基于概率算法自适应更新背景的运动车辆检测

交通流量检测是智能交通系统中的一个重要研究方向和热点问题,基于视频的车辆检测是交通流量采集分析的核心技术,它为交通流量参数的实时获取提供了可能。为实现在复杂交通视频场景中实时准确检测各类的运动车辆,在研究传统背景差分算法的缺点的工作基础上,提出一个自适应的贝叶斯概率背景检测算法,进而完成了较准确的运动车辆分类检测。实验结果表明该方法具有高效实时的特点,能够较准确地实现复杂交通路面的背景提取和运动车辆的检测,具有良好的鲁棒性。     

Moving vehicle detection,tracking and traffic parameter estimation from a satellite video: a perspective on a smarter city

ABSTRACT

Satellite remote sensing is undergoing a revolution in terms of sensors and temporal coverage. The possibility of acquiring earth’s surface video from space provides an opportunity to investigate broader applications of remote sensing. High-resolution spaceborne videos can become a vital factor in earth observation. Temporally continuous tracking of moving objects, i.e. vehicles, vessels, or even military equipment on Earth’s surface demands high spatial resolution satellite videos. Detecting moving vehicles in the urban areas from space video can lead governments to a new era of traffic monitoring. Satellite videos will find many applications in the field of traffic monitoring. In this article, first, moving vehicles are detected using background subtraction with 94.7% accuracy. Afterwards, vehicles’ trajectories, average velocities, dynamic velocities, and space-time diagram are estimated and trajectories are classified based on velocities. Finally, the total frame traffic density is computed.     

A Parallel Pipeline Based Multiprocessor System For Real-Time Measurement of Road Traffic Parameters

Real-time measurement and analysis of road traffic flow parameters such as volume, speed and queue are increasingly required for traffic control and management. Image processing is considered as an attractive and flexible technique for automatic analysis of road traffic scenes for the measurement and data collection of road traffic parameters. In this paper, the authors describe a novel image processing based approach for analysis of road traffic scenes. Combined background differencing and edge detection techniques are used to detect vehicles and measure various traffic parameters such as vehicle count and the queue length. A RISC based multiprocessor system was designed to enable real-time execution of the authors algorithm. The multiprocessor system has nine processing modules connected in a parallel pipeline fashion. Results shows that the authors multiprocessor system is able to provide measurement of traffic parameters in real-time. Results are presented for real tests of our system by analysing traffic scenes on the highways of Singapore.     

A real-time vehicle detection and a novel vehicle tracking systems for estimating and monitoring traffic flow on highways

Real-time highway traffic monitoring systems play a vital role in road traffic management, planning, and preventing frequent traffic jams, traffic rule violations, and fatal road accidents. These systems rely entirely on online traffic flow info estimated from time-dependent vehicle trajectories. Vehicle trajectories are extracted from vehicle detection and tracking data obtained by processing road-side camera images. General-purpose object detectors including Yolo, SSD, EfficientNet have been utilized extensively for real-time object detection task, but, in principle, Yolo is preferred because it provides a high frame per second (FPS) performance and robust object localization functionality. However, this algorithm’s average vehicle classification accuracy is below 57%, which is insufficient for traffic flow monitoring. This study proposes improving the vehicle classification accuracy of Yolo, and developing a novel bounding box (Bbox)-based vehicle tracking algorithm. For this purpose, a new vehicle dataset is prepared by annotating 7216 images with 123831 object patterns collected from highway videos. Nine machine learning-based classifiers and a CNN-based classifier were selected. Next, the classifiers were trained via the dataset. One out of ten classifiers with the highest accuracy was selected to combine to Yolo. This way, the classification accuracy of the Yolo-based vehicle detector was increased from 57% to 95.45%. Vehicle detector 1 (Yolo) and vehicle detector 2 (Yolo + best classifier), and the Kalman filter-based tracking as vehicle tracker 1 and the Bbox-based tracking as vehicle tracker 2 were applied to the categorical/total vehicle counting tasks on 4 highway videos. The vehicle counting results show that the vehicle counting accuracy of the developed approach (vehicle detector 2 + vehicle tracker 2) was improved by 13.25% and this method performed better than the other 3 vehicle counting systems implemented in this study.     

Real-time vehicle tracking mechanism with license plate recognition from road images

Abstract Information about vehicles on the road is very important for the maintenance of traffic control in current complex traffic condition. Images of vehicles are captured by vehicle-directed cameras. This paper proposes a new vehicle tracking mechanism using license plate recognition technology, which is essential to having information about vehicles on the roads. The proposed method is a real-time processing system using multistep image processing, as well as recognition and tracking processes from 2D and 3D images. The experimental results of real environmental images in recognition and tracking using the proposed method are shown.     

