基于主观逻辑的自动驾驶系统多源定位在线性能评估
Online Performance Assessment of Multi-Source-Localization for Autonomous Driving Systems Using Subjective Logic
AI总结：
本文提出基于主观逻辑的多源定位在线性能评估方法，对里程计、SLAM和GNSS三种定位系统分别建模并相互参照，在隧道环境中验证了其可行性。
摘要：
自动驾驶（AD）高度依赖高精度定位，这是所有与驾驶相关的软件组件的关键部分。精确的定位对于利用高精地图、预测其他道路参与者以及控制车辆本身都是必需的。由于这一原因，定位绝对与安全相关。定位系统的典型误差包括长期漂移、跳变和错误定位，必须检测这些误差以提高安全性。对当前定位性能进行在线评估和评价是一项具有挑战性的任务，通常通过卡尔曼滤波对单一 定位系统进行处理。当前的自动驾驶车辆通过将多个单独的定位方法融合到一个整体状态估计中来应对这些挑战。这类方法需要专家知识，才能在具有挑战性的环境中获得有竞争力的性能。这种专家知识基于对当前情境和环境的不同定位方法的信任和优先级排序。\n本文提出了一种新的基于主观逻辑（SL）的多定位系统在线性能评估技术。在我们的研究车辆中，有三种不同的定位系统可用，即基于里程计、同时定位与建图（SLAM）和全球导航卫星系统（GNSS）的系统。我们的性能评估分别对这三个定位系统的行为进行建模，并将它们相互参照。实验使用基于奥迪A6的CoCar NextGen进行。该车辆的定位系统在具有挑战性的条件下进行了评估，特别是在隧道环境中。整体评估表明了该方法的可行性。
英文摘要：
Autonomous driving (AD) relies heavily on high precision localization as a crucial part of all driving related software components. The precise positioning is necessary for the utilization of high-definition maps, prediction of other road participants and the controlling of the vehicle itself. Due to this reason, the localization is absolutely safety relevant. Typical errors of the localization systems, which are long term drifts, jumps and false localization, that must be detected to enhance safety. An online assessment and evaluation of the current localization performance is a challenging task, which is usually done by Kalman filtering for single localization systems. Current autonomous vehicles cope with these challenges by fusing multiple individual localization methods into an overall state estimation. Such approaches need expert knowledge for a competitive performance in challenging environments. This expert knowledge is based on the trust and the prioritization of distinct localization methods in respect to the current situation and environment. This work presents a novel online performance assessment technique of multiple localization systems by using subjective logic (SL). In our research vehicles, three different systems for localization are available, namely odometry-, Simultaneous Localization And Mapping (SLAM)- and Global Navigation Satellite System (GNSS)-based. Our performance assessment models the behavior of these three localization systems individually and puts them into reference of each other. The experiments were carried out using the CoCar NextGen, which is based on an Audi A6. The vehicle's localization system was evaluated under challenging conditions, specifically within a tunnel environment. The overall evaluation shows the feasibility of our approach.
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