自动车道居中:现成的计算机视觉产品与基于基础设施的芯片式凸起路面标线对比

Parth Kadav, Sachin Sharma, Johan Fanas Rojas, Pritesh Patil, C. Wang, A. R. Ekti, Richard T. Meyer, Zachary D. Asher
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摘要

自动驾驶汽车(AV)的安全运行取决于对驾驶环境的准确感知,这就需要使用各种传感器。然后,计算算法必须处理所有这些传感器数据,这通常会导致较高的车载计算负荷。例如,现有的车道标记是为人类驾驶员设计的,会随着时间的推移而褪色,而且在施工区域可能会出现矛盾,这就需要在自动驾驶汽车中进行专门的传感和计算处理。但是,如果将车道信息直接传输给自动驾驶汽车,就可以避免这一标准流程。高清地图和路侧装置(RSU)可用于向自动驾驶汽车直接传输数据,但其建立和维护费用可能过高。此外,为了确保自动驾驶汽车的稳健和安全运行,增加冗余也是有益的。要有效满足这一需求,必须有一个经济高效的无源解决方案。在这项研究中,我们提出了一种新的基础设施信息源(IIS)--芯片支持的凸起路面标记(CERPMs),它在为自动驾驶汽车提供环境数据的同时,还能降低自动驾驶汽车的计算负荷和相应增加的车辆能耗。CERPM 安装在道路车道沿线,取代了传统的无处不在的凸起路面标记,利用长距离广域网(LoRaWAN)协议直接向附近车辆传输地理空间信息和限速信息。然后将这些信息与 Mobileye 现成的商用传统系统进行比较,后者使用计算机视觉处理车道标记。我们的感知子系统处理来自 CEPRM 和 Mobileye 的原始数据,生成车道居中(LC)应用所需的可行路径。为了评估两种系统的检测性能,我们考虑了三条条件各异的测试路线。结果表明,当道路曲率超过 ±0.016 m-1 时,Mobileye 系统无法检测到车道标记。在陡峭的曲率测试场景中,该系统只能检测到 6.7% 的测试路线两侧的车道标记。另一方面,CERPM 将编程好的地理空间信息传输给车辆上的感知子系统,以生成车辆控制所需的参考轨迹。在所有测试场景中,CERPM 都能成功生成车辆控制所需的参考轨迹。此外,CERPM 可在距离车辆位置 340 米处被探测到。我们的总体结论是,CERPM 技术是可行的,它有潜力解决困扰当前一代自动驾驶汽车的运行稳健性和能效问题。
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Automated Lane Centering: An Off-the-Shelf Computer Vision Product vs. Infrastructure-Based Chip-Enabled Raised Pavement Markers
Safe autonomous vehicle (AV) operations depend on an accurate perception of the driving environment, which necessitates the use of a variety of sensors. Computational algorithms must then process all of this sensor data, which typically results in a high on-vehicle computational load. For example, existing lane markings are designed for human drivers, can fade over time, and can be contradictory in construction zones, which require specialized sensing and computational processing in an AV. But, this standard process can be avoided if the lane information is simply transmitted directly to the AV. High definition maps and road side units (RSUs) can be used for direct data transmission to the AV, but can be prohibitively expensive to establish and maintain. Additionally, to ensure robust and safe AV operations, more redundancy is beneficial. A cost-effective and passive solution is essential to address this need effectively. In this research, we propose a new infrastructure information source (IIS), chip-enabled raised pavement markers (CERPMs), which provide environmental data to the AV while also decreasing the AV compute load and the associated increase in vehicle energy use. CERPMs are installed in place of traditional ubiquitous raised pavement markers along road lane lines to transmit geospatial information along with the speed limit using long range wide area network (LoRaWAN) protocol directly to nearby vehicles. This information is then compared to the Mobileye commercial off-the-shelf traditional system that uses computer vision processing of lane markings. Our perception subsystem processes the raw data from both CEPRMs and Mobileye to generate a viable path required for a lane centering (LC) application. To evaluate the detection performance of both systems, we consider three test routes with varying conditions. Our results show that the Mobileye system failed to detect lane markings when the road curvature exceeded ±0.016 m−1. For the steep curvature test scenario, it could only detect lane markings on both sides of the road for just 6.7% of the given test route. On the other hand, the CERPMs transmit the programmed geospatial information to the perception subsystem on the vehicle to generate a reference trajectory required for vehicle control. The CERPMs successfully generated the reference trajectory for vehicle control in all test scenarios. Moreover, the CERPMs can be detected up to 340 m from the vehicle’s position. Our overall conclusion is that CERPM technology is viable and that it has the potential to address the operational robustness and energy efficiency concerns plaguing the current generation of AVs.
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