共查询到19条相似文献,搜索用时 203 毫秒
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基于ITS技术的汽车驾驶安全辅助系统 总被引:2,自引:0,他引:2
基于ITS技术的汽车驾驶安全辅助系统是提高道路交通安全的有效手段,本文介绍了清华大学汽车安全与节能国家重点实验室在此领域的研究与开发工作。在研究行驶环境感知和信息融合、驾驶员特性和安全距离模型、车辆运动控制及系统集成等关键技术的基础上,研制了汽车驾驶安全辅助系统试验平台和试验样车,实现了行车前撞预警、安全车距保持、智能车道保持等功能,并完成了相关试验分析与评价,为进一步开展基于ITS的汽车主动安全辅助技术的研究以及汽车驾驶辅助系统的产业化奠定了基础。 相似文献
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Mark Tucker 《汽车与配件》2011,(Z1)
最新的驾驶辅助系统(DAS)技术通过传感器数据融合,把来自不同信息源的传感器数据结合起来,形成更全面的有关道路状况的报告,从而让车辆和道路更安全。 相似文献
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驾驶辅助系统(DAS):多种传感器数据的融合 总被引:1,自引:0,他引:1
Mark Tucker 《汽车与配件》2011,(6):28-30
最新的驾驶辅助系统(DAS)技术通过“传感器数据融合”,把来自不同信息源的传感器数据结合起来,形成更全面的有关道路状况的报告,从而让车辆和道路更安全。 相似文献
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同步定位与建图(Simultaneous Localization and Mapping,SLAM)技术可使自动驾驶车辆在未知环境中根据车载传感器采集到的数据估计自身位姿,建立环境地图,为车辆的规划、决策提供定位信息,是近年来自动驾驶技术研究的热点之一。基于车载激光雷达的点云数据,聚焦SLAM技术在自动驾驶领域的应用,围绕前端里程计、后端优化和回环检测技术,对国内外相关研究进行综述。考虑到单一传感器的局限性,结合目前多传感器融合研究的热点与难点,展望了自动驾驶多传感器融合SLAM技术在自动驾驶领域的机遇与挑战。 相似文献
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对驾驶模拟技术在道路行车安全领域的研究及应用现状和存在的问题进行了分析。在广泛调研国内外相关文献的基础上,对驾驶模拟器进行了分类,并总结了国内外主要代表性科研型驾驶模拟器的发展历程,分析了典型驾驶模拟器的自由度、主要特征和应用领域。以“人-车-路-环境-事故”为主线,从不良驾驶行为特性分析、车辆主动安全技术研究、道路与交通设计、车辆驾驶环境以及道路行车事故研究5个方面,系统地梳理了驾驶模拟技术在国内外道路行车安全领域的应用研究现状、存在问题以及应用展望。在不良驾驶行为特性分析方面,重点研究了运用驾驶行为特性开展分心驾驶行为和疲劳驾驶行为的识别;在车辆主动安全技术研究方面,综述了运用驾驶行为开展车辆底盘一体化控制技术、安全辅助驾驶控制技术和自动驾驶接管行为的评价研究;在道路与交通设计方面,综述了道路几何和标志标线等的设计评价;在车辆驾驶环境方面,综述了不良气象、路侧景观和交通冲突等驾驶环境对驾驶行为的影响;在道路行车事故研究方面,总结了道路行车事故再现和事故影响因素分析等内容。此外,对驾驶模拟技术进行了应用展望,主要包括特殊人群的驾驶行为特性、智能网联汽车系统的测试及验证、混合交通流环境下的行车安全问题。对未来应对驾驶模拟器的有效性评价、不适性以及二次开发等问题进行探讨,以便更好地促进驾驶模拟技术的发展。 相似文献
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如果将雷达传感器独立开来,其技术潜力已经差不多被挖尽。因此奥迪在自适应巡航控制系统(ACC)上推出了多传感器监控车辆四周的系统——结合雷达、影像和超声波系统。这种联网化的系统还包括Stop & Go功能,进一步扩展了远程控制系统。慕尼黑的德国联邦陆军大学对这种极端敏感、与安全息息相关的驾驶辅助系统进行了各种项目的测试。 相似文献
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车辆前方行驶环境识别技术探讨 总被引:1,自引:1,他引:0
基于雷达和视觉技术对车辆前方行驶环境识别,进而判断车辆安全状态和实现纵向横行运动状态警示和控制,其是实现汽车安全辅助驾驶的主要技术途径。介绍车辆前方行驶环境识别涉及到的雷达和视觉的一些技术,其中包括雷达种类和适用场合,雷达检测障碍物的算法,车用图像的性能要求,基于图像特征和模型的车道线识别的方法,利用图像实现其他环境信息识别的方法。 相似文献
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S. H. Jeong J. E. Lee S. U. Choi J. N. Oh K. H. Lee 《International Journal of Automotive Technology》2012,13(7):1133-1140
Recently, the advanced driver assistance system (ADAS), which helps mitigate car accidents, has been developed using environmental detection sensors, such as long and short range radar, lidar, wide dynamic range cameras, ultrasonic sensors and laser scanners. Among these detection sensors, radars can quickly provide drivers with reliable information about the velocity, distance and direction of a target obstacle, as well as information about the vehicle in changing weather conditions. In the adaptive cruise control system (ACCS), three radar sensors are usually needed because two short range radars are used to detect objects in the adjacent lane and one long range radar is used to detect objects in-path. In this paper, low-cost radar based on a single sensor, which can detect objects in both the adjacent lane and in-path, is proposed for use in the ACCS. Before designing the proposed radar, we analyzed the world-wide radar technology and market trends for ACCS. Based on this analysis, we designed a novel radar sensor for the ACCS using radar components, such as an antenna, transceiver module, transceiver control module and signal processing algorithm. Finally, target detection experiments were conducted. In the experimental results, the proposed single radar can successfully complete the detection required for the ACCS. In the conclusion, the perspective and issues in the future development of the ACCS radar are described. 相似文献
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智能车辆的障碍物检测研究方法综述 总被引:3,自引:0,他引:3
按照使用传感器的不同类型来分类,对智能车辆的障碍物检测和识别技术进行了综述,并分析各种障碍物检测方法。这些方法中主要包括基于立体视觉方法、基于激光雷达的方法:基于彩色机器视觉的方法及基于结构光的方法等等,同时作者指出任何一种有效的障碍物检测系统不能只依靠单一传感器进行环境感知,因此利用多种传感器信息融合技术检测智能车辆前方障碍物,是未来该领域的研究重点与难点。另外,还介绍了近几年一些研究机构在该领域的研究成果,并对所使用的一些算法进行简要的概括,为我国在智能车辆的障碍物检测领域的发展提供借鉴。 相似文献
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S. H. Jeong C. G. Choi J. N. Oh P. J. Yoon B. S. Kim M. Kim K. H. Lee 《International Journal of Automotive Technology》2010,11(3):409-416
This paper presents a low cost design and implementation of a parallel parking assist system (PPAS) based on ultrasonic sensors.
