Object Detection in Adaptive Cruise Control Using Multi-Class Support Vector Machine

In this paper, we present a new object detection method in Adaptive Cruise Control (ACC) with the Support Vector Machine (SVM) algorithm using data from a radar system. ACC using Closest in Path Vehicle (CIPV) detects the object vehicle that comes in front of the vehicle's front radar. Therefore, if the object vehicle abruptly cuts into the lane ahead of ego vehicle, the speed of the ego vehicle should quickly reduce. This phenomenon makes passengers feel uncomfortable. To cope with this phenomenon, in this paper we propose multiple classifications of various driving situations using multi-class SVM. Classified data was used to detect the CIPV among nearby vehicles in ACC. The proposed method shows improved performance in predicting the motion of objects in advance over the conventional radar system so that it enable for the ACC system either to decelerate or to accelerate smoothly in advance. The performance of proposed method was validated via experimental results.