자동 차선 변경 시스템의 측후방 차량 충돌 위험도 판단

Abstract

본 논문은 자동 차선 변경 시스템에서 협조 여부 판단 개념을 적용하여 측 후방 영역의 상대 차량에 대한 충돌 위험도 판단 방법을 제시한다. 제안된 충돌 위험도 판단 방법에서 상대 거리, 속도, 가속도 정보를 예측하여 상대 가속도를 추정하기 위해 칼만 필터를 설계했다. 등가속도 모델 기반의 충돌 안전 거리에 대한 상대 거리의 비율로 충돌 위험도 판단 인덱스를 개발했고 안전 조건을 제시하였다. 또한 본 논문에서 제시한 충돌 위험도 판단 알고리즘은 등가속도 모델에서 충돌 위험이 예측될 때, 상대 차량 운전자의 협조/비협조를 판단하여 협조적인 경우 미리 차선 변경을 시작하여 원활한 차선 변경을 가능하도록 한다. 이러한 방법을 실제 차량에 적용 시, 도시와 같이 복잡한 교통 상황에서도 원활하고 빠르게 차선 변경이 가능할 것으로 기대된다. 제안된 방법은 차량 동역학 S/W인 CarSim과 MATLAB/Simulink를 통한 모의 실험을 통해 검증하였다.|We develop a predictive risk assessment for side crash using a cooperation concept in LXS. The predictive risk assessment is determined on the assessment of a situation whether object vehicles are cooperative or not. In the proposed method, Kalman filter is designed to estimate the relative longitudinal acceleration and to predict the relative longitudinal position, velocity, and acceleration. In this paper, we index object vehicles with cooperative or non-cooperative drivers based on their relative acceleration and collision-free time. We also introduce an index for risk assessment considering relative distance and a distance for safety margin. The proposed method enables lane change earlier even though there is collision risk expected at the moment of starting lane change. Thus the proposed method can reduce the execution time for lane change by earlier starting to move the ego vehicle near to the target lane without waiting until collision risk is cleared. The proposed method is validated via computational simulation results with CarSim and MATLAB/Simulink.; We develop a predictive risk assessment for side crash using a cooperation concept in LXS. The predictive risk assessment is determined on the assessment of a situation whether object vehicles are cooperative or not. In the proposed method, Kalman filter is designed to estimate the relative longitudinal acceleration and to predict the relative longitudinal position, velocity, and acceleration. In this paper, we index object vehicles with cooperative or non-cooperative drivers based on their relative acceleration and collision-free time. We also introduce an index for risk assessment considering relative distance and a distance for safety margin. The proposed method enables lane change earlier even though there is collision risk expected at the moment of starting lane change. Thus the proposed method can reduce the execution time for lane change by earlier starting to move the ego vehicle near to the target lane without waiting until collision risk is cleared. The proposed method is validated via computational simulation results with CarSim and MATLAB/Simulink.