencies are in discussions to identify the best way forward for V2V and V2X communication. For instance, too much radio communication and interference can cause latency and communication problems. For this reason, a standard way to proceed would be to have CB 2x (citizen band radio 2 ways) or have this communication over a 5G cell service. Either option could allow for more connections and communications much faster without potential interference but would require the national or local build-out of a 5G network.

7.3. Overview

Autonomous shuttle technologies are evolving rapidly. Recent technological innovations allow autonomous shuttles to bypass obstacles on their own or request a supervising centre to allow for remote manoeuvring after checking the environment and safety conditions.

CUTRIC’s 2021 report on autonomous shuttles lists several published historical standards that apply to autonomous shuttle systems and safety assurances [2]. However, with current “sensory fusion” technology being insufficient, most AV shuttle deployments worldwide, including in Canada, require onboard drivers that can take over manual controls when needed, especially in busy environments where the shuttle can be set up with priority zone programming.

In recent years, manufacturers have worked with customers to better navigate environmental and weather conditions based on seasonal factors. According to their recommendations, shuttles should be recommissioned and redeployed at specific times of the year when seasonal changes hinder the shuttle's safe operations (e.g., due to snowbanks or springtime chairs and tables outside restaurants with patios). In these cases, shuttles must be reprogrammed to operate on a different route or a few feet from the original deployment lane.

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Given that the built environment is necessary to enable current AV shuttle technologies as transit solutions, it is critical to collaboratively communicate with city representatives, transportation agencies and manufacturers during the early stages of the project planning process. Manufacturers and operators interviewed for this report recommend building early-stage coordination (consortium-based) teams to model and develop schedules for the shuttles and to allocate resources for operations and customer communications on both the transit side and the manufacturer or operator side of the team equation. Another important aspect that has emerged in interviews completed for this report includes documentation processes for when the vehicle runs in autonomous mode versus manual mode. In Canada, an autonomous shuttle in the Town of Whitby, operating as part of Durham Region Transit (DRT), accidentally hit a tree. Though the shuttle was navigating in manual mode at the time, it impacted the project's continuity in the region and affected rider confidence.

To help transit agencies prepare in advance for such circumstances, Transport Canada has prepared a series of documents to support manufacturers and transit agencies, city representatives, and other stakeholders build a guidebook to comply with minimum safety standards. According to Transport Canada, most autonomous shuttles do not meet Canadian roadworthiness and safety standards.

Thus, autonomous shuttle leasing and deployment in Canada must be applied under the “exception” category [3]. The agency’s “dos and don’ts” list can be used during a deployment project's commissioning and training stage and must be incorporated in a training manual. The following are actions to be considered in such scenarios:

(i) If inclement weather situations arise, the recommendation is to switch the operation of the shuttle back into manual mode. This action should be made mandatory.

(ii) In an emergency like a shuttle accident, it is important to have documentation in place that confirms whether the vehicle is in manual or autonomous mode. Such documentation will shed light on the investigation and identify any lack of standardization in the operator and safety training standards that may have influenced or contributed to the causes of the accident.

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8. CENTRAL OPERATIONS SYSTEMS FOR MULTIPLE MANUFACTURERS

8.1. Standardization of DSRC units with other road-side infrastructure across multiple manufacturers

An important issue facing all transit systems seeking to deploy AV shuttles as mobility solutions is whether autonomous shuttles manufactured by different manufacturers can communicate with any type of standardized dedicated short-range communication (DSRC) unit [2].

Based on interviews conducted for this report, manufacturers confirm that autonomous shuttles manufactured by different manufacturers can work with standardized DSRC units. Since communication between shuttles and a DSRC unit occurs via radio transmission, shuttles manufactured by various manufacturers can communicate with each other using standardized onboard units (OBUs) and with standardized roadside units (RSUs) if they are configured and set up using the appropriate radio channels and frequencies. A study based on Dijkstra’s algorithm for AVs shows the system can handle large numbers of shuttles and stations at the same time[9]. The study took place in Vietnam where four stations and six vehicles were tested. Researchers developed an algorithm to manage a shuttle vehicle and several route stations. The initiative produced a process for solving communication issues and gaps within V2V and V2I connectivity as part of complex and dynamic systems, demonstrating it is possible. It demonstrated the interoperability of autonomous shuttle communications may be possible.

