An Expert Ensemble for Detecting Anomalous Scenes, Interactions, and Behaviors in Autonomous Driving

Autonomous driving is at a critical stage in revolutionizing transportation systems and reshaping societal norms. More than 1,400 self-driving cars, trucks, and other vehicles are currently in operation or testing in the U.S. (Etherington 2019), and 4.5 million autonomous vehicles are expected to run on U.S. roads by 2030 (Meyer 2023). While autonomous driving is promising in improving traffic efficiency and personal mobility, safety is a prerequisite of all possible achievements and is becoming the first priority in practice (Du et al. 2020). In October 2023, Cruise, one of the leading autonomous driving companies, was ordered by California to stop operations of driverless cars in the state after one of Cruise's cars struck a pedestrian in San Francisco (Kerr 2023). The rare incident involved a woman who was first hit by a human driver and then thrown onto the road in front of a Cruise vehicle. The Cruise vehicle then rolled over the pedestrian and finally stopped on top of her, causing serious injuries. Such an accident reflects one of the greatest challenges in autonomous driving: the safety of an autonomous car is largely determined by the ability to detect and react to rare scenarios rather than common normal situations, which have been well considered during development. Although rare in a long-tailed distribution, unusual driving scenarios do happen and can have large impact on driving safety. To mitigate the impact of abnormal ego behaviors when outside the design domains, a detection system for anomalous driving scenarios is necessary, the output of which can be potentially used as a high-level decision for motion planning.