自主车辆的环境感知受其物理传感器范围和算法性能的限制,以及通过降低其对正在进行的交通状况的理解的闭塞。这不仅构成了对安全和限制驾驶速度的重大威胁,而且它也可能导致不方便的动作。智能基础设施系统可以帮助缓解这些问题。智能基础设施系统可以通过在当前交通情况的数字模型的形式提供关于其周围环境的额外详细信息,填补了车辆的感知中的差距并扩展了其视野。数字双胞胎。然而,这种系统的详细描述和工作原型表明其可行性稀缺。在本文中,我们提出了一种硬件和软件架构,可实现这样一个可靠的智能基础架构系统。我们在现实世界中实施了该系统,并展示了它能够创建一个准确的延伸高速公路延伸的数字双胞胎,从而提高了自主车辆超越其车载传感器的极限的感知。此外,我们通过使用空中图像和地球观测方法来评估数字双胞胎的准确性和可靠性,用于产生地面真理数据。
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自动化驾驶系统(广告)开辟了汽车行业的新领域,为未来的运输提供了更高的效率和舒适体验的新可能性。然而,在恶劣天气条件下的自主驾驶已经存在,使自动车辆(AVS)长时间保持自主车辆(AVS)或更高的自主权。本文评估了天气在分析和统计方式中为广告传感器带来的影响和挑战,并对恶劣天气条件进行了解决方案。彻底报道了关于对每种天气的感知增强的最先进技术。外部辅助解决方案如V2X技术,当前可用的数据集,模拟器和天气腔室的实验设施中的天气条件覆盖范围明显。通过指出各种主要天气问题,自主驾驶场目前正在面临,近年来审查硬件和计算机科学解决方案,这项调查概述了在不利的天气驾驶条件方面的障碍和方向的障碍和方向。
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本文提出了一种新颖的方法,用于在具有复杂拓扑结构的地下领域的搜索和救援行动中自动合作。作为CTU-Cras-Norlab团队的一部分,拟议的系统在DARPA SubT决赛的虚拟轨道中排名第二。与专门为虚拟轨道开发的获奖解决方案相反,该建议的解决方案也被证明是在现实世界竞争极为严峻和狭窄的环境中飞行的机上实体无人机的强大系统。提出的方法可以使无缝模拟转移的无人机团队完全自主和分散的部署,并证明了其优于不同环境可飞行空间的移动UGV团队的优势。该论文的主要贡献存在于映射和导航管道中。映射方法采用新颖的地图表示形式 - 用于有效的风险意识长距离计划,面向覆盖范围和压缩的拓扑范围的LTVMAP领域,以允许在低频道通信下进行多机器人合作。这些表示形式与新的方法一起在导航中使用,以在一般的3D环境中可见性受限的知情搜索,而对环境结构没有任何假设,同时将深度探索与传感器覆盖的剥削保持平衡。所提出的解决方案还包括一条视觉感知管道,用于在没有专用GPU的情况下在5 Hz处进行四个RGB流中感兴趣的对象的板上检测和定位。除了参与DARPA SubT外,在定性和定量评估的各种环境中,在不同的环境中进行了广泛的实验验证,UAV系统的性能得到了支持。
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越来越多的交通部门的问题是事故,交通流量不良和污染。智能运输系统使用外部基础架构(其)可以解决这些问题。据我们所知,不存在对现有解决方案的系统审查。为了填补这一知识缺口,本文概述了现有的使用外部基础架构。此外,本文发现目前没有充分的回答的研究问题。出于这个原因,我们对文件进行了文献综述,它自2009年以来介绍了其解决方案。我们根据他的技术水平分类结果并分析了它们的性质。因此,我们使其有所可比性,并突出了过去的发展以及目前的趋势。根据提及的方法,我们分析了346多篇论文,其中包括40个试验床项目。总之,目前其可以实时提供有关交通情况下的个体的高准确信息。然而,在其使用现代传感器,即插即用机制以及高度数据的分散方式中,进一步研究其应重点关注对流量的更可靠的流量感知。通过解决这些主题,智能运输系统的开发处于校正方向,以实现全面推出。
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本文介绍了Cerberus机器人系统系统,该系统赢得了DARPA Subterranean挑战最终活动。出席机器人自主权。由于其几何复杂性,降解的感知条件以及缺乏GPS支持,严峻的导航条件和拒绝通信,地下设置使自动操作变得特别要求。为了应对这一挑战,我们开发了Cerberus系统,该系统利用了腿部和飞行机器人的协同作用,再加上可靠的控制,尤其是为了克服危险的地形,多模式和多机器人感知,以在传感器退化,以及在传感器退化的条件下进行映射以及映射通过统一的探索路径计划和本地运动计划,反映机器人特定限制的弹性自主权。 Cerberus基于其探索各种地下环境及其高级指挥和控制的能力,表现出有效的探索,对感兴趣的对象的可靠检测以及准确的映射。在本文中,我们报告了DARPA地下挑战赛的初步奔跑和最终奖项的结果,并讨论了为社区带来利益的教训所面临的亮点和挑战。
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摄像机是自动化驱动系统中的主要传感器。它们提供高信息密度,并对检测为人类视野提供的道路基础设施线索最优。环绕式摄像机系统通常包括具有190 {\ DEG} +视野的四个鱼眼相机,覆盖在车辆周围的整个360 {\ DEG}集中在近场传感上。它们是低速,高精度和近距离传感应用的主要传感器,如自动停车,交通堵塞援助和低速应急制动。在这项工作中,我们提供了对这种视觉系统的详细调查,在可以分解为四个模块化组件的架构中,设置调查即可识别,重建,重建和重组。我们共同称之为4R架构。我们讨论每个组件如何完成特定方面,并提供一个位置论证,即它们可以协同组织以形成用于低速自动化的完整感知系统。我们通过呈现来自以前的作品的结果,并通过向此类系统提出架构提案来支持此参数。定性结果在视频中呈现在HTTPS://youtu.be/ae8bcof7777uy中。
