This paper proposes a two-stage trajectory planning method for self-driving vehicles (SDVs)
in intersection scenarios with uncertain social circumstances while considering other traffic …

Navigating safely and efficiently in dense and heterogeneous traffic scenarios is challenging
for autonomous vehicles (AVs) due to their inability to infer the behaviors or intentions of …

Autonomous vehicle requires a general planner for all possible scenarios. Existing
researches design such a planner by a unified scenario description. However, it may …

Making safe and human-like decisions is an essential capability of autonomous driving
systems, and learning-based behavior planning presents a promising pathway toward …

Highly automated and fully autonomous vehicles are much more likely to be accepted if they
react in the same way as human drivers do, especially in a hybrid traffic situation, which …

On-road trajectory planning is a direct reflection of an autonomous vehicle's intelligence
level when traveling on an urban road. The prevalent on-road trajectory planners include the …

Motion planning algorithms for urban automated driving must handle uncertainty due to
unknown intention and future motion of Dynamic Obstacles (DOs). Considering a single …

Autonomous navigation in dense traffic scenarios remains challenging for autonomous
vehicles (AVs) because the intentions of other drivers are not directly observable and AVs …

This paper focuses on the real-time trajectory planning problem for autonomous vehicles
driving in realistic urban environments. To solve the complex navigation problem, we adopt …

Connected automated driving has the potential to significantly improve urban traffic
efficiency, eg, by alleviating issues due to occlusion. Cooperative behavior planning can be …