No human drives a car in a vacuum; she/he must negotiate with other road users to achieve
their goals in social traffic scenes. A rational human driver can interact with other road users …

Large-scale deployment of autonomous vehicles has been continually delayed due to safety
concerns. On the one hand, comprehensive scene understanding is indispensable, a lack of …

The large-scale deployment of autonomous vehicles is yet to come, and one of the major
remaining challenges lies in urban dense traffic scenarios. In such cases, it remains …

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

Learning-based behavior prediction methods are increasingly being deployed in real-world
autonomous systems, eg, in fleets of self-driving vehicles, which are beginning to …

When autonomous vehicles are deployed on public roads, they will encounter countless and
diverse driving situations. Many manually designed driving policies are difficult to scale to …

Deep motion forecasting models have achieved great success when trained on a massive
amount of data. Yet, they often perform poorly when training data is limited. To address this …

In complex and dynamic urban traffic scenarios, the accurate trajectory prediction of
surrounding pedestrians with interactive behaviors plays a vital role in the self-driving …

Planning under social interactions with other agents is an essential problem for autonomous
driving. As the actions of the autonomous vehicle in the interactions affect and are also …

Learning-based vehicle planning is receiving increasing attention with the emergence of
diverse driving simulators and large-scale driving datasets. While offline reinforcement …