Traffic congestion detection in large-scale scenarios using vehicle-to-vehicle communications

Cooperative vehicular systems are currently being investigated to design innovative ITS (Intelligent Transportation Systems) solutions for road traffic management and safety. Through the wireless exchange of information between vehicles, and between vehicles and infrastructure nodes, cooperative systems can support novel decentralized strategies for ubiquitous and more cost-attractive traffic monitoring. In this context, this paper presents and evaluates CoTEC (COperative Traffic congestion detECtion), a novel cooperative technique based on Vehicle-to-Vehicle (V2V) communications designed to detect road traffic congestion. CoTEC is evaluated under large-scale highway scenarios using iTETRIS, a unique open source simulation platform created to investigate the impact of cooperative vehicular systems. The obtained results demonstrate CoTEC's capability to accurately detect and characterize road traffic congestion conditions under different traffic scenarios and V2V penetration rates. In particular, CoTEC results in congestion detection probabilities higher than 90%. These results are obtained without overloading the cooperative communications channel. In fact, CoTEC reduces the communications overhead needed to detect road traffic congestions compared to related techniques by 88%.     

Design and evaluation of a model predictive vehicle control algorithm for automated driving using a vehicle traffic simulator

This paper describes the design and evaluation of a model predictive control algorithm for automated driving on a motorway using a vehicle traffic simulator. For the development of a highly automated driving control algorithm, motion planning is necessary to satisfy driving condition in various road traffic situations. There are two key issues in motion planning of automated driving vehicles. One of the key issues is how to handle potentially dangerous situations that could occur in order to guarantee the safety of vehicles. The second key issue is how to guarantee the disturbance rejection of the controller under model uncertainties and external disturbances. To improve safety with respect to the future behaviors of subject vehicles, not the current states but rather the predicted behaviors of surrounding vehicles should be considered. The desired driving mode and a safe driving envelope are determined based on the probabilistic prediction of surrounding vehicles behaviors over a finite prediction horizon. To obtain the desired steering angle and longitudinal acceleration for maintaining the subject vehicle in the safe driving envelope during a finite prediction horizon, a motion planning controller is designed based on an model predictive control (MPC) approach. The developed control algorithm has been successfully implemented on a vehicle electronic control unit (ECU). The proposed control algorithm has been evaluated on a real-time vehicle traffic simulator. The throttle, brake, and steering control inputs and the controlled vehicle behavior have been compared to those of manual driving.     

Presenting a traffic management and control system in driver assistance form based on vehicular networks

Vehicular Networks is considered a major step in the field of Intelligent Transportation System (ITS). In this technology, some equipment will be installed on vehicles and special places at roadsides which will enable the wireless communication between vehicles with each other and will provide the communication between the vehicles and roadside equipment. One of the ITS application is Traffic monitoring system. Such system enables accessing traffic videos by traffic monitoring centers to make traffic decision. However, providing traffic video for the vehicles can be appealing. This paper addresses a new application in vehicular networks and ITS which can provide this videos for drivers in a city. Each driver request timely traffic video of a location from a web server and the web server forward this request to a stream management server. This server based on current location of the requester vehicle, its speed and its direction calculates appropriate video chunks for each RSU along vehicle destination. This study aims to present a system which can bring a high accessibility for content and can provide it with an appropriate bandwidth and quality for vehicles. Due to the scalability and bandwidth limitations for its content and streaming, vehicular networks are used in this system.     

Personal driving diary: Automated recognition of driving events from first-person videos

In this paper, we introduce the concept of personal driving diary. A personal driving diary is a multimedia archive of a person’s daily driving experience, describing important driving events of the user with annotated videos. This paper presents an automated system that constructs such multimedia diary by analyzing videos obtained from a vehicle-mounted camera. The proposed system recognizes important interactions between the driving vehicle and the other actors in videos (e.g., accident, overtaking, etc.), and labels them together with its contextual knowledge on the vehicle (e.g., mean velocity) to construct an event log. A decision tree based activity recognizer is designed, detecting driving events of vehicles and pedestrians from the first-person view videos by analyzing their trajectories and spatio-temporal relationships. The constructed diary enables efficient searching and event-based browsing of video clips, which helps the users when retrieving videos of dangerous situations. Our experiment confirms that the proposed system reliably generates driving diaries by annotating the vehicle events learned from training examples.     

