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This paper presents a robust method to estimate the relative distance and speed with the approaching vehicles in side and rear blind region of the vehicle for a BSDS with a vision system. Because the vehicle with same relative distance can be represented at different pixel position on the image frame according to the road curvature, a Perception Action Network has been designed based upon the lane model and the vehicle dynamics in order to recognize the rear side lanes and its curvature under ill conditioned background image. Through the various simulation studies for the proposed BSDS by using MATLAB and PRESCAN, it has been concluded that the proposed method can provide the accurate estimations of the relative distance and speed between the vehicles during lane change with satisfied error tolerance.

This study proposes a map-based control method to improve a vehicle's lateral stability, and the performance of the proposed method is compared with that of the conventional model-referenced control method. Model-referenced control uses the sliding mode method to determine the compensated yaw moment; in contrast, the proposed map-based control uses the compensated yaw moment map acquired by vehicle stability analysis. The vehicle stability region is calculated by a topological method based on the trajectory reversal method. A 2-DOF vehicle model and Pacejka's tire model are used to evaluate the proposed map-based control method. The performances of model-referenced control and map-based control are compared under various road conditions and driving inputs.