  As self-driving systems become better, simulating scenarios where the
autonomy stack may fail becomes more important. Traditionally, those scenarios
are generated for a few scenes with respect to the planning module that takes
ground-truth actor states as input. This does not scale and cannot identify all
possible autonomy failures, such as perception failures due to occlusion. In
this paper, we propose AdvSim, an adversarial framework to generate
safety-critical scenarios for any LiDAR-based autonomy system. Given an initial
traffic scenario, AdvSim modifies the actors' trajectories in a physically
plausible manner and updates the LiDAR sensor data to match the perturbed
world. Importantly, by simulating directly from sensor data, we obtain
adversarial scenarios that are safety-critical for the full autonomy stack. Our
experiments show that our approach is general and can identify thousands of
semantically meaningful safety-critical scenarios for a wide range of modern
self-driving systems. Furthermore, we show that the robustness and safety of
these systems can be further improved by training them with scenarios generated
by AdvSim.

Robotics;Artificial Intelligence;Computer Vision and Pattern Recognition;Machine Learning