基于通行异常行为的高速公路车辆信用度评价模型与算法研究

为了应对层出不穷的逃费作弊手段,提升高速公路运营管理部门的逃费稽查水平,研究了基于车辆通行大数据的可疑逃费车辆的评价方法。在分析大量通行流水数据的基础上,给出了逃费车辆存在的11种通行异常行为,提出了基于异常行为出现次数的车辆信用度评价模型,实现了对车辆逃费可疑度的量化。利用基于加权平均的多属性效用算法,计算出所有车辆的信用度值。经实际应用验证,正确率达33%,表明模型在较小程度上有一定的合理性和正确性。在此基础上,利用BP神经网络算法对模型进行了改进。研究结果表明,利用改进模型推荐得到的逃费可疑车辆,稽查正确率上升至67%,可大幅提高稽查人员的工作效率,取得了比较满意的效果。

Satellite and high altitude platform-based inter-vehicle communications in vast and desolate areas

In order to solve the problem of inter-vehicle communication （IVC） in vast and desolate areas such as the desert and the Gobi, two vehicle network models are proposed. One is based on satellite communication and the other is based on high altitude platform （ HAP ） communication. The system outline and networking modes of the two models are described. In the satellite communication based model, all the vehicles are equipped with vehicle-bone satellite communication on the move terminals and the communication signals between vehicles are forwarded by satellite. In the high altitude platform-based model, the HAPs are equipped with base station facilities to form aerial base stations, and vehicles can communicate with each other via common terrestrial mobile communication devices. Some key parameters such as path loss, link loss and system capacity are also computed. The analysis shows that both the two models can satisfy the requirement of IVC in the descriptive environment.

APPLICATION OF IMPROVED EMD IN VIBRATION SIGNAL FEATURE EXTRACTION OF VEHICLE

In order to truly obtain the feature extraction of vibration signals under the strong background noise, the analysis and improvement of empirical mode decomposition （EMD） is carried on. After that, the improved EMD is applied to the feature extraction of vehicle vibration signals. First, the multi-autocorrelation method is adopted in each input signal,so the noise is reduced effectively. Then, EMD is used to deal with these signals,and the intrinsic mode functions （IMFs） are obtained. Finally, for obtaining the feature information of these signals, the Hilbert transformation and the spectrum analysis are performed in some IMFs. Theoretical analysis and ex- periment verify the effectiveness of the method, which are valuable reference for the same engineering problems.

Traffic light detection and recognition in intersections based on intelligent vehicle

To ensure revulsive driving of intelligent vehicles at intersections, a method is presented to detect and recognize the traffic lights. First, the stabling siding at intersections is detected by applying Hough transformation. Then, the colors of traffic lights are detected with color space transformation. Finally, self-associative memory is used to recognize the countdown characters of the traffic lights. Test results at 20 real intersections show that the ratio of correct stabling siding recognition reaches up to 90%;and the ratios of recognition of traffic lights and divided characters are 85% and 97%, respectively. The research proves that the method is efficient for the detection of stabling siding and is robust enough to recognize the characters from images with noise and broken edges.

Design and Development of the DTC Induction Motor Drive for Electric Vehicle

The design and development of the traction controller for electric vehicle is introduced, which is based on the induction motor. This drive is developed by using a digital signal processor at low cost and carried out with the module design concept of both software and hardware. Nevertheless, a scheme of the sensorless direct torque control is based on the developed hardware, of which the feasibility is tested by a trial program. Additionally, both the interface function of the drive hardware and the feasibility of its software are proved to be good by the trail programs. A test motor can run about 18?r/min by a variable frequency program with the space vector pulse width modulation technology, of which the torque is visible pulsatile. In this presentation, based on the theoretical approach, the sensorless torque control is to be studied and applied to electric vehicles, of which the quick, smooth and stable torque response is emphasized because it quite benefits improving the drive performance of electric vehicles.

