Automated driving systems face challenges in GPS-denied situations. To address this issue, kinematic dead reckoning is implemented using measurements from the steering angle, steering rate, yaw rate, and wheel speed sensors onboard the vehicle. However, dead reckoning methods suffer from drift. This paper provides an arc-length-based map matching method that uses a digital 2D map of the scenario in order to correct drift in the dead reckoning estimate. The kinematic model's prediction is used to introduce a temporal notion to the spatial information available in the map data. Results show reliable improvement in drift for all GPS-denied scenarios tested in this study. This innovative approach ensures that automated vehicles can maintain continuous and reliable navigation, significantly enhancing their safety and operational reliability in environments where GPS signals are compromised or unavailable.

翻译：自动驾驶系统在GPS拒止场景下面临挑战。为解决此问题，本研究利用车载转向角、转向速率、横摆角速度和轮速传感器的测量数据，实现了基于运动学的航位推算。然而，航位推算方法存在漂移问题。本文提出一种基于弧长的地图匹配方法，该方法利用场景的数字二维地图来修正航位推算估计中的漂移。通过运动学模型的预测，为地图数据中的空间信息引入了时间维度。实验结果表明，该方法在本研究测试的所有GPS拒止场景中均能有效改善漂移。这一创新方法确保了自动驾驶车辆能够保持连续可靠的导航，显著提升了其在GPS信号受损或不可用环境中的安全性与运行可靠性。