为兼顾驾驶员和辅助驾驶系统在车道保持控制中的优势，根据实时驾驶员操作动作和车辆道路信息对车辆横向安全性进行评价，并对车辆控制权在驾驶员和辅助驾驶系统之间做出实时决策，以实现人机协同控制。在车道识别方面，采用了同帧图像的分区识别、相邻帧图像的车道候选区估计等方法。在车道跟踪控制时，根据车辆横向安全性高低采用不同控制策略，并基于模糊规则确定辅助驾驶控制力度以计算人机协同控制时的实际辅助驾驶控制量。在不同车速和不同道路条件下，采用人工驾驶和人机协同控制两种方式进行车道保持实车试验。试验结果表明，所采用的方法能够有效识别道路车道线，且人机协同控制下的车道跟踪具有较好的精确性和稳定性。
In order to obtain the advantages of both manual control and assistance control for lane tracking, the vehicle lateral safety grade is evaluated based on driving action and position relationship of the vehicle and the lane. And control right is decided among the driver and the driver assistance system, so the man-machine coordinative control can be carried out. For the lane image recognition, segmental lane detection and candidate lane estimation is adopted. According to different vehicle lateral safety grading, different control strategies are applied. The assistance control index is calculated based on fuzzy control rules, and then the assistance control is performed when the man-machine coordinative control is needed. Under different road conditions, the road experiments are carried out by means of manual driving and man-machine cooperative driving with different vehicle speed. The results show that the lanes can be recognized efficiently, and the vehicle under man-machine coordinative control can track the lanes more accurately and reliably.
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