融合特征的卫星视频车辆单目标跟踪

目的 卫星视频作为新兴遥感数据,可以提供观测区域高分辨率的空间细节信息与丰富的时序变化信息,为交通监测与特定车辆目标跟踪等应用提供了不同于传统视频视角的信息。相较于传统视频数据,卫星视频中的车辆目标分辨率低、尺度小、包含的信息有限。因此,当目标边界不明、存在部分遮挡或者周边环境表观模糊时,现有的目标跟踪器往往存在严重的目标丢失问题。对此,本文提出一种基于特征融合的卫星视频车辆核相关跟踪方法。方法 对车辆目标使用原始像素和方向梯度直方图（histogram of oriented gradient,HOG）方法提取包含互补判别能力的特征,利用核相关目标跟踪器分别得到具备不变性和判别性的响应图;通过响应图融合的方式结合两种特征的互补信息,得到目标位置;使用响应分布指标（response distribution criterion,RDC）判断当前目标特征的稳定性,决定是否更新跟踪器的表征模型。本文使用的相关滤波方法具有计算量小且运算速度快的特点,具备跟踪多个车辆目标的拓展能力。结果 在8个卫星视频序列上与主流的6种相关滤波跟踪器进行比较,实验数据涵盖光照变化、快速转弯、部分遮挡、阴影干扰、道路颜色变化和相似目标临近等情况,使用准确率曲线和成功率曲线的曲线下面积（area under curve,AUC）对车辆跟踪的精度进行评价。结果表明,本文方法较好地均衡了使用不同特征的基础跟踪器（性能排名第2）的判别能力,准确率曲线AUC提高了2.9%,成功率曲线AUC下降了4.1%,成功跟踪车辆目标,不发生丢失,证明了本文方法的先进性和有效性。结论 本文提出的特征融合的卫星视频车辆核相关跟踪方法,均衡了不同特征提取器的互补信息,较好解决了卫星视频中车辆目标信息不足导致的目标丢失问题,提升了精度。     

城市路网多路口路径动态实时选择方法

为了缓解城市交通拥堵问题,如何充分利用现有的道路资源进行有效的路线导航,一直是学者们关心的热点问题.现有的研究方法包括：优化交通灯信号周期以增大交通流量;对个别车辆的行驶路线进行优化;利用历史交通数据或者通过路网中心和车辆之间的主从式博弈进行路径导航等.然而,这些研究并没有考虑到微观行驶车辆的个性化交通需求以及多车辆彼此之间的路线选择冲突,对于城市路网中交通状况的动态不确定性也没有充分考虑.基于以上问题,提出了城市交通路网动态实时多路口路径选择模型DR²SM（dynamic and real-time route selection model in urban traffic networks）,结合车辆对前方可选路线的偏好以及可选路线的实时交通状况,并利用自适应学习算法SALA（self-adaptive learning algorithm）进行博弈,以使得各行驶车辆的动态路线选择策略达到Nash均衡.     

基于机器学习反馈的车辆自动路况识别

研究车辆路况自动识别的问题,提高识别的准确率和鲁棒性。针对车辆的路况自动识别系统极易受外界环境的影响,传统的基于PCA的路况识别方法在提取路况信息时无法避免恶劣天气等环境的影响,造成最终的识别不准确和鲁棒性不高的问题。为了克服这一难题,提出了基于机器学习的车辆路况自动识别系统。首先采用Haar小波特征提取方法,将受环境影响的路况图像中的有效特征准确提取并降维,然后利用支持向量机选择合适的特征参数,将特征参数输入到AdaBoost分类器中进行分类识别后就完成了最终的车辆路况自动识别,避免了传统方法自动识别受恶劣环境影响的问题。实验证明,这种方法能够有效克服外界环境的影响,准确完成车辆路况的自动识别,并且识别结果具有较好的鲁棒性和满意的效果。     

移动群智感知下的交通流速缺失数据恢复算法

GPS等设备的普及使得基于大规模车辆数据的城市级道路状态评估成为可能,本文研究移动群智感知下的交通流速缺失数据恢复问题.首先,利用探测车收集车辆数据,设计了基于网格密度提取路网的方法;其次,针对GPS数据特点设计一种自适应的路段流速计算方法,得到交通流速矩阵;再次,对交通状况评估时存在的数据缺失情形进行分类,基于数据时...     

RFID，GPS和GIS技术集成在交通智能监管系统中的应用研究*

为实现在城市复杂路网情况下对交通车辆的实时监控,并且能通过一定数量的车辆运行状态来判断道路交通的拥挤状况,采用射频识别技术(RFID)对道路上运行的车辆进行动态识别和数据信息交换;依靠全球定位系统(GPS)技术实时获得目标车辆的位置信息,并通过地理信息系统(GIS)将车辆的运行状况以及路网的交通状况以电子地图形式实时地展现给用户。将GPS、GIS与RFID技术综合应用于城市道路交通管理系统中,在此基础上设计出道路交通车辆的全程监控模型和系统框架。对交通监管的信息化建设具有一定的借鉴意义。     

Motion planning of autonomous vehicles in a non-autonomous vehicle environment without speed lanes

