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智能车辆的车辆检测技术综述

A Review of Vehicle Detection Techniques for Intelligent Vehicles.

作者信息

Wang Zhangu, Zhan Jun, Duan Chunguang, Guan Xin, Lu Pingping, Yang Kai

出版信息

IEEE Trans Neural Netw Learn Syst. 2023 Aug;34(8):3811-3831. doi: 10.1109/TNNLS.2021.3128968. Epub 2023 Aug 4.

Abstract

Robust and efficient vehicle detection is an important task of environment perception of intelligent vehicles, which directly affects the behavior decision-making and motion planning of intelligent vehicles. Due to the rapid development of sensor and computer technology, the algorithm and technology of vehicle detection have been updated rapidly. But, there are few reviews on vehicle detection of intelligent vehicles, especially covering all kinds of sensors and algorithms in recent years. This article presents a comprehensive review of vehicle detection approaches and their applications in intelligent vehicle systems to analyze the development of vehicle detection, with a specific focus on sensor types and algorithm classification. First, more than 300 research contributions are summarized in this review, including all kinds of vehicle detection sensors (machine vision, millimeter-wave radar, lidar, and multisensor fusion), and the performance of the classic and latest algorithms was compared in detail. Then, the application scenarios of vehicle detection with different sensors and algorithms were analyzed according to their performance and applicability. Moreover, we also systematically summarized the methods of vehicle detection in adverse weather. Finally, the remaining challenges and future research trends were analyzed according to the development of intelligent vehicle sensors and algorithms.

摘要

强大而高效的车辆检测是智能车辆环境感知的一项重要任务,它直接影响智能车辆的行为决策和运动规划。由于传感器和计算机技术的快速发展,车辆检测的算法和技术也在迅速更新。但是,针对智能车辆的车辆检测的综述很少,尤其是近年来涵盖各种传感器和算法的综述。本文对车辆检测方法及其在智能车辆系统中的应用进行了全面综述,以分析车辆检测的发展,特别关注传感器类型和算法分类。首先,本综述总结了300多篇研究成果,包括各种车辆检测传感器(机器视觉、毫米波雷达、激光雷达和多传感器融合),并详细比较了经典算法和最新算法的性能。然后,根据不同传感器和算法的性能及适用性,分析了它们在车辆检测中的应用场景。此外,我们还系统地总结了恶劣天气下的车辆检测方法。最后,根据智能车辆传感器和算法的发展,分析了剩余的挑战和未来的研究趋势。

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