Abstract Understanding of the driving scenario represents a necessary condition for autonomous driving. Within the control routine of an autonomous vehicle, it represents the preliminary step for the motion planning system. Estimation algorithms hence need to handle a considerable number of information coming from multiple sensors, to provide estimates regarding the motion of ego-vehicle and surrounding obstacles. Furthermore, tracking is crucial in obstacles state estimation, because it ensures obstacles recognition during time. This paper presents an integrated algorithm for the estimation of ego-vehicle and obstacles’ positioning and motion along a given road, modeled in curvilinear coordinates. Sensor fusion deals with information coming from two Radars and a Lidar to identify and track obstacles. The algorithm has been validated through experimental tests carried on a prototype of an autonomous vehicle.

中文翻译：

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一种用于自动驾驶的自车和障碍物状态估计的集成算法

摘要 对驾驶场景的理解是自动驾驶的必要条件。在自动驾驶汽车的控制程序中，它代表了运动规划系统的初步步骤。因此，估计算法需要处理来自多个传感器的大量信息，以提供关于自我车辆和周围障碍物的运动的估计。此外，跟踪在障碍物状态估计中至关重要，因为它可以确保在一定时间内识别障碍物。本文提出了一种集成算法，用于估计沿给定道路的自我车辆和障碍物的定位和运动，以曲线坐标为模型。传感器融合处理来自两个雷达和一个激光雷达的信息，以识别和跟踪障碍物。