As a core part of an autonomous driving system, motion planning plays an important role in
safe driving. However, traditional model-and rule-based methods lack the ability to learn …

Safe motion planning in complex and interactive environments is one of the major
challenges for developing autonomous vehicles. In this paper, we propose an interaction …

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 …

Motion planning algorithms, an essential part of the autonomous driving system, have been
extensively studied. However, in large-scale complex scenarios, how to develop an optimal …

This paper presents an efficient hierarchical motion planning framework with a long
planning horizon for autonomous driving in structured environments. A 3D motion planning …

Merge scenarios on highway are often challenging for autonomous driving, due to its lack of
sufficient tacit understanding on and subtle interaction with human drivers in the traffic flow …

Predicting the future states of surrounding traffic participants and planning a safe, smooth,
and socially compliant trajectory accordingly are crucial for autonomous vehicles (AVs) …

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

Learning-based approaches have achieved remarkable performance in the domain of
autonomous driving. Leveraging the impressive ability of neural networks and large …

In this manuscript, we introduce a real-time motion planning system based on the Baidu
Apollo (open source) autonomous driving platform. The developed system aims to address …