Planning is one of the key problems for autonomous vehicles operating in road scenarios. Present planning algorithms operate with the assumption that traffic is organised in predefined speed lanes, which makes it impossible to allow autonomous vehicles in countries with unorganised traffic. Unorganised traffic is though capable of higher traffic bandwidths when constituting vehicles vary in their speed capabilities and sizes. Diverse vehicles in an unorganised exhibit unique driving behaviours which are analysed in this paper by a simulation study. The aim of the work reported here is to create a planning algorithm for mixed traffic consisting of both autonomous and non-autonomous vehicles without any inter-vehicle communication. The awareness (e.g. vision) of every vehicle is restricted to nearby vehicles only and a straight infinite road is assumed for decision making regarding navigation in the presence of multiple vehicles. Exhibited behaviours include obstacle avoidance, overtaking, giving way for vehicles to overtake from behind, vehicle following, adjusting the lateral lane position and so on. A conflict of plans is a major issue which will almost certainly arise in the absence of inter-vehicle communication. Hence each vehicle needs to continuously track other vehicles and rectify plans whenever a collision seems likely. Further it is observed here that driver aggression plays a vital role in overall traffic dynamics, hence this has also been factored in accordingly. This work is hence a step forward towards achieving autonomous vehicles in unorganised traffic, while similar effort would be required for planning problems such as intersections, mergers, diversions and other modules like localisation.     

基于道路风险评估的城市路网实时路径选择

为了缓解城市交通拥堵、避免交通事故的发生,城市路网的路径选择一直以来是一个热门的研究课题.随着边缘计算和车辆智能终端技术的发展,城市路网中的行驶车辆从自组织网络朝着车联网(Internet of vehicles,IoV)范式过渡,这使得车辆路径选择问题从基于静态历史交通数据的计算向实时交通信息计算转变.在城市路网路径选择问题上,众多学者的研究主要聚焦如何提高出行效率,减少出行时间等.然而这些研究并没有考虑所选路径是否存在风险等问题.基于以上问题,首次构造了一个基于边缘计算技术的道路风险实时评估模型(real-time road risk assessment model based on edge computing, R³A-EC),并提出基于该模型的城市路网实时路径选择方法(real-time route selection method based on risk assessment, R²S-RA). R3A-EC模型利用边缘计算技术的低延迟,高可靠性等特点对城市道路进行实时风险评估,并利用最小风险贝叶斯决策验证道路是否存在风险问...     

Heuristic based evolution for the coordination of autonomous vehicles in the absence of speed lanes

The current state of the art in the planning and coordination of autonomous vehicles is based upon the presence of speed lanes. In a traffic scenario where there is a large diversity between vehicles the removal of speed lanes can generate a significantly higher traffic bandwidth. Vehicle navigation in such unorganized traffic is considered. An evolutionary based trajectory planning technique has the advantages of making driving efficient and safe, however it also has to surpass the hurdle of computational cost. In this paper, we propose a real time genetic algorithm with Bezier curves for trajectory planning. The main contribution is the integration of vehicle following and overtaking behaviour for general traffic as heuristics for the coordination between vehicles. The resultant coordination strategy is fast and near-optimal. As the vehicles move, uncertainties may arise which are constantly adapted to, and may even lead to either the cancellation of an overtaking procedure or the initiation of one. Higher level planning is performed by Dijkstra's algorithm which indicates the route to be followed by the vehicle in a road network. Re-planning is carried out when a road blockage or obstacle is detected. Experimental results confirm the success of the algorithm subject to optimal high and low-level planning, re-planning and overtaking.     

结合状态机和动态目标路径的无人驾驶决策仿真

目的 决策系统是无人驾驶技术的核心研究之一。已有决策系统存在逻辑不合理、计算效率低、应用场景局限等问题,因此提出一种动态环境下无人驾驶路径决策仿真。方法 首先,基于规则模型构建适于无人驾驶决策系统的交通有限状态机;其次,针对交通动态特征,提出基于统计模型的动态目标路径算法计算状态迁移风险;最后,将交通状态机和动态目标路径算法有机结合,设计出一种基于有限状态机的无人驾驶动态目标路径模型,适用于交叉口冲突避免和三车道换道行为。将全速度差连续跟驰模型运用到换道规则中,并基于冲突时间提出动态临界跟车距离。结果 为验证模型的有效性和高效性,对交通环境进行虚拟现实建模,模拟交叉口通行和三车道换道行为,分析文中模型对车流量和换道率的影响。实验结果显示,在交叉口通行时,自主车辆不仅可以检测冲突还可以根据风险评估结果执行安全合理的决策。三车道换道时,自主车辆既可以实现紧急让道,也可以通过执行换道达成自身驾驶期望。通过将实测数据和其他两种方法对比,当车流密度在0.20.5时,本文模型的平均速度最高分别提高32 km/h和22 km/h。当车流密度不超过0.65时,本文模型的换道成功率最高分别提升37%和25%。结论 实验结果说明本文方法不仅可以在动态城区环境下提高决策安全性和正确性,还可以提高车流量饱和度,缓解交通堵塞。