8.2. Standardization of latency period

An ongoing concern is the latency period in communications between vehicles, and other vehicles and surrounding infrastructure. Latency in communications is a crucial parameter to consider when shuttles manufactured by different manufacturers are on the road. Even though most manufacturer latency standards are approximately 100 milliseconds [16], no specific standards are established.

According to interview data, even when the latency is reduced to a minimum, the time delta between sending and receiving devices can still vary based on the equipment used. It can be subject to how the systems are configured beyond the units communicating via radio.

Latency can also arise at other intersections of communication. For example, it can appear during communications between the unit receiving the radio transmission, the computers doing the calculations and the decision-making portion of the operations.

2 DSRC is a technology used for V2V and V2I. This technology uses transponders that are onboard units (OBUs) and road side units (RSU) to communicate for traffic safety.

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In terms of time lag, a 1500 millisecond latency in communication is a factor for performance degradation. This type of information could not be used for accurate communication. Based on interview data collected for this report, manufacturers disagree about the optimal gathering time for information flow. Interviewees reported a range of views on optimal latency periods.

Some participants identified optimal latency periods for information flow from 100-120 milliseconds with anything above 150 milliseconds considered to be degraded data. Others identified an acceptable latency period of up to 250 milliseconds with anything above 250 milliseconds considered to be unacceptable. These differences are significant, demonstrating a need for latency standardization periods in Canadian deployments. Transit agencies today would be well advised to push for the lowest level of latency possible in devices and communications networks designed for AV shuttles.

Transport Canada has raised similar questions regarding safety and communication latency for remote driving. As there is limited evidence of best practices for remote driving from the operation room, Transport Canada has engaged international partners to understand emerging use cases and associated safety risks to update their guidelines. Potential safety risks may require improvements when managing or engaging in remote support (control room to shuttle), particularly when it involves a remote driver [3].

Concerns exist over instances of signal loss or when interruptions occur. For remote support to work effectively, a communication link must always be maintained with the autonomous shuttle. A loss or interruption in the communication connection, or a signal latency, can impact the reception time of information. These issues place the AV shuttle system at severe risk. The remote driver may be required to react immediately to avoid a collision. Transport Canada’s report confirms that exclusive remote driving is not yet feasible for testing in the Canadian landscape. Therefore, according to the Government of Canada, it is mandatory to always have a driver onboard due to latency concerns and signalling gaps with the existing infrastructure.

8.3. Operations and control room standardization

Within the matrix of concerns identified above, it is important to consider the standardization of operation centres, control rooms or command centres to be essential for e-LSAs to function safely.

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Operations and control rooms are part of the infrastructure through which agencies possess control over their transit fleet. They are the infrastructure through which operators of autonomous shuttles control and monitor AVs operating in real-world conditions. This involves AVs processing digital and visual information about shuttle movements. Supervisors can see and coordinate pre-programmed shuttles and monitor the shuttles' positions, assigned routes and destinations. Operations controllers can also monitor several vehicle parameters, including cabin temperature, battery charge, vehicle weight, and other data as vehicles operate throughout the day.

The control room's main objective is to ensure that autonomous shuttles operate as expected.

Guidelines

Currently, there are limited standards around control room operations. The technology needed to equip a control room properly is provided by various companies. Research shows that most manufacturers prefer to build their own operational guidelines to undertake pilot projects, including operating procedures for control rooms [17].

Interview data collected for this report confirm that no shuttle projects are operating in Canada today (circa 2022-2023) that are part of a transit system. Thus, no operational room integration plans are in place or being developed to overcome the lack of operations or control room standardization, a challenge in the industry today. This gap means there are no guidelines or pre-determined procedures and protocols to follow in emergencies, such as when an emergency vehicle must be prioritized on the road as in the case of an ambulance. Currently, if an emergency button is pressed manually in an AV shuttle, the shuttle will stop and alert the remote operators in the control room to take necessary actions. There are two emergency buttons in the vehicle, one for the passengers and one for the operator. In such instances, the lack of operations room procedures leaves the debate open regarding the proprietary design of the actions that occur thereafter.

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Remote support for autonomous shuttles

Another gap in operations and control room standardization relates to signal loss or interruption, signal latency, an automation bias, and/or task-induced fatigue [3]. In deployments going forward, it will be critical for transit agencies to create operational road maps that can guide combining technical considerations and resources with challenging remote technical support as part of an overall vision for project deployment success.

