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.

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.

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.

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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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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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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:

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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