摘要
为了保证辅助驾驶技术行车的安全,在分析了基于视觉的车道跑偏检测方法的具体步骤的基础上,首先提出了利用由计算机视觉获得的车道标志线来进一步获得车-路关系的方法,并推导了几种车道跑偏判据TLC(time to lane crossing)的计算公式;然后利用“预瞄最优曲率模型”来仿真人-车-路的关系,并验证了当人的状态发生变化时,TLC判据可以有效地提供报警的效果;最后在红旗自主驾驶样车的视觉导航系统中进行了实验,实验结果表明,上述分析和仿真是可行的。
This paper analyzes the process of lane departure detection approach. In our vision-based Lane Departure Detection system, we use a single camera as input. In this paper, we discuss how to detect the lanes marking on the road and get the relationship between vehicle and road. Some measurements are derived to calculate TLC （time to lane crossing） , for measuring the position of the vehicle relative to the lanes. Besides, the forward-looking predicting mode is introduced to establish the relationship among driver, vehicle, and road. Further more, the criterion can be got to tell whether the vehicle is going to depart the lane without conscious. Simulations show that our vision-based lane departure approach does provide an effective alarm when the state of driver goes wrong. Experiments are taken on vision navigation system for HONGQI prototype, using lane change rather than lane departure. Images obtained from forward looking camera are preprocessed to gain lane markers, and TLC curve is then gained from the alteration of the lane markers position. The results prove that the analysis and simulation above can be feasible. The hazard of lane departure can be forecasted through the alteration of vehicle-road relationship and TLC parameter.
出处
《中国图象图形学报》
CSCD
北大核心
2007年第1期110-115,共6页
Journal of Image and Graphics
基金
国家自然科学基金重点项目(60234030)
关键词
车道跑偏
TLC
驾驶员建模
触界时间
lane departure detection, time to lane crossing（TLC） , driver modeling, active safety system
