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The papers in this special section focus on electric power trains, with particular emphasis on state-of-the-art research and development and new design concepts. These papers were presented at the IEEE Vehicle Power and Propulsion Conference (VPPC) that was held in Hangzhou, China, in October 2016. [ABSTRACT FROM PUBLISHER]

Cooperative Intelligent Transportation Systems (C-ITS) will change the modes of road safety and traffic management, especially at intersections without traffic lights, namely unsignalized intersections. Existing researches focus on vehicle control within a small area around an unsignalized intersection. This paper expands the control domain to a large area with multiple intersections. In particular, a Multi-intersection Vehicular Cooperative Control (MiVeCC) is proposed to enable cooperation among vehicles in a large area with multiple unsignalized intersections. Firstly, a vehicular end-edge-cloud computing framework is proposed to facilitate end-edge-cloud vertical cooperation and horizontal cooperation among vehicles. Under the framework, a two-stage reinforcement learning is implemented to obtain the optimal policy for vehicle control. To support RL in the proposed framework, a multi-vehicle state representation method and a safety-oriented value representation method are designed. The former structures the collected vehicle information, and the latter evaluates the traffic condition. A multi-intersection simulation platform is developed to evaluate the proposed scheme. Simulation results show that the proposed MiVeCC can improve travel efficiency at multiple intersections by up to 4.59 times without collision compared with benchmark methods. [ABSTRACT FROM AUTHOR]

Modern millimeter-wave automotive radars are employed to keep a safe distance between vehicles and reduce the collision risk when driving. Meanwhile, an on-board radar module is supposed to operate in the line-of-sight condition, which limits its sensing capabilities in intersections with obstructed visibility. Therefore, this paper investigates the scheme with passive reflector, enabling the automotive radar to detect an approaching vehicle in the non-line-of-sight (blind) urban intersection. First, extensive radar measurements of the backscattering power are carried out with the in-house assembled millimeter-wave radar equipment. Next, the measured data is employed to calibrate an accurate analytical model, deduced and described in this paper. Finally, the analytical models are deployed to define the optimal parameters of the radar scheme in the particular geometry of the selected intersection scenario. Specifically, it is found that the optimal angular orientation of the reflector is 43.5°, while the 20 m curvature radius shows better performance compared to a flat reflector. Specifically, the curved convex shape increases scattering power by 20 dB in the shadow region and, thus, improves the detection probability of the vehicle, approaching the blind intersection. [ABSTRACT FROM AUTHOR]

Millimeter-wave vehicle-to-vehicle communication systems with multiple-input multiple-output antenna array have drawn significant attention because it is expected to satisfy the growing bandwidth requirements for the cooperative intelligent transportation systems. However, maintaining high-quality communication links between communicating vehicles without high overhead is a challenging problem due to the high mobility of vehicles. In this paper, we propose a beam management algorithm with spatially distributed antenna subarrays instead of a single co-located antenna array. Unlike existing methods, we use distributed antenna subarrays to exploit the geometric relationship of the beam directions and the relative distance between communicating vehicles. We reduce the beam alignment errors by minimizing the sum of squared errors between the estimated beam direction after the beam training process and the refined beam direction derived from the measured position and velocity data. Additionally, a position data request protocol is devised to reduce the beam refining errors. Numerical results demonstrate that the proposed beam management algorithm successfully reduces the beam alignment errors and it is robust to position and velocity measurement errors. [ABSTRACT FROM AUTHOR]

Combining a global navigation satellite system (GNSS) and microelectromechanical technology-based inertial system (MINS) has become essential in land vehicle navigation systems. In an urban environment, the error of position output by GNSS receiver increases because of the blocking and reflection of the signal by buildings, which affects the positioning accuracy of the integrated system. In order to improve the positioning accuracy under these scenarios, this paper proposed a new robust method based on the estimation of the standard deviation of GNSS positioning error. In our study, a method for estimating the standard deviation ${\boldsymbol{\sigma}}^p$ (or variance) of the position error of GNSS receiver is firstly proposed. Then a robust filtering method combined multi-factor scaling and bias estimation is proposed based on the estimation of ${\boldsymbol{\sigma}}^p$. The simulations and vehicle navigation test show that the proposed method has a robust positioning effect. When ${\boldsymbol{\sigma}}^p$ increases, the position accuracy is closed to the Sage-Husa filtering method with boundary constraint (SH-KF). Simulations also indicate that the proposed method has better robust effect than the traditional Kalman filter and SH-KF when a large bias error of position exists. [ABSTRACT FROM AUTHOR]