Pilot projects can be used to develop strategies and guide the implementation of highly complex deployments. For example, in 2020, an Estonian team conducted a pilot project in Ülemiste City, a technology park hosting 10,000 workers [18]. In this project, an autonomous shuttle operated across the park and connected offices with an airport and a shopping centre. Findings associated with this project provide valuable information about the legal requirements by which control centres had to abide when operating shuttles remotely to ensure on-road safety within city limits.

According to interview data, the lack of technology standardization combined with the lack of operational guidelines defining communications between vehicles and infrastructure other than traffic lights mean that deployment projects should be leveraged to develop standards for AV network communications. At a minimum, they should develop integrated standard operating procedures of an operational road map that could be copied by other communities, as well.

Infrastructure network signalling to benefit autonomous shuttle operations

Canadian research institutes play an important role in developing studies to improve traffic and transit communications. Current research conducted at the University of Calgary is helping to advance transit signal priorities (TSP) thereby supporting safe and effective AV shuttle deployments.

Transit signal prioritization is a general term used to outline a set of operational improvements that use technology to reduce the dwell time at traffic signals for transit vehicles by holding green lights longer or shortening red lights. TSP may be implemented at individual intersections, corridors or on entire street systems. Most traffic networks in cities across Canada do not generate traffic information in a readily available form. As a result, many mobility deployments – including transit bus deployments – are not fully responsive to traffic conditions.

With the expectation of increasing deployments of newly emerging autonomous shuttle technologies, transit vehicles are expected to share more information between individual transit vehicles (V2V) and road-side infrastructure (V2I).

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Transit vehicles and autonomous shuttles are also likely to increasingly communicate with intelligent devices – like smartphones -- carried by passengers onboard to identify the precise real-time position, speed, and occupancy of vehicles. Ongoing research in this space underscores the importance of a fair TSP solution as part of a balanced trade-off between optimizing the efficiency of the system travel time without compromising system equity [17].

Operational environments for AV shuttles

Autonomous shuttle technology faces multiple challenges when interacting with general traffic. The centre turning lane configuration is one such challenge. Regulatory frameworks (i.e., those developed by Transport Canada (TC) and the Ontario Ministry of Transportation (MTO)) provide guidelines and manuals for drivers [29], guiding AV shuttle maneuverability and the associated level of risk. Centre turning lanes can be configured as one-way or two-way left turn lanes. These lanes are designed and constructed to alleviate traffic congestion, improve traffic flow and allow for easier access to and from properties mid-block. Characteristics and driving procedures of complex two-way left turn lanes potentially impacting AV shuttle operations are as follows:

Vehicles coming from the opposite direction

Signals being limited to only on-the-road surfaces

Uses limited to accessing and exiting local roads and parking

Limited visual spectrum and speed detection from oncoming regular traffic

Raised centre lanes at locations

Non-merging lanes

Configurations for turning in heavy traffic

U-turn permissions

High volume traffic locations with challenging lane configurations such as centre turning lanes might result in operational constraints for smart vehicles. Like other pilot projects, the autonomous shuttles in Ülemiste City show that the operational risk is higher within mixed traffic flow and complex vehicle movements. For example, autonomous shuttles in Ülemiste City were deployed with no dedicated or segregated lanes and were used in mixed traffic. The traffic speed on the route, which included left and right turns, was up to 30 km/h faster than the speed recognized by manufacturers for safe perations, as identified within this report. The project faced an incident when a personal vehicle ignored a yield sign and crashed into the shuttle [20].

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Uncontrolled human behaviour raises risks for mixed and/or congested traffic, making centre turning lanes unsuitable for safe operations. Mitigation measures such as adequate signalling and good pavement markings can be implemented to lower the level of risk. Driving autonomous shuttles in manual mode under specific conditions might also reduce risks.

Preliminary assessments of centre turning lanes for autonomous shuttle operations offer the following considerations:

Risks are higher for autonomous shuttle manoeuvrability at locations with centre turning lanes.

Autonomous shuttles should have priority at stop lights and traffic lights.

Sensors can detect the speed of external vehicles as “moving objects.” The autonomous shuttle will stop when another vehicle stops in front of it.

Shuttles can be pre-programmed to operate under conditions in which the operator can take control.

