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Dynamic Speed Adaptation for Path Tracking Based on Curvature Information and Speed Limits

机译:基于曲率信息和限速的路径跟踪动态自适应

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摘要

A critical concern of autonomous vehicles is safety. Different approaches have tried to enhance driving safety to reduce the number of fatal crashes and severe injuries. As an example, Intelligent Speed Adaptation (ISA) systems warn the driver when the vehicle exceeds the recommended speed limit. However, these systems only take into account fixed speed limits without considering factors like road geometry. In this paper, we consider road curvature with speed limits to automatically adjust vehicle’s speed with the ideal one through our proposed Dynamic Speed Adaptation (DSA) method. Furthermore, ‘curve analysis extraction’ and ‘speed limits database creation’ are also part of our contribution. An algorithm that analyzes GPS information off-line identifies high curvature segments and estimates the speed for each curve. The speed limit database contains information about the different speed limit zones for each traveled path. Our DSA senses speed limits and curves of the road using GPS information and ensures smooth speed transitions between current and ideal speeds. Through experimental simulations with different control algorithms on real and simulated datasets, we prove that our method is able to significantly reduce lateral errors on sharp curves, to respect speed limits and consequently increase safety and comfort for the passenger.
机译:自动驾驶汽车的关键问题是安全性。不同的方法已尝试提高驾驶安全性,以减少致命事故和严重伤害的次数。例如,当车辆超过建议的速度限制时,智能速度适应(ISA)系统会警告驾驶员。但是,这些系统仅考虑固定速度限制,而不考虑诸如道路几何形状之类的因素。在本文中,我们考虑了具有速度限制的道路曲率,通过我们提出的动态速度自适应(DSA)方法,以理想的速度自动调节车辆的速度。此外,“曲线分析提取”和“速度限制数据库创建”也是我们的贡献之一。离线分析GPS信息的算法可识别高曲率段并估算每条曲线的速度。限速数据库包含有关每个行进路径的不同限速区域的信息。我们的DSA使用GPS信息来感测道路的速度限制和弯道,并确保当前速度与理想速度之间的平稳速度过渡。通过在实际数据集和模拟数据集上使用不同控制算法进行的实验仿真,我们证明了我们的方法能够显着减少急弯处的侧向误差,从而遵守速度限制,从而提高了乘客的安全性和舒适性。

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