Longitudinal Collision Avoidance Based on Model Predictive Controllers and Fuzzy Inference Systems

During the last years' research on Collision Avoidance Systems (CAS) is gaining special attention, due to the decrease of on-road accidents. Current commercial systems can reduce the vehicle speed in case of emergencies such as the appearance of obstacles on the road. However, the behavior of commercial systems is frequently too rigid failing to achieve a proper balance between safety and comfort. In this scenario, this work presents a new approach in which the contextual information of the surrounding environment, such as dedicated infrastructure for vulnerable road users or objects in the vicinity, is used to assess the risks through a Fuzzy inference system. Once risks are evaluated the constraints on the controller acting over the longitudinal vehicle motion are established accordingly. The controller uses a Model Predictive Control (MPC) algorithm. The presented approach illustrates the benefits of modulating the constraints of the MPC controller according to the risk assessment. This approach generates a dynamic speed profile smoothing out critical braking scenarios depending on distances to further objects. For validation, a complex urban scenario was simulated. Results show good performance on the speed planner, also allowing an extendable generalization to different road structures and predefined behaviors from maps and perception systems.