Autonomous shuttles have a high level of sensitivity to differences in surface levels and materials.

Autonomous shuttles can operate in mixed traffic conditions with limited operational design domain (ODD) elements integrated, but the speed of autonomous shuttles deployed in initial deployments in Canada should operate at no more than 20 km/h maximum to ensure safety.

Autonomous shuttles need considerable space to make a U-turn which might end inobstructing the flow of oncoming traffic (with two-way turning lanes) and abruptly generate opposite traffic speed reductions in the best of situations.

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In addition, the following considerations should be considered when an autonomous shuttle is applied in a centre turning lane:

Centre lanes should be considered a high-risk operational environment.

In mixed traffic, the priority right-of-way to turn left should be given to the shuttle.

In centre turning lanes, it may be challenging to detect oncoming vehicles. In these cases,

DSRC units will play a critical role in controlling traffic flow.

Sensors make the vehicle sensitive. Raising centre turning lanes can cause false alarms.

Centre turning lanes are ideal for stop and fast-turn driving. Autonomous shuttles have operational limits in speed. Maneuverability in autonomous mode makes fast turns difficult. Low-speed shuttles may not be able to move fast enough when approaching the centre turning lane without putting the shuttle itself or other vehicles at risk of collision.

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9. RIDERSHIP GROWTH STRATEGY AND PLANNING

Standardizing central operating systems and control room procedures achieves passenger and road safety and communication efficiency. These goals, in turn, help to achieve increased transit ridership over time. Research shows that riders are less likely to use public transportation if there is no bus or train station within a 20-minute walk from either a rider’s home or a major destination.

Autonomous shuttles can help bridge these gaps in the transit system by offering first-kilometre/last-kilometre routes that 40-foot and 60-foot buses cannot serve due to prohibitive costs and low ridership. Urban and suburban communities are flexible in using short-route services to connect with transit hubs or popular destinations. Autonomous shuttles also offer economic, energy and operational efficiencies in such situations. In its 2021 report on autonomous shuttles, CUTRIC completed a preliminary ridership impact analysis using demographic data for select communities where AV shuttles are being proposed [2]. This analysis indicates a high variation between the number of cars displaced and the proposed increase in ridership due to AV shuttle deployments. However, the analysis lacks accuracy due to gaps created by incomplete data regarding community profiles, household composition, car ownership and alternative modes of mobility in communities.

Nonetheless, it provides a basis for calculating an estimated number of shuttles required in given transit scenarios for the first kilometre/last kilometre solutions [1]. These calculations are helpful when transit agencies are attempting to predict ridership growth associated with autonomous shuttle technologies deployed in the future.

9.1. Passenger safety

Passenger safety is a top priority for all transit systems. Within the context of AV shuttles,manufacturers and transit agencies must plan for public safety and rider accessibility when onboarding, offboarding and interacting with the shuttles.

The first step in achieving passenger safety is comprehending user and rider perceptions of AV shuttle deployments before and during operation. Passenger safety is predicated on riders’ perceptions of autonomous shuttles as reliable, safe and trusted vehicles as well as being innovative and effective means of commuting.

In a 2021 Austrian study, a team of researchers explored several factors contributing to the perceived safety of passengers during autonomous shuttle rides. The study concludes that driving technicalities become less important as passengers gain experience with autonomous systems [19]. Riders’ access to an onboard concierge or “driver” who can take over manual control also adds to the perceived safety of the system and positively impacts ridership volumes. The design and set-up of autonomous shuttles can improve the perception of safety and could be used as an indirect means of increasing ridership overall.

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9.2. Increase in accessibility

With autonomous shuttles in operation, people with disabilities, the elderly, and people unable to drive or walk to transit stations will find a new way of accessing the transit system. The convenience and comfort of using autonomous shuttles are also expected to impact the number of trips generated.

These shuttles may also reduce the need to drive and park on streets because of a lack of parking spots at congested transit lots during peak hours. Autonomous shuttles may decrease transit travel time overall by circumnavigating traffic congestion if deployed on dedicated laneways. Autonomous shuttles may also reduce travel costs by reducing the need for private vehicles for some riders given that car ownership is typically costlier over the ownership life cycle compared to transit usage. With reductions in mobility barriers and costs, autonomous shuttle deployments are expected to increase transit ridership overall [1].