东瀛烈骑 川崎Ninja ZX-10R VS 铃木GSX R1000

在二轮机车的世界里,你永远无法忽视两个名字的存在——川崎、铃木。而它们旗下的两大王牌车种——Ninja(忍者)与GSX R(大R)更是扛起了品牌荣耀的大旗,今天我们就来与两位车友一同来感受这股来自东瀛的速度旋风。"忍者"无敌川崎Ninja ZX-10R的前脸极具设计感,精细的车灯设计,突显出Ninja ZX-10R锐利的眼眸。搭配车头正中的LED行车灯和独特的风挡设计,使Ninja ZX-10R前脸更加别致、激进。Kawasaki独创的条状转速表,LED背光,自动调光,更加清晰简洁,使骑手阅读车辆信息时更加轻松便捷。尾部设计紧凑、干练,倒梯形设计的LED尾灯,在夜间行车时更加耀眼夺目。

Vehicle detection based on information fusion of vehicle symmetrical contour and license plate position

An efficient vehicle detection approach is proposed for traffic surveillance images, which is based on information fusion of vehicle symmetrical contour and license plate position. The vertical symmetry axis of the vehicle contour in an image is. first detected, and then the vertical and the horizontal symmetry axes of the license plate are detected using the symmetry axis of the vehicle contour as a reference. The vehicle location in an image is determined using license plate symmetry axes and the vertical and the horizontal projection maps of the vehicle edge image. A dataset consisting of 450 images （15 classes of vehicles） is used to test the proposed method. The experimental results indicate that compared with the vehicle contour-based, the license plate location-based, the vehicle texture-based and the Gabor feature-based methods, the proposed method is the best with a detection accuracy of 90.7% and an elapsed time of 125 ms.

Critical safe distance design to improve driving safety based on vehicle-to-vehicle communications

A critical safe distance(CSD)model in V2V(vehicle-to-vehicle)communication systems was proposed to primarily enhance driving safety by disseminating warning notifications to vehicles when they approach calculated CSD.By elaborately analyzing the vehicular movement features especially when braking,our CSD definition was introduced and its configuration method was given through dividing radio range into different communication zones.Based on our definition,the needed message propagation delay was also derived which could be used to control the beacon frequency or duration.Next,the detailed CSD expressions were proposed in different mobility scenarios by fully considering the relative movement status between the front and rear vehicles.Numerical results show that our proposed model could provide reasonable CSD under different movement scenarios which eliminates the unnecessary reserved inter-vehicle distance and guarantee the safety at the same time.The compared time-headway model always shows a smaller CSD due to focusing on traffic efficiency whereas the traditional braking model generally outputs a larger CSD because it assumes that the following car drives with a constant speed and did not discuss the scenario when the leading car suddenly stops.Different from these two models,our proposed model could well balances the requirements between driving safety and traffic throughput efficiency by generating a CSD in between the values of the two models in most cases.

Vehicular Beacon Broadcast Scheduling Based on Age of Information(AoI)

Vehicular beaconing plays an important role to facilitate various applications in the paradigm of Internet of Vehicles(IoV). Due to high dynamic and resource limitation in IoV, how to schedule the vehicular beacon broadcast is challenging, especially in dense scenarios. In this paper, we investigate the beacon broadcast scheduling problem considering the Age of Information(AoI). We first propose an algorithm minimizing the expected sum of AoI considering the limited communication resource and vehicle mobility. Then the performance of the proposed algorithm is analyzed. With the proposed algorithm, the optimal solution can be obtained under certain conditions. Extensive simulations are conducted to verify the efficiency, effectiveness and fairness of the proposed solution.

Vehicle actuation based short-term traffic flow prediction model for signalized intersections

Traffic flow prediction is an important component for real-time traffic-adaptive signal control in urban arterial networks.By exploring available detector and signal controller information from neighboring intersections,a dynamic data-driven flow prediction model was developed.The model consists of two prediction components based on the signal states(red or green) for each movement at an upstream intersection.The characteristics of each signal state were carefully examined and the corresponding travel time from the upstream intersection to the approach in question at the downstream intersection was predicted.With an online turning proportion estimation method,along with the predicted travel times,the anticipated vehicle arrivals can be forecasted at the downstream intersection.The model performance was tested at a set of two signalized intersections located in the city of Gainesville,Florida,USA,using the CORSIM microscopic simulation package.Analysis results show that the model agrees well with empirical arrival data measured at 10 s intervals within an acceptable range of 10%-20%,and show a normal distribution.It is reasonably believed that the model has potential applicability for use in truly proactive real-time traffic adaptive signal control systems.

