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A New Method for Target Object Selection for ACC System Based on Analysis of Vehicle Trajectories

机译:基于车辆轨迹分析的ACC系统目标对象选择的新方法

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摘要

The trajectory variation of preceding objects with changing road curvature and uncertain driving behaviors of both host and preceding cars make it difficult for conventional radar-based Adaptive Cruise Control (ACC) system to effectively select its valid target object, which is mainly caused by the deficient judgment about the preceding curves and the behaviors of preceding cars. Through analysis of the trajectories that host and preceding objects generate, the new proposed method can differentiate the operating conditions of each car, either in straight lane, on curve or in lane-change, thus front path prediction and host vehicle's future lane estimation can be precisely fulfilled. From radar and host car's information a coordinate that changes under several criteria can be established, and based on this coordinate the trajectories of preceding and host car can be recorded and analyzed, some mathematics methods are adopted to reach the qualitative conclusion. The new method can find the valid target for ACC system and enable the system to conquer some typical drawbacks of conventional ACC, such as the confusion between lane-change and curve-enter of a preceding car, and also the speed of a preceding object can be modified as soon as it enters a curve. Simulations have been conducted to validate the method, and the results show that the proposed method could exactly judge the operating condition and find correct target for ACC system.
机译:前面对象具有变化的道路曲率和前轿厢和前车的不确定驾驶行为的轨迹变化使得传统的基于雷达的自适应巡航控制(ACC)系统难以有效地选择其有效的目标对象,这主要是由缺陷引起的关于前曲线的判断以及前面的汽车的行为。通过分析主机和前面对象产生的轨迹,新的方法可以将每辆车的操作条件区分开,直线通道,曲线或车道变化,因此前路径预测和主机的未来车道估计可以精确完成。从雷达和主机的信息中可以建立在若干标准下变化的坐标,并且基于该协调可以记录和分析先前和主车的轨迹,采用了一些数学方法来实现定性结论。新方法可以找到ACC系统的有效目标,使系统能够征服传统ACC的一些典型缺点,例如前车的车道变化和曲线之间的混淆,以及前面的物体的速度可以一旦进入曲线就会修改。已经进行了仿真以验证该方法,结果表明,该方法可以完全判断操作条件并找到ACC系统的正确目标。

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