Additionally, manufacturers are designing and planning to deploy shuttles for people with special needs. Currently, some autonomous shuttles feature a ramp for wheelchair access and a specific button to request the ramp from both inside and outside the vehicle.

Manufacturing guidelines for autonomous shuttles published by Transport Canada list user protection, accessibility, privacy and passenger monitoring as critical considerations when planning for riders on board [3].

10. STANDARDIZATION OF CYBER SECURITY PROTOCOLS

Despite the potential benefits of autonomous shuttles, they pose significant security and privacy risks to users and transit agencies if not correctly designed from a privacy and cyber-security perspective.

There is an intrinsic connection between cyber security and safety. With vehicle automation technology and widespread vehicle connectivity, security risks are high. Autonomous shuttles for public transportation applications face challenges due to a lack of cyber security regulation. The sector has raised valid concerns in Canada and elsewhere. CUTRIC’s 2021 report on autonomous vehicles [2] provides a detailed analysis of the kinds of cyber-attacks on AVs and connected systems and the standards that can be used to avoid them. Similarly, Transport Canada highlights guidelines and guidebooks considered to be security standards for the Canadian landscape [3].

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In Europe, a study conducted by Estonian researchers showcases the ways in which autonomous vehicle and shuttle cybersecurity interacts and relates to safety operations. While evaluating the Today, different protocols exist for cyber security within the sector. Concerning autonomous shuttle technology, the ISO 21434 + UN-ECE regulation number 155 is used as a guideline and the main guidebook. For the safety of the vehicle specifically, the ISO 26262-2018 safety guide [22] is applicable in managing the consequences of failures. The “SOTIF" ISO 21448 is used for the Accelerate Development to deal with the performance limitation of perception and maladaptive behaviour of shuttles. As international manufacturers of autonomous shuttles will be deploying vehicles that will be operated in Canada, they will be required to follow UN-ECE No. 155 [23], the cyber security management system from the United Nations that must be approved before introducing a new vehicle to the country.

DuckieBot developed at the Massachusetts Institute of Technology (MIT) for cyber security vulnerability testing, researchers found that the results could be applied to autonomous shuttles operating in Estonia. [21]. The MIT DuckieBot test bed was used to replicate the complexity of interactions in relevant systems of the autonomous shuttles. The evaluation demonstrates that cyber-physical test beds and range can support agile, repeatable cyber security testing that is low-cost and conducted in a controlled and safe environment.

11. CHARGING INFRASTRUCTURE STANDARDIZATION

With the increasing popularity of autonomous shuttles, transit agencies will require charging station installations. Depending on the number of autonomous electric shuttles deployed in tandem with electric buses, local electricity grids may be affected. Experts are developing different charging strategies, standards, and grid-integration methods to minimize the adverse effects of EV charging and strengthen the benefits of EV grid integration [24].

Currently, no standards govern charging design or charging infrastructure for low-power AV shuttles.

According to data collected, charging infrastructure is usually shared with and optimized for light-duty electric vehicles (EVs) operated by the same fleet operator. In these cases, shuttles may be parked in an indoor garage with charging stations installed to support shuttles and light-duty car fleets. The optimal installation setup and usage of these chargers in parking lots or at depots needs to be modelled and assessed from a fleet-wide electrification perspective with transit agencies seeking to deploy electrified AV shuttles in tandem with other light-duty electrified fleet vehicles.

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12. PREDICTIVE PERFORMANCE MODELLING OF ELECTRIC

LOW-SPEED AUTONOMOUS SHUTTLES

Autonomous shuttles are unlikely to demand power and energy from the grid at the levels required for electric buses. However, they do constitute a new load within the energy system. Transit agencies seeking to deploy autonomous shuttles as transit solutions should understand the required energy load.

The following section summarizes CUTRIC’s technical feasibility study of autonomous shuttles deployed within a test Canadian transit agency. In this technical portion, CUTRIC provides an anonymized sample of AV shuttle modelling. This modelling work presents calculations of electric low-speed autonomous shuttle (e-LSAs) performance along set routes and pre-determined pathways. CUTRIC’s RoutΣ.i™ 2.0 modelling tool is a set of predictive tools that use detailed input data comprising route geometry, route topography, traffic impediments, speed profiles and vehicle specifications to generate energy-consumption predictions for each selected route on a per vehicle basis. A spectrum of possibilities regarding the service's utilization level is considered.

