This paper proposes all-around multiple vehicle recognition algorithm based on radar and vision sensor fusion for driver assistance system. In order to realize driver assistance system which considers collision safety toward surrounding vehicles, it is necessary to recognize the vehicles in accordance with longitudinal and lateral position of the vehicles. For this purpose, there are three challenging problems with sensor configuration this thesis presented. First of all, front radar detects objects about guardrail, and it could be recognized left and/of right vehicle. In order to solve this problem, guardrail recognition algorithm which is based on shape and motion attribute is presented. Secondly, due to lack of vision sensor to detect backward, it is impossible to measure radius of curvature in backward. It causes wrong vehicle recognition in curved road especially. To solve the problem, it is suggested that radius of curvature toward rear direction is estimated based on the radius measured by front vision and time delay. The last problem is misdetection in side of ego vehicle, and it is caused by limit of field of view of environmental sensors. In order to improve, tracking and estimation algorithm is proposed. It is realized by discrete Kalman filter based on Constant Velocity (CV) model and Constant Turn Rate and Velocity (CTRV) model. Finally, the suggested algorithm for all-around multiple vehicle recognition is validated via field test data on real road.