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安装在微空中车辆(MAV)上的地面穿透雷达是有助于协助人道主义陆地间隙的工具。然而,合成孔径雷达图像的质量取决于雷达天线的准确和精确运动估计以及与MAV产生信息性的观点。本文介绍了一个完整的自动空气缩进的合成孔径雷达(GPSAR)系统。该系统由空间校准和时间上同步的工业级传感器套件组成,使得在地面上方,雷达成像和光学成像。自定义任务规划框架允许在地上控制地上的Stripmap和圆形(GPSAR)轨迹的生成和自动执行,以及空中成像调查飞行。基于因子图基于Dual接收机实时运动(RTK)全局导航卫星系统(GNSS)和惯性测量单元(IMU)的测量值,以获得精确,高速平台位置和方向。地面真理实验表明,传感器时机为0.8美元,正如0.1美元的那样,定位率为1 kHz。与具有不确定标题初始化的单个位置因子相比,双位置因子配方可提高高达40%,批量定位精度高达59%。我们的现场试验验证了本地化准确性和精度,使得能够相干雷达测量和检测在沙子中埋入的雷达目标。这验证了作为鸟瞰着地图检测系统的潜力。
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The last decade witnessed increasingly rapid progress in self-driving vehicle technology, mainly backed up by advances in the area of deep learning and artificial intelligence. The objective of this paper is to survey the current state-of-the-art on deep learning technologies used in autonomous driving. We start by presenting AI-based self-driving architectures, convolutional and recurrent neural networks, as well as the deep reinforcement learning paradigm. These methodologies form a base for the surveyed driving scene perception, path planning, behavior arbitration and motion control algorithms. We investigate both the modular perception-planning-action pipeline, where each module is built using deep learning methods, as well as End2End systems, which directly map sensory information to steering commands. Additionally, we tackle current challenges encountered in designing AI architectures for autonomous driving, such as their safety, training data sources and computational hardware. The comparison presented in this survey helps to gain insight into the strengths and limitations of deep learning and AI approaches for autonomous driving and assist with design choices. 1
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Modeling perception sensors is key for simulation based testing of automated driving functions. Beyond weather conditions themselves, sensors are also subjected to object dependent environmental influences like tire spray caused by vehicles moving on wet pavement. In this work, a novel modeling approach for spray in lidar data is introduced. The model conforms to the Open Simulation Interface (OSI) standard and is based on the formation of detection clusters within a spray plume. The detections are rendered with a simple custom ray casting algorithm without the need of a fluid dynamics simulation or physics engine. The model is subsequently used to generate training data for object detection algorithms. It is shown that the model helps to improve detection in real-world spray scenarios significantly. Furthermore, a systematic real-world data set is recorded and published for analysis, model calibration and validation of spray effects in active perception sensors. Experiments are conducted on a test track by driving over artificially watered pavement with varying vehicle speeds, vehicle types and levels of pavement wetness. All models and data of this work are available open source.