Three separate duty cycles are considered to mirror low, average, and excessive energy use, namely light-duty, medium-duty, and heavy-duty scenarios. The factors distinguishing the three duty cycles are the number of passengers on board, the auxiliary load (heating or cooling) and the number of stops along the route. For the light-duty cycle, the vehicle has a single passenger on board, operates on minimum auxiliary load, and stops at all mandatory stops but only half of the non-mandatory stops.

The medium-duty cycle assumes half of the passenger capacity, an intermediary level auxiliary load utilization and follows the stop pattern of the light-duty cycle. The heavy-duty cycle considers the maximum passenger and auxiliary load capacity and simulates the vehicle stopping at all stops along the route.

12.1. Modelling inputs and assumptions

In this assessment, CUTRIC’s research team simulates specific vehicle models based on the following assumptions:

A complete round trip of the vehicle.

Access to a 100+ kW automated on-route charger after the completion of each round trip.

A minimum allowable state of charge (SOC) limits to prevent premature battery degradation set at 20 per cent, i.e., the battery SOC is not permitted to fall below the 20 per cent mark in the simulations.

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12.2. Modelling methodology

In its feasibility work, CUTRIC integrated the following parameters when assessing the energy performance of autonomous shuttles:

Speed and topological profile of the selected routes

Energy efficiency and energy consumption of the autonomous shuttle along each identified route.

Maximum possible trips with depot-only charging episodes

On-route charging times between two e-LSA trips to allow indefinite service (not limited by the battery capacity)

12.3. Speed and topography profile generation

CUTRIC’s researchers digitize the shuttle routes and associated traffic impediments using ArcGIS software to identify the route's topography using a digital elevation model (DEM) to obtain elevation data and traffic impediments along the selected route.

The maximum speed considered for shuttle operations is 20 km/h. The assumption of a 20 km/h speed restriction is based on the performance and speed of automated sensory system technologies (i.e., LiDAR, RADAR and ultrasonic sensors). CUTRIC modelled six potential routes for the transit agency in this study.

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12.4. Results of feasibility study

The energy efficiency figures (kWh/km) documented below show average vehicle performance outcomes considering differing route profiles, passenger loads and auxiliary loads. Table 14 shows the energy efficiency of the autonomous shuttle across the three duty cycles and all routes identified by the transit agency.

Routes Light Medium Heavy Route 1 0.29 0.50 1.09 Route 2 0.28 0.48 1.03 Route 3 0.34 0.57 1.03 Route 4 0.28 0.47 0.99 0.46 0.91 Route 6 0.33 0.53 0.97 Route 5 0.27
CUTRIC-CRITUC KNOWLEDGE SERIES IN LOW CARBON SMART MOBILITY INNOVATION. VOLUME 4, NO. 1 (2023). Table 14. Energy efficiency (kWh/km) of the vehicle Figure 3 is a graphical representation of the energy efficiencies provided in kWh/km for the three duty cycles and all routes.
40 0.0 0.2 0.4 0.6 0.8 1.0 1.2 Route 1 Route 2 Route 3 Route 4 Route 5 Route 6 Energy Efficiencies (kWh/km) Energy Efficiencies (kWh/km) Light Medium Heavy
Figure 3. Energy efficiencies

Table 15 shows the energy the autonomous shuttle consumes for three duty cycles and all routes. The round-trip energy consumption figures shown are slightly higher than the energy consumed by the vehicles because the energy efficiency of chargers is also considered in this study. For routes 1, 2 and 5, the distance travelled between charging episodes is twice the route length, compared to other routes, because the charger is only assumed to be available at one end of the route resulting in higher energy consumption. Alternatively, routes 3, 4 and 6 are closed loops. The distance travelled between Charging episodes is equal to the route length.

