Trajectory planning is essential for self-driving vehicles and has stringent requirements for
accuracy and efficiency. The existing trajectory planning methods have limitations in the …

Neural network-based driving planners have shown great promises in improving task
performance of autonomous driving. However, it is critical and yet very challenging to ensure …

Currently autonomous or self-driving vehicles are at the heart of academia and industry
research because of its multi-faceted advantages that includes improved safety, reduced …

This paper introduces a dynamic model of the driver control strategy of lane-change
behavior and applies it to trajectory planning in driver-assistance systems. The proposed …

Trajectory tracking control in the cut-in scenarios is challenging, since the autonomous
vehicles have to follow the reference trajectory and cooperate with the cut-in vehicles. This …

Knowledge of an automobile driver's intended actions (eg, to turn, change lanes, etc.) could
facilitate the integration of intelligent vehicle systems with the driver. The actions can be …

A safe trajectory planning for on-road autonomous driving is a challenging problem owing to
the variety and complexity of driving environments. The problem should involve the …

In this paper, a state space sampling-based local trajectory generation framework for
autonomous vehicles driving along a reference path is proposed. The presented framework …

Reliable trajectory prediction of preceding target vehicles (PTVs) is crucial for the planning
and decision making of automated vehicles. However, the future trajectory is affected by the …

Lane change is one of the most principle driving behaviors on structure roads. It frequently
happens in daily driving. A key issue in lane change technique is trajectory planning, where …