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While the capabilities of autonomous systems have been steadily improving in recent years, these systems still struggle to rapidly explore previously unknown environments without the aid of GPS-assisted navigation. The DARPA Subterranean (SubT) Challenge aimed to fast track the development of autonomous exploration systems by evaluating their performance in real-world underground search-and-rescue scenarios. Subterranean environments present a plethora of challenges for robotic systems, such as limited communications, complex topology, visually-degraded sensing, and harsh terrain. The presented solution enables long-term autonomy with minimal human supervision by combining a powerful and independent single-agent autonomy stack, with higher level mission management operating over a flexible mesh network. The autonomy suite deployed on quadruped and wheeled robots was fully independent, freeing the human supervision to loosely supervise the mission and make high-impact strategic decisions. We also discuss lessons learned from fielding our system at the SubT Final Event, relating to vehicle versatility, system adaptability, and re-configurable communications.
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Mohamed Bin Zayed国际机器人挑战(MBZIRC)2020为无人机(无人机)构成了不同的挑战。我们提供了四个量身定制的无人机,专门为MBZIRC的单独空中机器人任务开发,包括自定义硬件和软件组件。在挑战1中,使用高效率,车载对象检测管道进行目标UAV,以捕获来自目标UAV的球。第二个UAV使用类似的检测方法来查找和流行散落在整个竞技场的气球。对于挑战2,我们展示了一种能够自主空中操作的更大的无人机:从相机图像找到并跟踪砖。随后,将它们接近,挑选,运输并放在墙上。最后,在挑战3中,我们的UAV自动发现使用LIDAR和热敏摄像机的火灾。它用船上灭火器熄灭火灾。虽然每个机器人都具有任务特定的子系统,但所有无人机都依赖于为该特定和未来竞争开发的标准软件堆栈。我们介绍了我们最开源的软件解决方案,包括系统配置,监控,强大无线通信,高级控制和敏捷轨迹生成的工具。为了解决MBZirc 2020任务,我们在多个研究领域提出了机器视觉和轨迹生成的多个研究领域。我们介绍了我们的科学贡献,这些贡献构成了我们的算法和系统的基础,并分析了在阿布扎比的MBZIRC竞赛2020年的结果,我们的系统在大挑战中达到了第二名。此外,我们讨论了我们参与这种复杂的机器人挑战的经验教训。
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Computer vision applications in intelligent transportation systems (ITS) and autonomous driving (AD) have gravitated towards deep neural network architectures in recent years. While performance seems to be improving on benchmark datasets, many real-world challenges are yet to be adequately considered in research. This paper conducted an extensive literature review on the applications of computer vision in ITS and AD, and discusses challenges related to data, models, and complex urban environments. The data challenges are associated with the collection and labeling of training data and its relevance to real world conditions, bias inherent in datasets, the high volume of data needed to be processed, and privacy concerns. Deep learning (DL) models are commonly too complex for real-time processing on embedded hardware, lack explainability and generalizability, and are hard to test in real-world settings. Complex urban traffic environments have irregular lighting and occlusions, and surveillance cameras can be mounted at a variety of angles, gather dirt, shake in the wind, while the traffic conditions are highly heterogeneous, with violation of rules and complex interactions in crowded scenarios. Some representative applications that suffer from these problems are traffic flow estimation, congestion detection, autonomous driving perception, vehicle interaction, and edge computing for practical deployment. The possible ways of dealing with the challenges are also explored while prioritizing practical deployment.
