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 …

Sharing information between connected and autonomous vehicles (CAVs) fundamentally
improves the performance of collaborative object detection for self-driving. However, CAVs …

Autonomous vehicle (AV) stacks are typically built in a modular fashion, with explicit
components performing detection, tracking, prediction, planning, control, etc. While …

The tasks of object detection and trajectory forecasting play a crucial role in understanding
the scene for autonomous driving. These tasks are typically executed in a cascading …

Multi-agent trajectory prediction is crucial for various practical applications, spurring the
construction of many large-scale trajectory datasets, including vehicles and pedestrians …

Object detection and multiple object tracking (MOT) are essential components of self-driving
systems. Accurate detection and uncertainty quantification are both critical for onboard …

Giving autonomous agents the ability to forecast their own outcomes and uncertainty will
allow them to communicate their competencies and be used more safely. We accomplish …

While a lot of work has been carried on developing trajectory prediction methods, and
various datasets have been proposed for benchmarking this task, little study has been done …

Safe, socially compliant, and efficient navigation of low-speed autonomous vehicles (AVs) in
pedestrian-rich environments necessitates considering pedestrians' future positions and …

Trajectory forecasting for traffic participants (eg, vehicles) is critical for autonomous platforms
to make safe plans. Currently, most trajectory forecasting methods assume that object …