Traffic surveillance is an important issue in Intelligent Transportation Systems(ITS). In this paper, we propose a novel surveillance system to detect and track vehicles using ubiquitously deployed magnetic sensors. That is, multiple magnetic sensors, mounted roadside and along lane boundary lines, are used to track various vehicles. Real-time vehicle detection data are reported from magnetic sensors, collected into data center via base stations, and processed to depict vehicle trajectories including vehicle position, timestamp, speed and type. We first define a vehicle trajectory tracking problem. We then propose a graph-based data association algorithm to track each detected vehicle, and design a related online algorithm framework respectively. We finally validate the performance via both experimental simulation and real-world road test. The experimental results demonstrate that the proposed solution provides a cost-effective solution to capture the driving status of vehicles and on that basis form various traffic safety and efficiency applications.

计算机视觉在智能运输系统（ITS）和交通监视中发挥了重要作用。除了快速增长的自动化车辆和拥挤的城市外，通过实施深层神经网络的实施，可以使用视频监视基础架构进行自动和高级交通管理系统（ATM）。在这项研究中，我们为实时交通监控提供了一个实用的平台，包括3D车辆/行人检测，速度检测，轨迹估算，拥塞检测以及监视车辆和行人的相互作用，都使用单个CCTV交通摄像头。我们适应了定制的Yolov5深神经网络模型，用于车辆/行人检测和增强的排序跟踪算法。还开发了基于混合卫星的基于混合卫星的逆透视图（SG-IPM）方法，用于摄像机自动校准，从而导致准确的3D对象检测和可视化。我们还根据短期和长期的时间视频数据流开发了层次结构的交通建模解决方案，以了解脆弱道路使用者的交通流量，瓶颈和危险景点。关于现实世界情景和与最先进的比较的几项实验是使用各种交通监控数据集进行的，包括从高速公路，交叉路口和城市地区收集的MIO-TCD，UA-DETRAC和GRAM-RTM，在不同的照明和城市地区天气状况。

智能运输系统（ITS）对可持续和绿色城市生活的发展至关重要。它是数据驱动的，并通过从气管到智能相机的传感器大量来启用。这项工作探索了基于基于光纤的分布式声传感器（DAS）的新型数据源，以进行交通分析。检测车辆的类型和估计车辆的占用是其主要关注点。第一个是由于需要跟踪，控制和预测交通流的动机。第二个目标是对高占用车辆车道的调节，以减少排放和拥堵。这些任务通常是通过检查车辆或使用新兴计算机视觉技术来执行的。前者不可扩展或有效，而后者对乘客的隐私有侵入性。为此，我们提出了一种深度学习技术，以分析DAS信号，以通过连续感应和不暴露个人信息来应对这一挑战。我们提出了一种处理DAS信号的深度学习方法，并基于在受控条件下收集的DAS数据来实现92％的车辆分类准确性和92-97％的占用检测。

Vehicle trajectory data has received increasing research attention over the past decades. With the technological sensing improvements such as high-resolution video cameras, in-vehicle radars and lidars, abundant individual and contextual traffic data is now available. However, though the data quantity is massive, it is by itself of limited utility for traffic research because of noise and systematic sensing errors, thus necessitates proper processing to ensure data quality. We draw particular attention to extracting high-resolution vehicle trajectory data from video cameras as traffic monitoring cameras are becoming increasingly ubiquitous. We explore methods for automatic trajectory data reconciliation, given "raw" vehicle detection and tracking information from automatic video processing algorithms. We propose a pipeline including a) an online data association algorithm to match fragments that are associated to the same object (vehicle), which is formulated as a min-cost network flow problem of a graph, and b) a trajectory reconciliation method formulated as a quadratic program to enhance raw detection data. The pipeline leverages vehicle dynamics and physical constraints to associate tracked objects when they become fragmented, remove measurement noise on trajectories and impute missing data due to fragmentations. The accuracy is benchmarked on a sample of manually-labeled data, which shows that the reconciled trajectories improve the accuracy on all the tested input data for a wide range of measures. An online version of the reconciliation pipeline is implemented and will be applied in a continuous video processing system running on a camera network covering a 4-mile stretch of Interstate-24 near Nashville, Tennessee.

