Acceleration

Physics

Geology

Control engineering

Automotive engineering

Pure mathematics

Mathematics

Engineering

Artificial intelligence

Control (management)

Transport engineering

Platoon

Overtaking

Potential field

Computer science

Vehicle dynamics

Field (mathematics)

Simulation

Control theory (sociology)

Classical mechanics

Geophysics

Motion planning

Robot

IEEE Transactions on Intelligent Transportation Systems

Autonomous driving of multi-lane vehicle platoons have attracted significant attention in recent years due to their potential to enhance the traffic-carrying capacity of the roads and produce better safety for drivers and passengers. This paper proposes a distributed motion planning algorithm to ensure safe overtaking of autonomous vehicles in a dynamic environment using the Artificial Potential Field method. Unlike the conventional overtaking techniques, autonomous driving strategies can be used to implement safe overtaking via formation control of unmanned vehicles in a complex vehicle platoon in the presence of human-operated vehicles. Firstly, we formulate the overtaking problem of a group of autonomous vehicles into a multi-target tracking problem, where the targets are dynamic. To model a multi-vehicle system consisting of both autonomous and human-operated vehicles, we introduce the notion of velocity difference potential field and acceleration difference potential field. We then analyze the stability of the multi-lane vehicle platoon and propose an optimization-based algorithm for solving the overtaking problem by placing a dynamic target in the traditional artificial potential field. A simulation case study has been performed to verify the feasibility and effectiveness of the proposed distributed motion control strategy for safe overtaking in a multi-lane vehicle platoon.

Distributed Motion Planning for Safe Autonomous Vehicle Overtaking via Artificial Potential Field