A typical trajectory planner of self-driving vehicles commonly relies on predicting the future
behavior of surrounding obstacles. Recently, deep learning technology has been widely …

An overtaking trajectory planning algorithm is an essential part of autonomous vehicles, but
maximizing trip efficiency (minimum travel time) while guaranteeing safety is non-trivial. In …

Accurate and reliable prediction of future motions of the nearby agents and effective
environment understanding will contribute to high-quality and meticulous path planning for …

Intersections have been identified as the most complex and accident‐prone traffic scenarios
on road. Making appropriate decisions at intersections for driving safety, efficiency, and …

This paper proposes risk-informed decision-making and control methods for autonomous
vehicles (AVs) under severe driving conditions, where many vehicle interactions occur on …

This paper proposes a novel motion planning and tracking framework based on improved
artificial potential fields (APFs) and a lane change strategy to enhance the performance of …

Accurate behavior prediction of other vehicles in the surroundings is critical for intelligent
transportation systems. Common practices to reason about the future trajectory are through …

This paper presents the design and first test on a simulator of a vehicle trajectory-planning
algorithm that adapts to traffic on a lane-structured infrastructure such as highways. The …

In the coming decades, it is a universal consensus that autonomous vehicles (AVs) and
human-driven vehicles will share the traffic roads. Trajectory planning of AVs has been …

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