Citation

Li K, Yin Z, Ba Y, Yang Y, Kuang Y, Sun E. Machines (Basel) 2023; 11(2): e303.

Copyright

(Copyright © 2023, MDPI Multidisciplinary Digital Publishing Institute)

DOI

10.3390/machines11020303

PMID

unavailable

Abstract

This paper presents an integrated active obstacle avoidance controller in the Model Predictive Control (MPC) framework to ensure adaptive collision-free obstacle avoidance under complex scenarios while maintaining a good level of vehicle stability and steering smoothness. Firstly, with the observed road conditions and obstacle states as inputs, a data-driven Gaussian Process Regression (GPR) model is constructed and trained to generate confidence intervals, as scene-adaptive dynamic safety envelopes represent the safety boundaries of obstacle avoidance. Subsequently, the generated safety envelopes are transformed into soft and hard constraints, incorporated into the MPC controller and rolling updated in the prediction horizon to further cope with uncertain and rapidly evolving driving conditions. Minimizing both the control increments and stability feature parameters are formulated into the objectives of the MPC controller. By solving the multi-objective optimization problem with soft and hard constraints imposed, control commands are obtained to steer the vehicle in order to avoid the obstacles safely and smoothly with guaranteed vehicle stability. The experiments conducted on a motion-base driving simulator show that the proposed controller manages to perform safe and stable obstacle avoidance even under hazardous conditions. It is also verified that the proposed controller can be applied to more complex scenarios with dynamic obstacles presented.

Language: en

Keywords

Gaussian processes regression (GPR); model predictive control (MPC); multi-objective optimization; obstacle avoidance; safety envelope