Modern autonomous driving system is characterized as modular tasks in sequential order,
ie, perception, prediction, and planning. In order to perform a wide diversity of tasks and …

Autonomous driving vehicles can effectively improve traffic conditions and promote the
development of intelligent transportation systems. An autonomous vehicle can be divided …

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 …

A self-driving vehicle (SDV) must be able to perceive its surroundings and predict the future
behavior of other traffic participants. Existing works either perform object detection followed …

We propose Occupancy Flow Fields, a new representation for motion forecasting of multiple
agents, an important task in autonomous driving. Our representation is a spatio-temporal …

Automated driving has the potential to revolutionize personal, public, and freight mobility.
Beside accurately perceiving the environment, automated vehicles must plan a safe …

Abstract Bird's-eye View (BeV) representations have emerged as the de-facto shared space
in driving applications offering a unified space for sensor data fusion and supporting various …

Self-driving vehicles (SDVs) must be rigorously tested on a wide range of scenarios to
ensure safe deployment. The industry typically relies on closed-loop simulation to evaluate …

Forecasting the scalable future states of surrounding traffic participants in complex traffic
scenarios is a critical capability for autonomous vehicles, as it enables safe and feasible …

Planning the trajectory of the controlled ego vehicle is a key challenge in automated driving.
As for human drivers, predicting the motions of surrounding vehicles is important to plan the …