Prediction of Centerlane Violation for vehicle in opposite direction using Fuzzy Logic and Interacting Multiple Model

Abstract

For intelligent vehicle technology, it is very important to recognize the states of around vehicles and assess the collision risk for safety driving of the vehicle. Specifically, it is very fatal the collision with the vehicle coming from opposite direction. In this paper, a centerlane violation prediction method is proposed. Only radar signal based prediction makes lots of false alarm cause of measurement noise and the false alarm can make more danger situation than the non-prediction situation. We proposed the novel prediction method using IMM algorithm and fuzzy logic to increase accuracy and get rid of false positive. Fuzzy logic adjusts the radar signal and the IMM algorithm appropriately. It is verified by the computer simulation that shows stable prediction result and fewer number of false alarm.

Novel Collision Warning System using Neural Networks

“…자차 량과 주변의 차량간의 충돌 가능성을 판단하는 것도 그중 하나이다 [4]. 하지만 충돌 이전에 충돌 가능성을 판단하는 것은 일반적인 방법으로는 불가능하기 때문에 몬테 카를로 모의실험 [5], 퍼지 기법 [6] 시간 ∆이후의 자차량의 위치 및 진행방향은 식(1) ∼ 식(3)을 이용하여 구할 수 있다.…”