Generally, a PPAS requires several types of sensors, such as an ultrasonic sensor, camera sensor, radar sensor and laser sensor
for parking space detection. However, our proposed PPAS only requires two ultrasonic sensors on the front and lateral sides
for parking space detection. Moreover, a steering angle sensor and wheel speed sensor installed in the vehicle are used to
obtain vehicle position information for localization in ultrasonic range data. The hardware architecture of the PPAS based
on an electronic control unit (ECU) module, sensor modules and a human machine interface (HMI) module was proposed. Moreover,
the software architecture of the PPAS is based on system initialization, scheduling, recognition and a control algorithm.
In particular, a novel sensor algorithm was proposed to minimize the vehicle corner error of the ultrasonic sensor. A prototype
of the PPAS based on the proposed architecture was constructed. The experimental results demonstrate that the implemented
prototype is robust and successfully performs parking space detection and automatic steering control. Finally, the low cost
design and implementation of the PPAS was possible due to the cheap ultrasonic sensors, simple hardware design and low computational
complexity of the proposed algorithm. 相似文献
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毫米波雷达作为车辆主动安全技术的核心传感器之一,在预警系统中,雷达的安装误差会导致其坐标系与上层应用不一致,影响系统性能甚至安全性。雷达自动校准功能借助整车运行环境中的静止目标,通过解算静止目标物相对雷达运行速度与整车实际运行速度的矢量关系,并采取批量自学习的方法以确定雷达初始安装偏差角度值,进而使用软件补偿的方式对偏差值进行修正,使雷达传感器与整车坐标系保持一致。 相似文献
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As driver assistant systems (DAS) and active safety vehicles (ASV) with various functions become popular, it is not uncommon
for multiple systems to be installed on a vehicle. If each function uses its own sensors and processing unit, it will make
installation difficult and raise the cost of the vehicle. As a countermeasure, research integrating multiple functions into
a single system has been pursued and is expected to make installation easier, decrease power consumption, and reduce vehicle
pricing. This paper proposes a novel side/rear safety system using only one scanning laser radar, which is installed in the
rear corner of the driver’s side. Our proposed system, ISRSS (integrated side/rear safety system), integrates and implements
four system functions: BSD (blind spot detection), RCWS (rear collision warning system), semi-automatic perpendicular parking,
and semi-automatic parallel parking. BSD and RCWS, which operate while the vehicle is running, share a common signal processing
result. The target position designation for perpendicular parking and parallel parking situations is based on the same signal
processing. Furthermore, as system functions during running and those during automatic parking operate in exclusive situations,
they can share common sensors and processing units efficiently. BSD and RCWS system functions were proved with 13025 and 2319
frames, respectively. The target position designation for perpendicular and parallel parking situations was evaluated with
112 and 52 situations and shows a success rate of 98.2% and 92.3%, respectively. 相似文献
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Sang Hyeop Lee Suk Lee Man Ho Kim 《International Journal of Automotive Technology》2018,19(5):837-844
An advanced driver assistance system (ADAS) uses radar, visual information, and laser sensors to calculate variables representing driving conditions, such as time-to-collision (TTC) and time headway (THW), and to determine collision risk using empirically set thresholds. However, the empirically set threshold can generate differences in performance that are detected by the driver. It is appropriate to quickly relay collision risk to drivers whose response speed to dangerous situations is relatively slow and who drive defensively. However, for drivers whose response speed is relatively fast and who drive actively, it may be better not to provide a warning if they are aware of the collision risk in advance, because giving collision warnings too frequently can lower the reliability of the warnings and cause dissatisfaction in the driver, or promote disregard. To solve this problem, this study proposes a collision warning system (CWS) based on an individual driver’s driving behavior. In particular, a driver behavior model was created using an artificial neural network learning algorithm so that the collision risk could be determined according to the driving characteristics of the driver. Finally, the driver behavior model was learned using actual vehicle driving data and the applicability of the proposed CWS was verified through simulation. 相似文献