Feasibility-based and Personalized Crash Imminence Detection and Control in Braking Situations

Predicting the future speed of a vehicle in driving situation is a challenging task due to the high nonlinearity of human driver's control action in various driving environments. However, such a prediction is beneficial to a number of research areas including intelligent transportation system development, driver behavior evaluation and safety improvement. As an example, an accurate speed prediction model helps the driver prevent vehicle-to-vehicle crash by predicting crash imminent situations in advance. Most speed prediction models consider the acceleration as a control input in the longitudinal case. This paper proposes a novel method to predict the acceleration using the model predictive control (MPC) combined with artificial neural networks (ANNs) as a reference. ANNs are trained to design MPC to have the optimal control law that is the same as the true value of one step future acceleration. To identify various drivers' driving abilities and to develop personalized models, personalized constraints are correspondingly proposed. By analyzing the feasibility, the proposed model is able to detect crash imminent situations based on each driver's daily driving habits. With the personalized crash-imminence detection model, a hybrid MPC is implemented to produce an additional control input as a crash-avoidance measure.