Hierarchical CNNPID Based Active Steering Control Method for Intelligent Vehicle Facing Emergency Lane-Changing

Abstract

Abstract To resolve the response delay and overshoot problems of intelligent vehicles facing emergency lane-changing due to proportional-integral-differential (PID) parameter variation, an active steering control method based on Convolutional Neural Network and PID (CNNPID) algorithm is constructed. First, a steering control model based on normal distribution probability function, steady constant radius steering, and instantaneous lane-change-based active for straight and curved roads is established. Second, based on the active steering control model, a three-dimensional constraint-based fifth-order polynomial equation lane-change path is designed to address the stability problem with supersaturation and sideslip due to emergency lane changing. In addition, a hierarchical CNNPID Controller is constructed which includes two layers to avoid collisions facing emergency lane changing, namely, the lane change path tracking PID control layer and the CNN control performance optimization layer. The scaled conjugate gradient backpropagation-based forward propagation control law is designed to optimize the PID control performance based on input parameters, and the elastic backpropagation-based module is adopted for weight correction. Finally, comparison studies and simulation/real vehicle test results are presented to demonstrate the effectiveness, significance, and advantages of the proposed controller.

Keywords

• Intelligent vehicle
• Rear-end collision avoidance
• Steering control
• Dynamics model
• Neural Network
• PID control