Key Technologies Impacting Autonomous Driving

Key Technologies Impacting Autonomous Driving

Introduction • The term ‘Autonomous Driving’ has been in the buzz off late • It is forecasted that there might be significant disruptions taking place in the automotive industry • Emerging automotive providers in India and abroad have partnered with car manufacturers to penetrate into this growing market • This partnership has been successful, as we are witnessing self-driving technology

Technologies for Autonomous Vehicles • Various advanced technologies are contributing towards the development of Advanced Autonomous Driving & Self-Driving cars • Some of the notable technologies fuelling the growth of autonomous cars include1. Sensors and Vision Processing 2. Artificial Intelligence and Deep Learning 3. Application Lifecycle Management

Sensors • Automotive companies are now combining sensor technologies for enhancing the situational awareness of autonomous cars\ • Self driving cars are using refined variants of infrared sensors • LiDAR (Light Detection and Ranging Technology) sensors are used for calculating the distance between a vehicle & its surroundings • With rotating LiDAR sensors, it is becoming possible to have a 3-D view of the vehicle for detecting any obstacles

Sensor Fusion • Sensor fusion is an emerging software technology that aims to synthesize the data collected from various sensors for providing accurate position and orientation data of the vehicle • It is a critical component of any autonomous vehicle which operates on parallel data inputs from different sensor types • All these inputs are combined to give a clear understanding of vehicle’s surroundings

Vision Processing

• LiDAR-based sensors are costly, as a result some providers are opting camera-based tools • These camera tools provide accurate information of the vehicle’s surroundings

Artificial Intelligence • The future vehicles ae going hold vast amount of information related to their ecosystem • A small challenge persists regarding complete automation • The vehicle must interpret the incoming data & make decisions based on the information • AI will be a major component in the autonomous technology

Deep Learning • Deep learning uses artificial neural networks for allowing a system to learn and perform its own decisions • Multiple sets of inputs and decisions in a driving scenario enables the use of such learning algorithms • Self-driving cars will offer better services with image recognition, processing, and decision-making using deep learning • The autonomous vehicles will analyze, understand and react to the context in a better manner when driven in simulated scenarios

Application Lifecycle Management (ALM) Integration • Development of self-driving and autonomous vehicles is a complex task, as it involves deploying various advanced technologies • Integration of major components can become complicated; automotive technology providers like ARAI closely monitor the current and future regulations related to self-driving • ALM play a major role in managing complex development procedures of autonomous cars through compliances and standards • Such platforms offer support for multiple engineering processes and assure transparency with traceability required for safer delivery of self-driving cars

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