The prevailing way to test a self-driving vehicle (SDV) in simulation involves non-reactive
open-loop replay of real world scenarios. However, in order to safely deploy SDVs to the …

Simulation has the potential to massively scale evaluation of self-driving systems, enabling
rapid development as well as safe deployment. Bridging the gap between simulation and …

Large-scale driving datasets such as Waymo Open Dataset and nuScenes substantially
accelerate autonomous driving research, especially for perception tasks such as 3D …

Rigorously testing autonomy systems is essential for making safe self-driving vehicles (SDV)
a reality. It requires one to generate safety critical scenarios beyond what can be collected …

Benchmarking vision-based driving policies is challenging. On one hand, open-loop
evaluation with real data is easy, but these results do not reflect closed-loop performance …

Interactive traffic simulation is crucial to autonomous driving systems by enabling testing for
planners in a more scalable and safe way compared to real-world road testing. Existing …

Existing research on autonomous driving primarily focuses on urban driving, which is
insufficient for characterising the complex driving behaviour underlying high-speed racing …

Evaluating and improving planning for autonomous vehicles requires scalable generation of
long-tail traffic scenarios. To be useful, these scenarios must be realistic and challenging …

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 …

We present a novel method for testing the safety of self-driving vehicles in simulation. We
propose an alternative to sensor simulation, as sensor simulation is expensive and has …