Autonomous Vehicle Performance Supported by AI, ML For Stable Operation
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Autonomous Vehicle Performance Supported by AI, ML For Stable Operation

Autonomous Vehicle Performance Artificial Intelligence AI Machine Learning ML Operation New-Age Technologies
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New-age technologies, combined with appropriate simulation, lead to the development of more robust autonomous driving solutions

The performance levels in an automated driving vehicle, from Level 1-5 need to be as accurate as possible, so as to enable smooth operation of the vehicle in the real world. Even the amount of disengagement of the vehicle from its autonomous mode to manual intervention needs to be highly-linear in nature. A number of these requirements are being met by new-age technologies of Artificial Intelligence (AI), Machine Learning (ML) and Deep Learning.

Webinar on Performance Engineering for Autonomous Vehicles

In auto mode, the vehicle needs to monitor its surroundings, and then take in all the information received through the various sensors and finally be able to take necessary actions for various scenarios. The algorithms built into these autonomous vehicles need to work accurately, learn new attributes of the environment, and finally react to different scenarios differently. Therefore, the machine and systems within it need to keep analysing the data coming in and take appropriate decisions so as to improve the overall functioning of the vehicle autonomy.

In the case of such vehicle, disengagements occur when the self-driving system is deactivated and control is handed back to a human driver due to various reasons or incidents. These could include system failure, traffic, weather or road situations that require human intervention to continue with the vehicle’s movement. It must be noted that during the incidence of disengagement, the vehicle’s automated systems need to alert the driver ahead of time of the requirement of manual intervention.

Video on Developing Autonomous Vehicles at Scale

These alerts can be provided only when the underlying systems can detect the need for manual intervention or to take corrective actions automatically. Such AI can only be achieved by the use of deep understanding of the sensor data and instant analysis of the same to enable a suitable reactive action from the automated system. There are simulation technologies that address the development of such systems to provide alerts and take corrective action, and they are being tested across the globe to fine tune automated driving even more and make it as robust as possible. Autonomous vehicle companies are also making use of simulation to collect data on the level of accuracy with which such alerts are thrown in their vehicles, to thereby further enhance their systems with technologies including ML and Deep Learning.