一种基于目标检测与跟踪的自动驾驶车辆后备定位算法

IF 5.3 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC
Mario Rodríguez-Arozamena;Jose Matute;Javier Araluce;Lukas Kuschnig;Christoph Pilz;Markus Schratter;Joshué Pérez Rastelli;Asier Zubizarreta
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引用次数: 0

摘要

将自动驾驶汽车(AVs)融入日常交通是一项持续的挑战。即使在系统出现故障的情况下,确保所有相关代理的安全至关重要,尤其是在城市环境中。本文介绍了一种针对自动驾驶汽车在主定位源故障时运行的回退定位算法。该方法利用静止车辆作为动态地标,通过感知模块识别,尽管它们最初的位置未知。该算法通过跟踪故障前的相对位置并应用三边测量,估计出自我车辆的位置。通过仿真、真实数据集和两种车辆模型的实际测试对该算法进行了评估。结果包括与不同撤退机动的地面真实情况相比的平均弹道误差为0.62米和1.58度。这意味着平均相对平移误差为1.65%,相对旋转误差为0.05度/米,从而提高了基于imu的航位推算的性能,从而为执行安全停车操作提供了定位。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Fallback Localization Algorithm for Automated Vehicles Based on Object Detection and Tracking
Integrating Automated Vehicles (AVs) into everyday traffic is an ongoing challenge. Ensuring the safety of all involved agents, even in the presence of system failures, is crucial, especially in urban environments. This paper introduces a fallback-oriented localization algorithm for AVs designed to operate during main localization source failures. The method leverages stationary vehicles as dynamic landmarks, identified through the perception module, despite their initially unknown positions. By tracking relative positions before failure and applying trilateration, the algorithm estimates the ego vehicle's position. The proposed algorithm is evaluated through simulations, a real-world dataset, and practical tests on two vehicle models. The results include an average trajectory error of 0.62 m and 1.58 deg compared to the ground truth over different fallback maneuvers. This translates into an average relative translational error of 1.65% and a relative rotational error of 0.05 deg/m, improving the performance of an IMU-based dead reckoning and, hence, providing localization for performing safe stop maneuvers.
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来源期刊
CiteScore
9.60
自引率
0.00%
发文量
25
审稿时长
10 weeks
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