Behavior-related research areas such as motion prediction/planning, representation/
imitation learning, behavior modeling/generation, and algorithm testing, require support from …

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 …

Vehicle trajectory prediction at intersections is both essential and challenging for
autonomous vehicle navigation. This problem is aggravated when the traffic is …

Autonomous vehicles operating in complex real-world environments require accurate
predictions of interactive behaviors between traffic participants. This paper tackles the …

Autonomous driving in multi-agent dynamic traffic scenarios is challenging: the behaviors of
road users are uncertain and are hard to model explicitly, and the ego-vehicle should apply …

We introduce Argoverse 2 (AV2)-a collection of three datasets for perception and forecasting
research in the self-driving domain. The annotated Sensor Dataset contains 1,000 …

We focus on the problem of predicting future states of entities in complex, real-world driving
scenarios. Previous research has approached this problem via low-level signals to predict …

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

To enable intelligent automated driving systems, a promising strategy is to understand how
human drives and interacts with road users in complicated driving situations. In this paper …

Vehicle trajectory prediction is crucial for autonomous driving and advanced driver assistant
systems. While existing approaches may sample from a predicted distribution of vehicle …