随着自动驾驶的发展，单个车辆的自动驾驶技术的提高已达到瓶颈。车辆合作自动驾驶技术的进步可以扩大车辆的感知范围，补充感知盲区并提高感知的准确性，以促进自主驾驶技术的发展并实现车辆路整合。该项目主要使用LIDAR来开发数据融合方案，以实现车辆和道路设备数据的共享和组合，并实现动态目标的检测和跟踪。同时，设计和用于测试我们的车辆道路合作意识系统的一些测试方案，这证明了车辆道路合作自动驾驶在单车自动驾驶上的优势。

如今，智能高速公路交通网络在现代运输基础设施中发挥了重要作用。可以在高速公路交通网络中促进可变速度限制（VSL）系统，以提供有用的动态速度限制信息，供驾驶员增强安全性。这种系统通常以稳定的咨询速度设计，因此当驾驶员遵循速度时，流量可以顺利移动，而不是在充满差距并放慢拥堵时加速。但是，当驾驶员离开由VSL系统管理的道路网络时，对车辆行为的研究几乎没有引起关注，VSL系统可能在很大程度上涉及意外的加速，减速和频繁的车道变化，从而造成随后的高速公路道路使用者的混乱。在本文中，由于驾驶员在VSL系统之后的高速公路交通网络上的车道变更意图，我们将重点关注交通流量异常。更具体地说，我们将图形建模应用于流行的移动模拟器Sumo在路段级别生成的交通流数据。然后，我们使用拟议的Lane-GNN方案（注意时间表卷积神经网络）评估车道变化检测的性能，并将其性能与时间卷积神经网络（TCNN）作为我们的基线进行比较。我们的实验结果表明，在某些假设下，提出的巷道GNN可以在90秒内以99.42％的精度检测驾驶员的车道变化意图。最后，将一些解释方法应用于受过训练的模型，以进一步说明我们的发现。

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.

具有自动化和连通性的赋予，连接和自动化的车辆旨在成为合作驾驶自动化的革命性推动者。然而，骑士需要对周围环境的高保真感知信息，但从各种车载传感器以及车辆到所有的通信（v2x）通信中都可以昂贵。因此，通过具有成本效益的平台基于高保真传感器的真实感知信息对于启用与CDA相关的研究（例如合作决策或控制）至关重要。大多数针对CAVS的最先进的交通模拟研究都通过直接呼吁对象的内在属性来依赖情况 - 意识信息，这阻碍了CDA算法评估的可靠性和保真度。在这项研究中，\ textit {网络移动镜（CMM）}共模拟平台设计用于通过提供真实感知信息来启用CDA。 \ textit {cmm}共模拟平台可以通过高保真传感器感知系统和具有实时重建系统的网络世界模仿现实世界。具体而言，现实世界的模拟器主要负责模拟交通环境，传感器以及真实的感知过程。 Mirror-World Simulator负责重建对象，并将其信息作为模拟器的内在属性，以支持CD​​A算法的开发和评估。为了说明拟议的共模拟平台的功能，将基于路边的激光雷达的车辆感知系统原型作为研究案例。特定的流量环境和CDA任务是为实验设计的，其结果得到了证明和分析以显示平台的性能。

Multi-modal fusion is a basic task of autonomous driving system perception, which has attracted many scholars' interest in recent years. The current multi-modal fusion methods mainly focus on camera data and LiDAR data, but pay little attention to the kinematic information provided by the bottom sensors of the vehicle, such as acceleration, vehicle speed, angle of rotation. These information are not affected by complex external scenes, so it is more robust and reliable. In this paper, we introduce the existing application fields of vehicle bottom information and the research progress of related methods, as well as the multi-modal fusion methods based on bottom information. We also introduced the relevant information of the vehicle bottom information data set in detail to facilitate the research as soon as possible. In addition, new future ideas of multi-modal fusion technology for autonomous driving tasks are proposed to promote the further utilization of vehicle bottom information.

自动检测交通事故是交通监控系统中重要的新兴主题。如今，许多城市交叉路口都配备了与交通管理系统相关的监视摄像机。因此，计算机视觉技术可以是自动事故检测的可行工具。本文提出了一个新的高效框架，用于在交通监视应用的交叉点上进行事故检测。所提出的框架由三个层次步骤组成，包括基于最先进的Yolov4方法的有效和准确的对象检测，基于Kalman滤波器与匈牙利算法进行关联的对象跟踪以及通过轨迹冲突分析进行的事故检测。对象关联应用了新的成本函数，以适应对象跟踪步骤中的遮挡，重叠对象和形状变化。为了检测不同类型的轨迹冲突，包括车辆到车辆，车辆对乘车和车辆对自行车，对物体轨迹进行了分析。使用真实交通视频数据的实验结果显示，该方法在交通监视的实时应用中的可行性。尤其是，轨迹冲突，包括在城市十字路口发生的近乎事故和事故，以低的错误警报率和高检测率检测到。使用从YouTube收集的具有不同照明条件的视频序列评估所提出框架的鲁棒性。该数据集可在以下网址公开获取：http：//github.com/hadi-ghnd/accidentdetection。

