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 …

Planning an autonomous vehicle's (AV) path in a space shared with pedestrians requires
reasoning about pedestrians' future trajectories. A practical pedestrian trajectory prediction …

Traffic flow prediction methods commonly rely on historical traffic data, such as traffic volume
and speed, but may not be suitable for high-capacity expressways or during peak traffic …

An effective understanding of the environment and accurate trajectory prediction of
surrounding dynamic obstacles are indispensable for intelligent mobile systems (eg …

Autonomous vehicles have a great potential in the application of both civil and military fields,
and have become the focus of research with the rapid development of science and …

Due to the complex and dynamic character of intersection scenarios, the autonomous
driving strategy at intersections has been a difficult problem and a hot point in the research …

In driving scenes, road agents often engage in frequent interaction and strive to understand
their surroundings. Ego-agent (each road agent itself) predicts what behavior will be …

While autonomous vehicles still struggle to solve challenging situations during on-road
driving, humans have long mastered the essence of driving with efficient, transferable, and …

Accurate citywide traffic inference is critical for improving intelligent transportation systems
with smart city applications. However, this task is very challenging given the limited training …

As one of the main elements of reinforcement learning, the design of the reward function is
often not given enough attention when reinforcement learning is used in concrete …