通过智能导航加强自动驾驶:全面改进方法

IF 5.2 2区 计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS
Zihao Xu , Yinghao Meng , Zhen Yin , Bowen Liu , Youzhi Zhang , Mengmeng Lin
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引用次数: 0

摘要

本文开发了一种智能导航系统,以实现准确、快速的自动驾驶响应。该系统通过目标检测、距离测量和导航避障三个模块进行改进。在目标检测模块中,提出了 YOLOv7x-CM 模型,通过引入 CBAM 注意机制和 MPDioU 损失函数来提高目标检测的效率和准确性。在障碍物距离测量模块中,引入了偏心角的概念,优化了传统的单目距离测量方法。在避障模块中,在局部路径规划算法 TEB 中引入了加速跳跃和转向速度约束,并提出了 TEB-S 算法。最后,本文使用 KITTI 数据集和 BDD100K 数据集评估了系统模块的性能。结果表明,YOLOv7x-CM 在 KITTI 数据集和 BDD100K 数据集上的 mAP @ 0.5 指标分别提高了 5.3% 和 6.8%,FPS 也提高了 35.4%。其次,对于优化的单目检测方法,平均相对距离误差减少了 9 倍。此外,与普通 TEB 算法相比,建议的 TEB-S 算法具有更短的避障路径和更高的效率。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Enhancing autonomous driving through intelligent navigation: A comprehensive improvement approach

In this paper, an intelligent navigation system is developed to achieve accurate and rapid response to autonomous driving. The system is improved with three modules: target detection, distance measurement, and navigation obstacle avoidance. In the target detection module, the YOLOv7x-CM model is proposed to improve the efficiency and accuracy of target detection by introducing the CBAM attention mechanism and MPDioU loss function. In the obstacle distance measurement module, the concept of an off-center angle is introduced to optimize the traditional monocular distance measurement method. In the obstacle avoidance module, acceleration jump and steering speed constraints are introduced into the local path planning algorithm TEB, and the TEB-S algorithm is proposed. Finally, this paper evaluates the performance of the system modules using the KITTI dataset and the BDD100K dataset. It is demonstrated that YOLOv7x-CM improves the mAP @ 0.5 metrics by 5.3% and 6.8% on the KITTI dataset and the BDD100K dataset, respectively, and the FPS also increases by 35.4%. Secondly, for the optimized monocular detection method, the average relative distance error is reduced by 9 times. In addition, the proposed TEB-S algorithm has a shorter obstacle avoidance path and higher efficiency than the normal TEB algorithm.

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来源期刊
CiteScore
10.50
自引率
8.70%
发文量
656
审稿时长
29 days
期刊介绍: In 2022 the Journal of King Saud University - Computer and Information Sciences will become an author paid open access journal. Authors who submit their manuscript after October 31st 2021 will be asked to pay an Article Processing Charge (APC) after acceptance of their paper to make their work immediately, permanently, and freely accessible to all. The Journal of King Saud University Computer and Information Sciences is a refereed, international journal that covers all aspects of both foundations of computer and its practical applications.
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