  Self-driving vehicles (SDVs) must be rigorously tested on a wide range of
scenarios to ensure safe deployment. The industry typically relies on
closed-loop simulation to evaluate how the SDV interacts on a corpus of
synthetic and real scenarios and verify it performs properly. However, they
primarily only test the system's motion planning module, and only consider
behavior variations. It is key to evaluate the full autonomy system in
closed-loop, and to understand how variations in sensor data based on scene
appearance, such as the shape of actors, affect system performance. In this
paper, we propose a framework, Adv3D, that takes real world scenarios and
performs closed-loop sensor simulation to evaluate autonomy performance, and
finds vehicle shapes that make the scenario more challenging, resulting in
autonomy failures and uncomfortable SDV maneuvers. Unlike prior works that add
contrived adversarial shapes to vehicle roof-tops or roadside to harm
perception only, we optimize a low-dimensional shape representation to modify
the vehicle shape itself in a realistic manner to degrade autonomy performance
(e.g., perception, prediction, and motion planning). Moreover, we find that the
shape variations found with Adv3D optimized in closed-loop are much more
effective than those in open-loop, demonstrating the importance of finding
scene appearance variations that affect autonomy in the interactive setting.
