A data‐driven chassis coordination control strategy

Abstract

Collaborative control strategy based on different driving conditions is a challenge for chassis systems with various electronic control units. This paper proposes a chassis cooperative control strategy based on vehicle inertial sensor data. Its novelty lies in the fact that it greatly simplifies the judgment logic while classifying various driving behaviours, and further reduces the possibility of bad control operation caused by misjudgement of driving state through heuristic decision logic. The clustering algorithm based on triaxial acceleration and angular velocity data was used to identify the driving behaviour of the vehicle, and the complex driving conditions were simplified into a single driving condition. The multi-axis weighted fusion method is used to extend the data and improve the generalization performance of the data. In order to improve the stability of steering control, S-type function is used to allocate the weights of AFS and DYC. The proposed control strategy is tested in the CarSim/Simulink co-simulation environment, and the simulation results of two key driving scenarios (double lane change and emergency braking) show that the proposed control strategy can effectively improve the vehicle handling and safety.This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

Review of Integrated Chassis Control Techniques for Automated Ground Vehicles

“…As for AD vehicles, global and local targets need to be set [ 116 ]. Therefore, the main sensors that can still be used in ICC are IMU [ 117 ], steering angle, angular wheel velocity sensors, tyre pressure measurement sensors (TPMS), vertical wheel displacement sensors, external temperature sensors, light and rain sensors, sensors for battery monitoring systems in EV such as current, voltage and temperature. IMU, angular wheel velocity sensors, and TPMS [ 118 ] can also provide useful real-time information for vehicle state estimation [ 119 ].…”