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本文介绍了使用腿收割机进行精密收集任务的集成系统。我们的收割机在狭窄的GPS拒绝了森林环境中的自主导航和树抓取了一项挑战性的任务。提出了映射,本地化,规划和控制的策略,并集成到完全自主系统中。任务从使用定制的传感器模块开始使用人员映射感兴趣区域。随后,人类专家选择树木进行收获。然后将传感器模块安装在机器上并用于给定地图内的本地化。规划算法在单路径规划问题中搜索一个方法姿势和路径。我们设计了一个路径,后面的控制器利用腿的收割机的谈判粗糙地形的能力。在达接近姿势时,机器用通用夹具抓住一棵树。此过程重复操作员选择的所有树。我们的系统已经在与树干和自然森林中的测试领域进行了测试。据我们所知,这是第一次在现实环境中运行的全尺寸液压机上显示了这一自主权。
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Figure 1: We introduce datasets for 3D tracking and motion forecasting with rich maps for autonomous driving. Our 3D tracking dataset contains sequences of LiDAR measurements, 360 • RGB video, front-facing stereo (middle-right), and 6-dof localization. All sequences are aligned with maps containing lane center lines (magenta), driveable region (orange), and ground height. Sequences are annotated with 3D cuboid tracks (green). A wider map view is shown in the bottom-right.
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Robust detection and tracking of objects is crucial for the deployment of autonomous vehicle technology. Image based benchmark datasets have driven development in computer vision tasks such as object detection, tracking and segmentation of agents in the environment. Most autonomous vehicles, however, carry a combination of cameras and range sensors such as lidar and radar. As machine learning based methods for detection and tracking become more prevalent, there is a need to train and evaluate such methods on datasets containing range sensor data along with images. In this work we present nuTonomy scenes (nuScenes), the first dataset to carry the full autonomous vehicle sensor suite: 6 cameras, 5 radars and 1 lidar, all with full 360 degree field of view. nuScenes comprises 1000 scenes, each 20s long and fully annotated with 3D bounding boxes for 23 classes and 8 attributes. It has 7x as many annotations and 100x as many images as the pioneering KITTI dataset. We define novel 3D detection and tracking metrics. We also provide careful dataset analysis as well as baselines for lidar and image based detection and tracking. Data, development kit and more information are available online 1 .
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两栖地面汽车将飞行和驾驶模式融合在一起,以实现更灵活的空中行动能力,并且最近受到了越来越多的关注。通过分析现有的两栖车辆,我们强调了在复杂的三维城市运输系统中有效使用两栖车辆的自动驾驶功能。我们审查并总结了现有两栖车辆设计中智能飞行驾驶的关键促成技术,确定主要的技术障碍,并提出潜在的解决方案,以实现未来的研究和创新。本文旨在作为研究和开发智能两栖车辆的指南,以实现未来的城市运输。
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Video, as a key driver in the global explosion of digital information, can create tremendous benefits for human society. Governments and enterprises are deploying innumerable cameras for a variety of applications, e.g., law enforcement, emergency management, traffic control, and security surveillance, all facilitated by video analytics (VA). This trend is spurred by the rapid advancement of deep learning (DL), which enables more precise models for object classification, detection, and tracking. Meanwhile, with the proliferation of Internet-connected devices, massive amounts of data are generated daily, overwhelming the cloud. Edge computing, an emerging paradigm that moves workloads and services from the network core to the network edge, has been widely recognized as a promising solution. The resulting new intersection, edge video analytics (EVA), begins to attract widespread attention. Nevertheless, only a few loosely-related surveys exist on this topic. A dedicated venue for collecting and summarizing the latest advances of EVA is highly desired by the community. Besides, the basic concepts of EVA (e.g., definition, architectures, etc.) are ambiguous and neglected by these surveys due to the rapid development of this domain. A thorough clarification is needed to facilitate a consensus on these concepts. To fill in these gaps, we conduct a comprehensive survey of the recent efforts on EVA. In this paper, we first review the fundamentals of edge computing, followed by an overview of VA. The EVA system and its enabling techniques are discussed next. In addition, we introduce prevalent frameworks and datasets to aid future researchers in the development of EVA systems. Finally, we discuss existing challenges and foresee future research directions. We believe this survey will help readers comprehend the relationship between VA and edge computing, and spark new ideas on EVA.
