8 accepted papers
Safety-critical scenarios are essential for the development of autonomous vehicles (AVs) but are rare in real-world driving data. While simulation offers a way to generate such scenarios, manually designed test cases lack scalability, and adversarial optimization often produces unrealistic behaviors…
Accurately modeling agent behaviors is an important task in self-driving. It is also a task with many symmetries, such as equivariance to the order of agents and objects in the scene or equivariance to arbitrary roto-translations of the entire scene as a whole; i.e., SE(2)-equivariance. The transfor
Autonomous vehicles (AVs) require extensive testing in simulation, but test case generation for driving scenarios is laborious. The desired scenarios are often out-of-distribution and have precise requirements on interactions with the AV policy under test. Manually programming scenarios allows for p…
We consider the task of traffic scene generation. A common approach in the self-driving industry is to use manual creation to generate scenes with specific characteristics and automatic generation to generate canonical scenes at scale. However, manual creation is not scalable, and automatic generati…
Realistic traffic simulation is crucial for developing self-driving software in a safe and scalable manner prior to real-world deployment. Typically, imitation learning (IL) is used to learn human-like traffic agents directly from real-world observations collected offline, but without explicit speci…
To deploy autonomous vehicles(AVs) in the real world, developers must understand the conditions in which the system can operate safely. To do this in a scalable manner, AVs are often tested in simulation on parameterized scenarios. In this context, it’s important to build a testing framework that pa…
"Recent advances in high-fidelity simulators [22,82,44] have enabled closed-loop training of autonomous driving agents, potentially solving the distribution shift in training v.s. deployment and allowing training to be scaled both safely and cheaply. However, there is a lack of understanding of how…
Neural architecture search (NAS) automatically finds the best task-specific neural network topology, outperforming many manual architecture designs. However, it can be prohibitively expensive as the search requires training thousands of different networks, while each training run can last for hours.…