Dynamic signal control for at-grade intersections under preliminary autonomous vehicle environment

Autonomous vehicle technology will transform fundamentally urban traffic systems.To better enhance the coming era of connected and autonomous vehicles,effective control strategies that interact wisely with these intelligent vehicles for signalized at-grade intersections are indispensable.Vehicle-to-infrastructure communication technology offers unprecedented clues to reduce the delay at signalized intersections by innovative information-based control strategies.This paper proposes a new dynamic control strategy for signalized intersections with vehicle-to-signal information.The proposed strategy is called periodic vehicle holding(PVH)strategy while the traffic signal can provide information for the vehicles that are approaching an intersection.Under preliminary autonomous vehicle(PAV)environment,left-turning and through-moving vehicles will be sorted based on different information they receive.The paper shows how PVH reorganizes traffic to increase the capacity of an intersection without causing severe spillback to the upstream intersection.Results show that PVH can reduce the delay by approximately 15%at a signalized intersection under relatively high traffic demand.

Performance of beacon safety message dissemination in Vehicular Ad hoc NETworks (VANETs)

Currently, there is a growing belief that putting an IEEE 802.11-like radio into road vehicles can help the drivers to travel more safely. Message dissemination protocols are primordial for safety vehicular applications. There are two types of safety messages which may be exchanged between vehicles: alarm and beacon. In this paper we investigate the feasibility of deploying safety applications based on beacon message dissemination through extensive simulation study and pay special attention to the safety requirements. Vehicles are supposed to issue these messages periodically to announce to other vehicles their current situation and use received messages for preventing possible unsafe situations. We evaluate the performance of a single-hop dissemination protocol while taking into account the quality of service (QoS) metrics like delivery rate and delay. We realize that reliability is the main concern in beacon message dissemination. Thus, a new metric named effective range is defined which gives us more accurate facility for evaluating QoS in safety applications specifically. Then, in order to improve the performance, the effects of three parameters including vehicle's transmission range, message transmission's interval time and message payload size are studied. Due to special characteristics of the safety applications, we model the relationship between communication-level QoS and application-level QoS and evaluate them for different classes of safety applications. As a conclusion, the current technology of IEEE 802.11 MAC layer has still some challenges for automatic safety applications but it can provide acceptable QoS to driver assistance safety applications.

Adaptive Resource Allocation Algorithm for Internet of Things with Bandwidth Constraint

In order to improve the transmission accuracy and efficiency of sensing and actuating signals in Internet of Things （loT） and ensure the system stability, an adaptive resource allocation algorithm is proposed, which dynami- cally assigns the network bandwidth and priority among components according to their signals＇ frequency domain characteristics. A remote sensed and controlled unmanned ground vehicle （UGV） path tracking test-bed was devel- oped and multiple UGV＇s tracking error signals were measured in the simulation for performance evaluation. Results show that with the same network bandwidth constraints, the proposed algorithm can reduce,, the accumulated and maximum errors of UGV path tracking by over 60% compared with the conventional static algorithm.

Recognition algorithm for turn light of front vehicle

Intelligent vehicle needs the turn light information of front vehicles to make decisions in autonomous navigation. A recognition algorithm was designed to get information of turn light. Approximated center segmentation method was designed to divide the front vehicle image into two parts by using geometry information. The number of remained pixels of vehicle image which was filtered by the morphologic feaatres was got by adaptive threshold method, and it was applied to recognizing the lights flashing. The experimental results show that the algorithm can not only distinguish the two turn lights of vehicle but also recognize the information of them. The algorithm is quiet effective, robust and satisfactory in real-time performance.

An Overview of Internet of Vehicles

The new era of the Internet of Things is driving the evolution of conventional Vehicle Ad-hoc Networks into the lnternet of Vehicles （IoV）. With the rapid development of computation and communication technologies, loV promises huge commercial interest and research value, thereby attracting a large number of companies and researchers. This paper proposes an abstract network model of the IoV, discusses the technologies required to create the IoV, presents different applications based on certain currently existing technologies, provides several open research challenges and describes essential future research in the area of loV.