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在未来几十年中,自动驾驶将普遍存在。闲置在交叉点上提高自动驾驶的安全性,并通过改善交叉点的交通吞吐量来提高效率。在闲置时,路边基础设施通过卸载从车辆到路边基础设施的知觉和计划,在交叉路口远程驾驶自动驾驶汽车。为了实现这一目标,iDriving必须能够以全帧速率以较少100毫秒的尾声处理大量的传感器数据,而无需牺牲准确性。我们描述了算法和优化,使其能够使用准确且轻巧的感知组件实现此目标,该组件是从重叠传感器中得出的复合视图的原因,以及一个共同计划多个车辆的轨迹的计划者。在我们的评估中,闲置始终确保车辆的安全通过,而自动驾驶只能有27%的时间。与其他方法相比,闲置的等待时间还要低5倍,因为它可以实现无流量的交叉点。
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感知环境是实现合作驾驶自动化(CDA)的最基本关键之一,该关键被认为是解决当代运输系统的安全性,流动性和可持续性问题的革命性解决方案。尽管目前在计算机视觉的物体感知领域正在发生前所未有的进化,但由于不可避免的物理遮挡和单辆车的接受程度有限,最先进的感知方法仍在与复杂的现实世界流量环境中挣扎系统。基于多个空间分离的感知节点,合作感知(CP)诞生是为了解锁驱动自动化的感知瓶颈。在本文中,我们全面审查和分析了CP的研究进度,据我们所知,这是第一次提出统一的CP框架。审查了基于不同类型的传感器的CP系统的体系结构和分类学,以显示对CP系统的工作流程和不同结构的高级描述。对节点结构,传感器模式和融合方案进行了审查和分析,并使用全面的文献进行了详细的解释。提出了分层CP框架,然后对现有数据集和模拟器进行审查,以勾勒出CP的整体景观。讨论重点介绍了当前的机会,开放挑战和预期的未来趋势。
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自动驾驶在过去十年中取得了重大的研究和发展中的重要里程碑。在道路上的自动车辆部署时,对该领域的兴趣越来越令人兴趣,承诺更安全,更生态的运输系统。随着计算强大的人工智能(AI)技术的兴起,自动车辆可以用高精度感测它们的环境,进行安全的实时决策,并在没有人类干预的情况下更可靠地运行。然而,在现有技术中,人类智能决策通常不可能理解,这种缺陷阻碍了这种技术在社会上可接受。因此,除了制造安全的实时决策之外,自治车辆的AI系统还需要解释如何构建这些决策,以便在许多司法管辖区兼容监管。我们的研究在开发可解释的人工智能(XAI)的自治车辆方法上阐明了全面的光芒。特别是,我们做出以下贡献。首先,我们在最先进的自主车辆行业的解释方面彻底概述了目前的差距。然后,我们显示了该领域的解释和解释接收器的分类。第三,我们为端到端自主驾驶系统的架构提出了一个框架,并证明了Xai在调试和调节这些系统中的作用。最后,作为未来的研究方向,我们提供了XAI自主驾驶方法的实地指南,可以提高运营安全性和透明度,以实现监管机构,制造商和所有参与利益相关者的公共批准。
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