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。

Recently, numerous studies have investigated cooperative traffic systems using the communication among vehicle-to-everything (V2X). Unfortunately, when multiple autonomous vehicles are deployed while exposed to communication failure, there might be a conflict of ideal conditions between various autonomous vehicles leading to adversarial situation on the roads. In South Korea, virtual and real-world urban autonomous multi-vehicle races were held in March and November of 2021, respectively. During the competition, multiple vehicles were involved simultaneously, which required maneuvers such as overtaking low-speed vehicles, negotiating intersections, and obeying traffic laws. In this study, we introduce a fully autonomous driving software stack to deploy a competitive driving model, which enabled us to win the urban autonomous multi-vehicle races. We evaluate module-based systems such as navigation, perception, and planning in real and virtual environments. Additionally, an analysis of traffic is performed after collecting multiple vehicle position data over communication to gain additional insight into a multi-agent autonomous driving scenario. Finally, we propose a method for analyzing traffic in order to compare the spatial distribution of multiple autonomous vehicles. We study the similarity distribution between each team's driving log data to determine the impact of competitive autonomous driving on the traffic environment.

In this paper, we propose SceNDD: a scenario-based naturalistic driving dataset that is built upon data collected from an instrumented vehicle in downtown Indianapolis. The data collection was completed in 68 driving sessions with different drivers, where each session lasted about 20--40 minutes. The main goal of creating this dataset is to provide the research community with real driving scenarios that have diverse trajectories and driving behaviors. The dataset contains ego-vehicle's waypoints, velocity, yaw angle, as well as non-ego actor's waypoints, velocity, yaw angle, entry-time, and exit-time. Certain flexibility is provided to users so that actors, sensors, lanes, roads, and obstacles can be added to the existing scenarios. We used a Joint Probabilistic Data Association (JPDA) tracker to detect non-ego vehicles on the road. We present some preliminary results of the proposed dataset and a few applications associated with it. The complete dataset is expected to be released by early 2023.

Many aerial robotic applications require the ability to land on moving platforms, such as delivery trucks and marine research boats. We present a method to autonomously land an Unmanned Aerial Vehicle on a moving vehicle. A visual servoing controller approaches the ground vehicle using velocity commands calculated directly in image space. The control laws generate velocity commands in all three dimensions, eliminating the need for a separate height controller. The method has shown the ability to approach and land on the moving deck in simulation, indoor and outdoor environments, and compared to the other available methods, it has provided the fastest landing approach. Unlike many existing methods for landing on fast-moving platforms, this method does not rely on additional external setups, such as RTK, motion capture system, ground station, offboard processing, or communication with the vehicle, and it requires only the minimal set of hardware and localization sensors. The videos and source codes are also provided.

Accurate speed estimation of road vehicles is important for several reasons. One is speed limit enforcement, which represents a crucial tool in decreasing traffic accidents and fatalities. Compared with other research areas and domains, the number of available datasets for vehicle speed estimation is still very limited. We present a dataset of on-road audio-video recordings of single vehicles passing by a camera at known speeds, maintained stable by the on-board cruise control. The dataset contains thirteen vehicles, selected to be as diverse as possible in terms of manufacturer, production year, engine type, power and transmission, resulting in a total of $ 400 $ annotated audio-video recordings. The dataset is fully available and intended as a public benchmark to facilitate research in audio-video vehicle speed estimation. In addition to the dataset, we propose a cross-validation strategy which can be used in a machine learning model for vehicle speed estimation. Two approaches to training-validation split of the dataset are proposed.

