Planning safe trajectories for autonomous vehicles in complex urban environments is
challenging since there are numerous semantic elements (such as dynamic agents, traffic …

In this article, we present a spatiotemporal trajectory planning algorithm for emergency
obstacle avoidance. Utilizing obstacle and driving environment data from the sensing …

Autonomous vehicles face numerous challenges to ensure safe operation in unpredictable
and hazardous conditions. The autonomous driving environment is characterized by high …

As a fundamental component of autonomous driving systems, motion planning has garnered
significant attention from both academia and industry. This paper focuses on efficient and …

Generating safe and non-conservative behaviors in dense, dynamic environments remains
challenging for automated vehicles due to the stochastic nature of traffic participants' …

The growing variety and complexity of marine research and application oriented tasks
requires unmanned surface vehicles (USVs) to operate fully autonomously over long time …

Potential fields have been integrated with local path-planning algorithms for autonomous
vehicles (AVs) to tackle challenging scenarios with dense and dynamic obstacles. Most …

Path planning for autonomous vehicles in arbitrary environments requires a guarantee of
safety, but this can be impractical to ensure in real-time when the vehicle is described with a …

Motion planning at urban intersections that accounts for the situation context, handles
occlusions, and deals with measurement and prediction uncertainty is a major challenge on …

The actions of an autonomous vehicle on the road affect and are affected by those of other
drivers, whether overtaking, negotiating a merge, or avoiding an accident. This mutual …