A-Eye: Driving with the Eyes of AI for Corner Case Generation
This work addresses the challenge of rare and dangerous situations in automated driving, which is crucial for safety but incremental in approach.
The authors tackled the problem of generating rare and critical driving scenarios (corner cases) to improve AI perception for automated vehicles, and demonstrated that enriching training data with these cases enhances pedestrian detection in safety-relevant episodes.
The overall goal of this work is to enrich training data for automated driving with so called corner cases. In road traffic, corner cases are critical, rare and unusual situations that challenge the perception by AI algorithms. For this purpose, we present the design of a test rig to generate synthetic corner cases using a human-in-the-loop approach. For the test rig, a real-time semantic segmentation network is trained and integrated into the driving simulation software CARLA in such a way that a human can drive on the network's prediction. In addition, a second person gets to see the same scene from the original CARLA output and is supposed to intervene with the help of a second control unit as soon as the semantic driver shows dangerous driving behavior. Interventions potentially indicate poor recognition of a critical scene by the segmentation network and then represents a corner case. In our experiments, we show that targeted enrichment of training data with corner cases leads to improvements in pedestrian detection in safety relevant episodes in road traffic.