自动化驾驶系统应该有能力监督自己的性能并要求人类驱动程序在必要时接管。在车道保持场景中，车辆未来轨迹的预测是实现安全可靠的驾驶自动化的关键。以前关于车辆轨迹预测的研究主要分为两类，即基于物理和基于机动的方法。本文采用了基于物理的方法，提出了一种基于闭环车辆动力学模型的车道出发预测算法。我们使用扩展卡尔曼滤波器根据感测模块输出来估计当前车辆状态。然后，具有实际车道保持控制法的卡尔曼预测器用于预测未来的转向动作和车辆状态。车道出发评估模块评估车辆角位置的概率分布，并决定是否启动人类收购请求。预测算法能够描述未来车辆姿势的随机特征，其在模拟测试中被预先证明。最后，在15至50 km / h的速度下的道路测试进一步表明，专业方法可以准确地预测车辆未来的轨迹。它可以作为对自动化车道保持功能的通道偏离风险评估的有希望的解决方案。

由于精确定位传感器，人工智能（AI）的安全功能，自动驾驶系统，连接的车辆，高通量计算和边缘计算服务器的技术进步，驾驶安全分析最近经历了前所未有的改进。特别是，深度学习（DL）方法授权音量视频处理，从路边单元（RSU）捕获的大型视频中提取与安全相关的功能。安全指标是调查崩溃和几乎冲突事件的常用措施。但是，这些指标提供了对整个网络级流量管理的有限见解。另一方面，一些安全评估工作致力于处理崩溃报告，并确定与道路几何形状，交通量和天气状况相关的崩溃的空间和时间模式。这种方法仅依靠崩溃报告，而忽略了交通视频的丰富信息，这些信息可以帮助确定违规行为在崩溃中的作用。为了弥合这两个观点，我们定义了一组新的网络级安全指标（NSM），以通过处理RSU摄像机拍摄的图像来评估交通流的总体安全性。我们的分析表明，NSM显示出与崩溃率的显着统计关联。这种方法与简单地概括单个崩溃分析的结果不同，因为所有车辆都有助于计算NSM，而不仅仅是碰撞事件所涉及的NSM。该视角将交通流量视为一个复杂的动态系统，其中某些节点的动作可以通过网络传播并影响其他节点的崩溃风险。我们还提供了附录A中的代孕安全指标（SSM）的全面审查。

Vehicle-to-Everything (V2X) communication has been proposed as a potential solution to improve the robustness and safety of autonomous vehicles by improving coordination and removing the barrier of non-line-of-sight sensing. Cooperative Vehicle Safety (CVS) applications are tightly dependent on the reliability of the underneath data system, which can suffer from loss of information due to the inherent issues of their different components, such as sensors failures or the poor performance of V2X technologies under dense communication channel load. Particularly, information loss affects the target classification module and, subsequently, the safety application performance. To enable reliable and robust CVS systems that mitigate the effect of information loss, we proposed a Context-Aware Target Classification (CA-TC) module coupled with a hybrid learning-based predictive modeling technique for CVS systems. The CA-TC consists of two modules: A Context-Aware Map (CAM), and a Hybrid Gaussian Process (HGP) prediction system. Consequently, the vehicle safety applications use the information from the CA-TC, making them more robust and reliable. The CAM leverages vehicles path history, road geometry, tracking, and prediction; and the HGP is utilized to provide accurate vehicles' trajectory predictions to compensate for data loss (due to communication congestion) or sensor measurements' inaccuracies. Based on offline real-world data, we learn a finite bank of driver models that represent the joint dynamics of the vehicle and the drivers' behavior. We combine offline training and online model updates with on-the-fly forecasting to account for new possible driver behaviors. Finally, our framework is validated using simulation and realistic driving scenarios to confirm its potential in enhancing the robustness and reliability of CVS systems.

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。

随着智能车辆和先进驾驶员援助系统（ADAS）的快速发展，新趋势是人类驾驶员的混合水平将参与运输系统。因此，在这种情况下，司机的必要视觉指导对于防止潜在风险至关重要。为了推进视觉指导系统的发展，我们介绍了一种新的视觉云数据融合方法，从云中集成相机图像和数字双胞胎信息，帮助智能车辆做出更好的决策。绘制目标车辆边界框并在物体检测器的帮助下（在EGO车辆上运行）和位置信息（从云接收）匹配。使用深度图像作为附加特征源获得最佳匹配结果，从工会阈值下面的0.7交叉口下的精度为79.2％。进行了对车道改变预测的案例研究，以表明所提出的数据融合方法的有效性。在案例研究中，提出了一种多层的Perceptron算法，用修改的车道改变预测方法提出。从Unity游戏发动机获得的人型仿真结果表明，在安全性，舒适度和环境可持续性方面，拟议的模型可以显着提高高速公路驾驶性能。