Automotive engineering

Crash

Mobile robot

Agronomy

Biology

Collision

Engineering

Model predictive control

Political science

Artificial intelligence

Controller (irrigation)

Control (management)

Programming language

Obstacle

Computer science

Collision avoidance

Vehicle dynamics

Computer security

Simulation

Control theory (sociology)

Obstacle avoidance

Motion planning

Robot

IEEE Transactions on Intelligent Transportation Systems

A motion planning method for autonomous vehicles confronting emergency situations where collision is inevitable, generating a path to mitigate the crash as much as possible, is proposed in this paper. The Model predictive control (MPC) algorithm is adopted here for motion planning. If avoidance is impossible for the model predictive motion planning system, the potential crash severity, and artificial potential field are filled into the controller objective to achieve general obstacle avoidance and the lowest crash severity. Furthermore, the vehicle dynamic is also considered as an optimal control problem. Based on the analysis mentioned earlier, the model predictive controller can optimize the command following, obstacle avoidance, vehicle dynamics, road regulation, and mitigate the inevitable crash based on the predicted values. The proposed MPC algorithm has been proved by simulation to have the ability to avoid obstacles and mitigate the crash if collision is inevitable.

Crash Mitigation in Motion Planning for Autonomous Vehicles