Percentile control improves corner case generation for autonomous vehicles
How corner is a corner case? Percentile control for highway scenario generation
Artificial Intelligence
Summary
Testing self-driving cars requires creating tricky traffic situations called corner cases. The authors found a way to measure how extreme these tricky scenarios are compared to normal driving futures. They built a system that can generate scenarios with a targeted level of risk, making it easier to test safety under varying dangers. This helps developers make sure their autonomous cars can handle rare but important situations.
What this means in practice
- •For autonomous vehicle developers: Generate driving scenarios with precise risk levels to better test and improve vehicle safety software before deployment.
- •For automotive simulation engineers: Create multi-agent highway traffic futures with controllable adversity for validating AV system responses under diverse hazardous conditions.
Authors
Jiaxi Liu, Hang Zhou, Hangyu Li, Yifan Wang, Keke Long, Chengyuan Ma, Bin Ran, Xiaopeng Li
Abstract
Generating corner-case scenarios with appropriate adversity in a simulation environment is critical for testing an autonomous vehicle (AV) software stack's safety performance before deployment. Existing autonomous-driving scenario generators can enforce specific behavior, adversity, or feasibility conditions, but they provide limited control over how extreme a generated scenario is relative to plausible futures in the same traffic context. This study represents the adversity of a generated scenario as its percentile in the conditional distribution of future risk given the observed history. This view supports calibrated answers to two questions: how "corner" a generated corner-case scenario is and how its "cornerness" can be fine-tuned. To this end, we formulate history-conditioned risk-percentile requests and learn a reference risk distribution that maps each requested percentile to a physical risk target. We then use a percentile-conditioned joint diffusion model with sampling-time risk guidance to generate multi-agent futures, together with a reference-based criterion for evaluating percentile realization. Experiments use the minimum post-encroachment time (PET) between the ego and its surrounding vehicles as the risk surrogate on highD. On the primary evaluation set, our method realizes 1,422 of 1,440 requests within a 0.05 percentile tolerance (98.75%), with mean percentile error 0.00673 and PET-target error 0.00991 seconds. The resulting interface connects context-relative risk specification, physical realization, and evaluation through a common risk scale. Project website and videos of generated scenarios are available at https://hhj233.github.io/CornerPercentile/.