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A trajectory-based approach for the lateral control of car following systems

机译:基于轨迹的汽车跟随系统横向控制方法

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A crucial task for steering an autonomous vehicle along a safe path in a car following scenario is the lateral control. The sensory input of such a lateral control are the position coordinates of the leader vehicle. The following problem occurs: due to the distance between the leader vehicle and the autonomous ego-vehicle, the lateral control has to interpolate a trajectory between the two vehicles. Using as a trajectory either a straight line or a curve of constant curvature causes the ego-vehicle to deviate from the leader vehicle's trajectory. Given a system delivering 3D points of the leader vehicle with time lags, one has a handle to reconstruct the leader vehicle's trajectory. In addition, one has to compensate the motion of the ego-vehicle by using its motion parameters. Once this transformation is performed, the position coordinates of the leader vehicle are available in a coordinate system at rest. Knowing the position of the ego-vehicle in that coordinate system, one can select the trajectory point of the leader vehicle that is closest to the ego-vehicle as input to the lateral controller. This simple approach increases significantly the precision of car following systems. The algorithm is applied successfully to an autonomous car for platooning at small velocities.
机译:在遵循场景的情况下,使无人驾驶汽车沿着汽车的安全路径行驶的关键任务是横向控制。这种侧向控制装置的感觉输入是引导车辆的位置坐标。发生以下问题:由于引导车辆与自主式自我车辆之间的距离,横向控制必须在两辆车辆之间插入轨迹。将直线或恒定曲率的曲线用作轨迹会导致自我车辆偏离引导车辆的轨迹。给定一种系统,该系统具有时滞来交付领导者车辆的3D点,则可以使用一个手柄来重建领导者车辆的轨迹。另外,必须通过使用其运动参数来补偿自我车辆的运动。一旦执行了此转换,就可以在静止的坐标系中使用引导者车辆的位置坐标。知道了自我车辆在该坐标系中的位置,就可以选择最靠近自我车辆的领导者车辆的轨迹点作为横向控制器的输入。这种简单的方法大大提高了汽车跟随系统的精度。该算法成功地应用于自动驾驶汽车,以小速度进行排。

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