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 …

In this paper, we propose GOHOME, a method leveraging graph representations of the High
Definition Map and sparse projections to generate a heatmap output representing the future …

Trajectory prediction is a critical component for autonomous vehicles (AVs) to perform safe
planning and navigation. However, few studies have analyzed the adversarial robustness of …

Perception and prediction are two separate modules in the existing autonomous driving
systems. They interact with each other via hand-picked features such as agent bounding …

Derived from rapid advances in computer vision and machine learning, video analysis tasks
have been moving from inferring the present state to predicting the future state. Vision-based …

In this paper, we propose THOMAS, a joint multi-agent trajectory prediction framework
allowing for an efficient and consistent prediction of multi-agent multi-modal trajectories. We …

Existing trajectory prediction methods for autonomous driving typically rely on one-stage
trajectory prediction models which condition future trajectories on observed trajectories …

Birds-Eye-View (BEV) perception can naturally represent natural scenes, which is conducive
to multimodal data processing and fusion. BEV data contain rich semantics and integrate the …

With the rapid development of machine learning, autonomous driving has become a hot
issue, making urgent demands for more intelligent perception and planning systems. Self …

Predicting the future trajectories of the traffic agents is a gordian technique in autonomous
driving. However, trajectory prediction suffers from data imbalance in the prevalent datasets …