A high-speed human-like collision avoidance controller based on a neural network under different road adhesion coefficients

Safety and stability have been two main considerations in the field of collision avoidance. However, if the response of a vehicle to avoid collision does not conform to the habits of a human driver, it can cause tension and discomfort to the driver, or even worse, misunderstanding between the driver and an automatic driving system can occur, leading to a traffic accident. Therefore, a driver's reaction characteristics should be considered, and the reaction expected by the driver should be satisfied as much as possible. This study proposes a human-like collision avoidance model based on a neural network under emergency conditions. The vehicle response data collected from experienced drivers in the collision-avoidance stage via driving simulators are used to train the neural network model. A front-wheel angle controller is developed to track the output of the human-like neural network model. Moreover, the stability region of a vehicle is obtained by the K-means method. Further, a differential braking controller is designed to ensure the safety and stability of the vehicle. The simulation results show that the proposed human-like collision avoidance controller not only meets the safety and stability requirements of collision avoidance but can also ensure good driving comfort for human drivers.