In recent years, imitation-based driving planners have reported considerable success.
However, due to the absence of a standardized benchmark, the effectiveness of various …

Many existing motion prediction approaches rely on symbolic perception outputs to generate
agent trajectories such as bounding boxes road graph information and traffic lights. This …

Autonomous Driving (AD) has evolved significantly since its beginnings in the 1980s, with
continuous advancements driven by both industry and academia. Traditional AD systems …

Motion prediction and cost evaluation are vital components in the decision-making system of
autonomous vehicles. However, existing methods often ignore the importance of cost …

Predicting the future motion of surrounding vehicles is a crucial enabler for safe autonomous
driving. The field of motion prediction has seen large progress recently with State-of-the-Art …

Motion planning is an essential element of the modular architecture of autonomous vehicles,
serving as a bridge between upstream perception modules and downstream low-level …

Autonomous driving systems require a comprehensive understanding and accurate
prediction of the surrounding environment to facilitate informed decision-making in complex …

Real-world autonomous driving systems must make safe decisions in the face of rare and
diverse traffic scenarios. Current state-of-the-art planners are mostly evaluated on real-world …