Self-learning control for coordinated collision avoidance of automated vehicles

For the improvement of automotive active safety and the reduction of traffic collisions, significant efforts have been made on developing a vehicle coordinated collision avoidance system. However, the majority of the current solutions can only work in simple driving conditions, and cannot be dynamically optimized as the driving experience grows. In this study, a novel self-learning control framework for coordinated collision avoidance is proposed to address these gaps. First, a dynamic decision model is designed to provide initial braking and steering control inputs based on real-time traffic information. Then, a multilayer artificial neural networks controller is developed to optimize the braking and steering control inputs. Next, a proportional–integral–derivative feedback controller is used to track the optimized control inputs. The effectiveness of the proposed self-learning control method is evaluated using hardware-in-the-loop tests in different scenarios. Experimental results indicate that the proposed method can provide good collision avoidance control effect. Furthermore, vehicle stability during the coordinated collision avoidance control can be gradually improved by the self-learning method as the driving experience grows.