论文部分内容阅读
为提高车辆自适应巡航控制(ACC)系统的有效性,利用雷达传感器、车道线识别传感器、车载陀螺仪、车辆总线设备等搭建车载试验平台,获取真实交通流环境中自车与前方车辆运动状态时的表征数据。基于自车与前方车辆的距离、前方车辆的横向速度与纵向速度参数,采用隐马尔科夫理论,建立前方车辆换道意图预测模型。用实测数据检验该模型。结果表明:用该模型能够准确快速预测前方车辆的车道变换与车道保持行为。在4.5 s的时间窗口宽度下,直线路段的预测准确率达到97%;在3.5 s的时间窗口宽度下,曲线路段的预测准确率达到96%。
In order to improve the effectiveness of vehicle adaptive cruise control (ACC) system, a vehicle test platform is built using radar sensors, lane-line recognition sensors, vehicle gyroscopes, vehicle bus devices and so on to obtain the movement status of vehicles and vehicles in the real traffic flow environment When the characterization data. Based on the distance between the vehicle and the vehicle in front and the speed and longitudinal velocity of the vehicle in the front, Hidden Markov theory was used to establish the prediction model of lane changing intention in front of the vehicle. The experimental data are used to test the model. The results show that the model can accurately and quickly predict the lane change and lane keeping behavior of the vehicle ahead. Under the time width of 4.5 s, the prediction accuracy of straight-line segments reaches 97%. The prediction accuracy of curved segments reaches 96% under the 3.5-s time window width.