CUTRIC-CRITUC KNOWLEDGE SERIES IN LOW CARBON SMART MOBILITY INNOVATION. VOLUME

Routes Light Medium Heavy Route 1 1.69 2.88 6.27 Route 2 2.19 3.74 7.98 Route 3 0.91 1.55 2.81 Route 4 0.84 1.41 2.99 Route 5 1.61 2.73 5.45 Route 6 1.14 1.86 3.40
4, NO. 1 (2023). Table 15 graphically depicts the autonomous shuttle energy consumption results. Table 15. Round trip energy consumption (kWh)
41 0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.2 1.2 1.2 Route 1 Route 2 Route 3 Route 4 Route 5 Route 6 Energy Efficiencies (kWh/km) Energy Efficiencies (kWh/km) Light Medium Heavy
Figure 4. Energy consumption by the charger

Table 16 compares the maximum number of round trips that could be completed by an autonomous shuttle for all routes and the three duty cycles without on-route charging.

Table 17 shows the total operational time an autonomous shuttle can serve without the inclusion of on-route charging. The number of trips would be indefinite (not limited by battery capacity) if the shuttle could have access to on-route charging episodes rated at 100+kW for a few minutes after each round trip.

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Routes Light Medium Heavy Route 1 19 11 5 Route 2 15 9 4 Route 3 35 21 12 Route 4 39 23 11 Route 5 20 12 6 Route 6 28 17 10 Routes Light Medium Heavy Route 1 217 127 87 Route 2 216 127 87 Route 3 424 250 166 Route 4 415 246 177 Route 5 211 124 86 Route 6 369 227 163
Table 16. Maximum number of trips with only-depot charging
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Table 17. Total operational time (in minutes)

Table 18 shows the minimum on-route charging time required between two autonomous shuttle round trips for continuous vehicle operation. The calculated values also include an average time needed for docking and undocking the autonomous shuttle chargers.

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(2023). Figure 7. Total operational time (minutes)
43 0 50 100 150 200 250 300 350 400 450 Route 1 Route 2 Route 3 Route 4 Route 5 Route 6 Energy Efficiencies (kWh/km) Energy Efficiencies (kWh/km) Light Medium Heavy Light-Duty Cycle Medium-Duty Cycle Heavy-Duty Cycle 2 3 5
Table 18: Minimum on-route charging time between two autonomous shuttles round trips for continuous vehicle operation (minutes)

13. CHALLENGES

Research for this report has identified the following challenges regarding autonomous vehicle deployments as transit solutions.

No standardized V2V communication exists to support vehicle-to-vehicle communications. A growing body of research shows interest in the effectiveness of V2I communication in fifth-generation (5G) networks supporting the co-existence of multi-tier heterogeneous wireless networks with diverse radio access technologies (RATs).

Successful V2V communications that support autonomous vehicles in mixed traffic would require a 5G communication system.

Stakeholders continue to face challenges concerning transit agencies’ ability to design, test, procure, and deploy mass autonomous shuttle systems. Transit agencies face challenges in launching AV shuttles given the expenses associated with smart infrastructure and charging systems required to launch smart shuttles. Some of these hurdles may include, but are not limited to, the following considerations:

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A lack of sufficient funding for public transit agencies given the high costs required for smart shuttle deployments compared to conventional transit vehicles, on a per rider basis;

Ambiguous liabilities in contracts signed between shuttle manufacturers, operators and transit agencies when it relates to the vehicle's driving functions, and transit operators that are accountable for the operations and performance of the vehicle.

A lack of standardization for operator and safety training. Although manufacturers provide hands-on training to operators and transit agencies, there is no certification program has been developed for North American agencies overall.

A lack of standard industry-accepted criteria for designing and selecting optimal pilot and demonstration test routes.

Overly optimistic expectations by public policy makers and transit planners about the readiness of the technology, resulting in disappointing perceptions of early demonstration project outcomes.

14. THE WAY FORWARD

As global autonomous shuttle landscape continues to evolve, pilot projects are being implemented in various parts of the world. Since 2018, Norway has implemented 13 municipal autonomous shuttle trials, some of which have occurred under normal traffic conditions on public roads. Other countries have deployed shuttles in low-speed environments, typically in places without public transportation, including a sea promenade, a pedestrian zone and a residential area.

Pilot projects have explored the operability of shuttles in specific “T” intersections and their interactions with vulnerable road-users, V2X communication with traffic signals, performance in winter conditions, and the suitability of AVs for an on-demand service.

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15. RECOMMENDATIONS

Based on the findings presented in this report, CUTRIC recommends the following planning and implementation steps be taken in the design and deployment of autonomous shuttle mobility projects in Canada.

Communication Protocols

1.

