Uncertainty about self-driving cars / autonomous vehicles (AVs) is at an all-time high. Michigan Engineering researchers aim to change that. Training AVs to recognize safety hazards is a complicated task. Autonomous vehicles can typically handle 99.99% of safety use cases. Once you get to the 0.001%, AVs may not be able to handle these case uses because they haven’t seen the scenarios yet. This 0.001% is the curse of rarity. Training autonomous vehicle software is especially time-consuming and expensive, because individual safety use cases come up so rarely in normal driving conditions. https://news.engin.umich.edu/2023/03/...

To fix this problem, a team of researchers used artificial intelligence to train virtual vehicles that can challenge autonomous vehicles in a virtual or augmented reality testing environment. The virtual cars were only fed safety-critical training data, making them better equipped to challenge AVs with more of those rare events in a shorter amount of time. In an era of uncertainty towards AVs, this solution can save auto manufacturers a prohibitive amount of time and money to ensure their systems are safe.

This research was led by Professor Henry Liu, Director of Center for Connected and Automated Transportation (CCAT), Director of Mcity
https://cee.engin.umich.edu/people/li...

Paper:
"Dense reinforcement learning for safety validation of autonomous vehicles"
Journal: Nature
Date: March 22, 2023
https://www.nature.com/articles/s4158...
DOI: 10.1038/s41586-023-05732-2

Learn more:
- https://news.engin.umich.edu/2023/03/...
- https://ccat.umtri.umich.edu/
- https://traffic.engin.umich.edu/

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