Delay Analysis and Formulation Inference of Signalized Intersection for Traffic Congestion Conditions

Vehicle delay is an important measure to evaluate the signal timings of signalized intersections.When optimization the signal control parameters, delays of vehicles from all approach directions of an intersection should be considered. Based on the analysis of the vehicle delay on an approach of intersection, directed against the typical condition of a congested intersection-over-saturated condition, the paper has analyzed and inferred the intersection delay dynamic formulation, and has established the relation between intersection delay,the signal timings, vehicle arrival rate and the queue lengths, and that provides useful information for understanding vehicle delay of signalized intersection and for establishing performance index function of signal timing optimization.

Intelligent GPS-Less Speed Detection and Clustering in VANET

Vehicular Ad Hoc Network （VANET） has emerged as a new wireless network for vehicular communications. To provide a flexible and high reliable communication service in VANET, vehicles are clustered to construct many small networks （clusters） so that channel interferences and flooding messages can be limited. This research presents a novel Multi-Resolution Relative Speed Detection （MRSD） model to improve the clustering algorithm in VANET without using Global Positioning System （GPS）. MRSD uses the Moving Average Convergence Divergence （MACD）, the Momentum of Received Signal Strength （MRSS）, and Artificial Neural Networks （ANNs） to estimate the motion state and the relative speed of a vehicle based purely on Received Signal Strength. The proposed MRSD model is accurate with the assistance of the intelligent classification, and incurs less overhead in the cluster head election than that of other algorithms.

Effect of information delay on string stability of platoon of automated vehicles under typical information frameworks

The effect of the information delay, which was caused by thc naturc of the distance sensors and wireless communication systems, on the string stability of platoon of automated vehicles was studied. The longitudinal vehicle dynamics model was built by taking the information delay into consideration, and three typical information frameworks, i.e., leader-predecessor framework （LPF）, multiple-predecessors framework （MPF） and predecessor-successor framework （PSF）, were defined and their related spacing error dynamics models in frequency domain were proposed. The string stability of platoon of automated vehicles was analyzed for the LPF, MPF and PSF, respectively. Meanwhile, the related sufficient string stable conditions were also obtained. The results demonstrate that the string stability can be guaranteed tbr the LPF and PSF with considering the information delay, but the ranges of the control gains of the control laws are smaller than those without considering the information delay. For the MPF, the ＂weak＂ string stability, which can be guaranteed without considering the information delay, cannot be obtained with considering the information delay. The comparative simulations further demonstrate that the LPF shows better string stability, but the PSF shows better string scalable performance.

A contention-based efficient-information perception algorithm (CEiPA) for vehicular ad hoc networks

The problem of information dissemination is researched for vehicular ad-hoc networks （VANET） in this paper, and a contention-based effficient-information perception algorithm （CEiPA） is proposed. The idea of CEiPA is that beacons are delivered over VANET with limited lifetime and efficient information. CEiPA consists of two phases. The first one is initialization phase, during which the count timers Tcyce and Tlocal are set to start beacon delivery while Tcycle is also used to monitor and restart beaconing. The second one is beacon delivery phase. An elaborate distance function is employed to set contention delay for beacons of each vehicle. In this way beacons will be sent in order, which decreases the collision of beacons. Simulation results show that CEiPA enables each beacon to carry more efficient information and spread them over more vehicles with lower network overhead than the periodic beacon scheme. CEiPA is also flexible and scalable because the efficient information threshold it employs is a balance among the freshness of information, network overhead and perception area of a vehicle.

Blind Deconvolution Processing of Loop Inductance Signals for Vehicle Reidentification

Vehicle reidentification is an elegant solution for gathering several pieces of valuable traffic information, e.g., space mean speed, travel time, vehicle tracking, and origin/destination data. Recently, a number of vehiclereidentification algorithms utilizing inductive loop signals have been proposed to take advantage of the widespread availability of loop detectors. These algorithms, however, all directly utilize the raw inductance signals for pattern matching and feature extraction without deconvolution. The raw loop signals are essentially a convolved output between the true vehicle inductance signature and the loop system function, and thus a deconvolution is needed in order to expose the detailed features of individual vehicles. The purpose of this paper is to present a recent investigation on restoration of true inductance signatures by applying a blind deconvolution process. The main advantage of blind deconvolution over the conventional deconvolution is that the computation does not require modeling of a precise loop-detector system function. Experimental results show that the proposed blind deconvolution reveals much more detailed features of inductance signals and, as a result, increases the vehicle reidentification accuracy.