Autonomous shuttles for transit applications should be deployed using current and standardized dedicated short-range communication (DSRC) technology. In the Canadian landscape, 5G technology is not developed to the extent required, hence it is deemed unavailable for this purpose. DSRC technology can adequately support autonomous shuttle speed, range, and movements. As 5G technology becomes more prominent, C-V2X will be used to compliment DSRC technology, allowing for more sophistication to be incorporated into future autonomous shuttle iterations.

2.

Modifications to road infrastructure elements along the selected route should be implemented for the safe navigation of the vehicle. Redundant sensor inputs create more reliable routes and avoid negative safety impacts on road users. Site visits can help to identify missing and/or necessary infrastructure elements for test routes. Roadside infrastructure, dedicated lane markings and signals along routes are factors to identify and address in the planning phase. Necessary civil works, such as sidewalk, station stop(s) and lane marking modifications may be required for safe autonomous shuttle operations and deployment.

3. 4.

Testing of sensor sensitivities should form part of the pilot demonstration assessment. Sensor sensitivity should be assessed as part of the pilot demonstration to support the ongoing improvement and validation of autonomous driving detection functions.

Communication protocols between city representatives, transportation agencies and manufacturers should be planned. Prioritizing communication channels between manufacturers and operators and focusing on change management protocols will mitigate events that put the project at risk of closure.

5.

Operational guidelines should govern the use of manual or autonomous modes. A document which clarifies situations in which manual or autonomous modes should be utilized should be a mandatory component in all training courses for operators, and for Emergency services personnel.

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Original Equipment Manufacturers

6. 7. 8. 9. 10. 11.

Third-party certification should form part of the operations plan. Operator certifications should be required for the party responsible for driving operations.

Autonomous shuttle operations centres should be standardized to the greatest extent possible. Control rooms or command centres should aim to unify their design and reduce output variances and inconsistencies in their designs across transit agencies to the extent possible. A standard operations plan should simplify every procedure within the room and should support safe and efficient remote communications.

Project-specific risk assessments should be based on local strategies. Each Canadian region presents different geographical and topographical characteristics, variable climates, municipal transportation policies, and transit priorities. A localized autonomous shuttle deployment roadmap and risk matrix can anticipate actions that autonomous shuttles might face within specific environments.

Collaboration between manufacturers and transit agencies should be fostered to develop cyber security protocols and standards. In the absence of cyber security protocols for autonomous shuttle technologies, manufacturers and transit agencies should work together to initiate pathways towards the development of common project-based cyber security protocols and standards that could be incorporated into general procurement specifications transit agencies.

Project deployments should allow two-way communication functionality between the control centre and passengers. Two-way communications functionality between on-board passengers and control rooms should increase the confidence riders have in autonomous shuttle technology overall. Devices inside vehicles, such as cameras and microphones, should be deployed to send alerts to the control room supervisor when necessary.

A communications plan for messaging should be included in overall autonomous shuttle deployment plans. Whether transit agencies are introducing this new transportation technology to increase ridership, make cities more attractive to newcomers or connect communities better, public information about project intentions and goals will impact ridership over the long-term. Transit agencies should publicize how the pilot will positively impact the commute for riders or transit ridership overall. Generating awareness about autonomous shuttle technology plays a key role within communities when transit is a priority.

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Cyber security Protocols Ridership Growth Strategy

12. (Charging)

Infrastructure

Integration of charging systems for autonomous shuttles with other light-duty electrified vehicles (EVs) in public fleets should be leveraged to advance the optimization of electrified public fleets overall. The location of charging systems for autonomous shuttles will play a key role in municipal and transit agency energy consumption optimization. Infrastructure sharing and cross-utilization between autonomous shuttles and light-duty EVs will be an important solution for the future deployment of these systems at the lowest possible operational cost.

16. CONCLUSIONS

CUTRIC continues to engage manufacturers, transit agencies, municipalities, academic institutions, and other stakeholders that have already conducted or supported pilot projects as part of its ongoing commercialization efforts with Canadian transit systems.

In Canada, the standardization of autonomous shuttle technology is in its early stages. Initiatives from other parts of the world can help to guide the design and development of future autonomous pilot projects. Evidently, ongoing work in autonomous vehicle standardization, cybersecurity, interoperability and charging systems alignment is needed to bring to reality the concept of autonomous shuttles as sustainable and effective first kilometre-last kilometre solutions.

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