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Vehicle Positioning in Road Networks without GPS

机译:车辆定位在没有GPS的道路网络中

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Estimating vehicle position on road maps is important for many ITS applications. Advanced Driver Assistance System (ADAS) may expect robust positioning invariant to day time, weather or simplifications induced by the topological representations of road maps. This paper describes a particle filter approach used to achieve vehicle positioning on freely available road maps. Anti-lock Braking System (ABS) and Electronic Stability Program (ESP) sensor data are used in the motion update model. Measurement updates solely rely on vehicle heading. As our results indicate, our approach is able to cope with the odometry error accumulation. We also found that our methodology is able to successfully localize and track a vehicle with a median error of 3.6m in a road network made of 380km of drivable roads with a performance comparable to a high-end INS unit.
机译:估算路线图上的车辆位置对于许多应用来说都很重要。先进的驾驶员辅助系统(ADAS)可能期望有强大的定位不变,到道路地图的拓扑表征所引起的天时间,天气或简化。本文介绍了一种用于在自由可用的道路地图上实现车辆定位的粒子过滤方法。防锁制动系统(ABS)和电子稳定性程序(ESP)传感器数据用于运动更新模型。测量更新完全依赖车辆航向。随着我们的结果表明,我们的方法能够应对内径误差累积。我们还发现,我们的方法能够成功定位和跟踪车辆中位数误差3.6米的道路网络中的误差,该路线由380公里的可驱动道路制成,具有与高端INS单元相当的性能。

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