Forward collision avoidance systems considering driver's driving behavior recognized by Gaussian Mixture Model

Although it is well known that driver's intention and driving behavior have great influence on the performance of the advanced driver assistance systems (ADAS), little consideration has been taken in the design of the existing systems. To improve the system performance, in particular, the acceptance and adaption of ADAS to human drivers, it is important to understand human drivers' intention and driving behavior that makes the systems more human-like or personalized for forward collision avoidance (FCA) and autonomous emergency braking (AEB). The research presented in this paper proposed a method to recognize driver's intention and driving behavior based on Gaussian Mixture Model (GMM). A typical testing scenarios of longitudinal braking case was created under a real-time driving simulator with both PanoSim-RT® and dSPACE®. The samples with 36 drivers were used for the testing, and the driving data were collected, analyzed and further employed in driving behavior recognition via a Gaussian mixture model. An optimization method was taken in model parameter identification. The parameters were used in the control design of FCA systems. Compared with existing FCA systems, the proposed personalized systems have demonstrated advantages in both performance and human acceptance.