基于激光雷达传感的ADAS指数自适应巡航控制和转向辅助

IF 4.3 2区 综合性期刊 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC
Abhishek Thakur;C. A. Rakshith Ram;Rajalakshmi Pachamuthu
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引用次数: 0

摘要

自动驾驶汽车是一项开创性的进步,为实现更安全、更高效的交通提供了创新解决方案。这些自动驾驶汽车依赖于先进的驾驶辅助系统(ADAS)功能,这些功能对于在复杂环境中安全导航至关重要。然而,确保安全和平稳运行仍然是一项艰巨的挑战,尤其是在遇到障碍物和转向反应灵敏的情况下。尽管激光雷达是自主导航中用于绘图、定位和感知的综合传感器。在 ADAS 方面对雷达进行了广泛的研究,但对基于激光雷达的巡航控制还需要更多的关注和探索。大多数巡航控制系统主要集成了硬编码或线性速度变化,或者忽略了转向旋转这一关键方面。在本文中,我们为自适应巡航控制(ACC)和自适应转向辅助(ASA)提出了一种基于激光雷达的新型指数方法(e-ACCSA),并将其与连续渐进加速机制整合在一起,以确保更平滑的速度转换。ACC 模块根据激光雷达传感器在感兴趣区域(ROI)内检测到的障碍物控制车速。与此同时,ASA 模块可加强转向控制,并根据转向旋转调整车速。ACC 和 ASA 的集成最终确保了安全无缝的自动驾驶。配合渐进式加速机制,可确保更平稳的速度转换。我们的算法符合美国国家公路交通安全管理局(NHTSA)和美国城市交通官员协会(NACTO)制定的安全速度标准。ADAS 系统在安装了激光雷达传感器的电动汽车上进行了实时测试。代码将在 https://github.com/abhishekt711/LiDAR-ADAS-ACC-ASA 上发布。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
LiDAR Sensing-Based Exponential Adaptive Cruise Control and Steering Assist for ADAS
Autonomous vehicles represent a groundbreaking advancement, prompting innovative solutions for safer, more efficient transportation. These self-driving vehicles rely on advanced driver assistance system (ADAS) features, which are essential for safe navigation through complex environments. Nevertheless, ensuring safety and smooth operation remains a formidable challenge, particularly when faced with obstacles and responsive steering. Although LiDAR serves as a comprehensive sensor for mapping, localization, and perception in autonomous navigation. Extensive research has been carried out on radar in the context of ADAS, but there is a need for more focus and exploration on LiDAR-based cruise control. Most cruise control systems primarily integrate hard-coded or linear speed variations or ignore the critical aspect of steering rotation. In this article, we propose a LiDAR-based novel exponential approach (e-ACCSA) for the adaptive Cruise control (ACC) and adaptive steering assist (ASA), as well as their integration along with the continuous gradual acceleration mechanism ensures smoother velocity transitions. The ACC module controls the vehicle’s speed based on the obstacles detected within the region of interest (ROI) by the LiDAR sensor. Simultaneously, the ASA module enhances steering control and adjusts the vehicle’s speed based on steering rotation. The integration of ACC and ASA ultimately ensures safe and seamless autonomous driving. Coupled with a gradual acceleration mechanism, ensures smoother velocity transitions. Our algorithm aligns with the safe velocity standards set by the National Highway Traffic Safety Administration (NHTSA) and National Association of City Transportation Officials (NACTO). The ADAS system is tested in real-time on an e-vehicle mounted with a LiDAR sensor. The code will be released at https://github.com/abhishekt711/LiDAR-ADAS-ACC-ASA.
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来源期刊
IEEE Sensors Journal
IEEE Sensors Journal 工程技术-工程:电子与电气
CiteScore
7.70
自引率
14.00%
发文量
2058
审稿时长
5.2 months
期刊介绍: The fields of interest of the IEEE Sensors Journal are the theory, design , fabrication, manufacturing and applications of devices for sensing and transducing physical, chemical and biological phenomena, with emphasis on the electronics and physics aspect of sensors and integrated sensors-actuators. IEEE Sensors Journal deals with the following: -Sensor Phenomenology, Modelling, and Evaluation -Sensor Materials, Processing, and Fabrication -Chemical and Gas Sensors -Microfluidics and Biosensors -Optical Sensors -Physical Sensors: Temperature, Mechanical, Magnetic, and others -Acoustic and Ultrasonic Sensors -Sensor Packaging -Sensor Networks -Sensor Applications -Sensor Systems: Signals, Processing, and Interfaces -Actuators and Sensor Power Systems -Sensor Signal Processing for high precision and stability (amplification, filtering, linearization, modulation/demodulation) and under harsh conditions (EMC, radiation, humidity, temperature); energy consumption/harvesting -Sensor Data Processing (soft computing with sensor data, e.g., pattern recognition, machine learning, evolutionary computation; sensor data fusion, processing of wave e.g., electromagnetic and acoustic; and non-wave, e.g., chemical, gravity, particle, thermal, radiative and non-radiative sensor data, detection, estimation and classification based on sensor data) -Sensors in Industrial Practice
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