Within the scenario of the proliferation of smart vehicles, a novel tendency is to exploit the cooperation between vehicles to create vehicular ad hoc networks (VANETs) using the IEEE 802.11p standard. IEEE 802.11p includes dedicated short-range communications (DSRC) between high-speed vehicles and between the vehicles and the roadside infrastructure in the licensed intelligent transportation system (ITS) band at 5.9 GHz. At such frequencies, fading problems caused by the Doppler effect and/or multipath adversely affect propagation, particularly in dense urban environments. To reduce such propagation impairments, the use of lower frequencies has been proposed in literature, namely the TV white spaces (TVWS) in the ultrahigh-frequency (UHF) band. TVWS is the part of the spectrum licensed to broadcasters that is not occupied at a given time in a given geographical area on a noninterfering/nonprotected basis with regard to primary services. This paper analyses coexistence issues between digital video broadcasting—terrestrial (DVB-T)2 and IEEE 802.11p transmission in the TVWS. The aim of this paper is twofold: to evaluate the protection of DVB-T2 broadcasting services interfered by IEEE 802.11p communications and the impact of adjacent DVB-T2 services on IEEE 802.11p communications in the TVWS. The main outcomes of this paper are the maximum transmission power level and bandwidth configuration of an 802.11p signal in the adjacent channel (i.e., TVWS) of an active DVB-T2 system while protecting the broadcast service, as well as the error rate curves for each allowed mode of IEEE 802.11p in the TVWS to evaluate the performance of VANETs communications coexisting with DVB-T2 regular services in the licensed TV band. [ABSTRACT FROM AUTHOR]

This paper presents the overall framework for high-performance vehicle streams that integrates the transportation and the communication layered architectures together. It shows the process in establishing an infrastructure for high-performance vehicles and the theoretical development and deployment of cooperative adaptive cruise control (CACC) for heterogeneous vehicles that is integrated with lateral control. The system is designed to be fault tolerant and uses the concept of subplatoons. Our framework is based on integrating a transportation layered architecture with the Wireless Access in Vehicular Environment (WAVE) communication protocol. [ABSTRACT FROM PUBLISHER]

In view of the rapid development of edge computing and vehicular network, edge-assisted vehicular crowdsensing system has brought significant benefits to Intelligent Transportation Systems (ITS). To satisfy the requirements of ITS applications, a substantial number of sensed data are generated continuously, which incurs considerable communication and computation delays. In addition, with fast-moving vehicles and the sheer amount of data, data loss becomes an issue during the process of data uploading. To deal with these challenges, this paper aims to propose a cost-and-quality aware data collection scheme for edge-assisted vehicular crowdsensing system, so as to satisfy the accuracy and timeliness requirements of ITS applications without overloading the network. Particularly, we design a functional architecture for edge-assisted vehicular crowdsensing system, where vehicular sensing, data uploading and sensing parameter adjustment interwork to enable ITS applications. Based on the architecture, two effective algorithms are proposed, which are adaptive clustering and online sensing parameter adjustment. Adaptive clustering algorithm can support reliable and timely data uploading by taking into account the cluster stability and the communication reliability during the process of clustering vehicles. Online sensing parameter adjustment algorithm can guide the actions of intelligent vehicles based on the real-time traffic condition, which can achieve a tradeoff between sensing accuracy and communication overhead. In order to evaluate the performance of our scheme, we develop a simulation platform in which all the components in the system are built. By conducting extensive simulation, the superiority of the proposed scheme is validated. [ABSTRACT FROM AUTHOR]

The large-scale application of electric taxis (ETs) in various regions has aroused great interest in developing high-efficiency charging solutions. ETs and charging stations (CSs) can send information to the information management center for real-time information sharing with the technical support of the vehicle-to-grid network. This paper studies the charging problem of intelligent transportation system composed of CSs and networked ETs. Each taxi selects the CS with the lowest charging cost based on the charging price and regional hotspot information. The CS obtains the strategy of taxi and adjusts the charging price to maximize its revenues. We use a multi-leader and multi-follower Stackelberg game to model the interaction between the CSs and ETs. Finally, the hotspot thematic map is generated by using the taxi information of the global positioning system in one day. Simulation results show that the model can effectively reduce the operating cost of taxis and guarantee the revenue of CSs considering the regional hotspot information and reliability coefficient. [ABSTRACT FROM AUTHOR]

The performance evaluation of wireless systems is crucial for the development of future systems with more connected devices. It is essential to have an easy and relevant method for ensuring the wireless communication performance of the devices. We have recently developed a new random line-of-sight (random-LOS) measurement system for evaluating the communication performance of wireless devices, e.g., transceivers and antennas installed on a vehicle. In the measurement system, a plane wave is generated in the test zone emulating the far-field wave transmitted from a radio base station. In this paper we present both numerical simulations and actual experimental results of the random-LOS over-the-air (OTA) measurement setup operating in the 1.5–3 GHz band. The measurement accuracy is determined by the field variations within the test zone, where a smaller variation gives better measurement accuracy. In this paper the achieved accuracy expressed in terms of standard deviation (STD) was evaluated to be approximately 1 dB of the power within a cylindrical test zone of height 0.4 m and diameter 2 m. The active multiple-input multiple-output (MIMO) performance of antenna systems installed on an actual vehicle was measured and evaluated using the presented setup. A comparison to a theoretical zero forcing (ZF) receiver is also presented. [ABSTRACT FROM AUTHOR]

This paper investigates the clutch control problem for automotive transmission systems. This paper focuses on the pressure tracking control for a wet clutch, which has a much shorter stroke and asks for a more precise position and pressure regulation. The system dynamics are highly nonlinear, and the developed model includes unmodeled dynamics and parameter uncertainties. All these factors will influence the closed-loop system performance if not considered carefully during the control design, which makes the control synthesis for clutch systems challenging. Compared with the existing sliding-mode control (SMC) method, an improved method based on nonlinear sliding mode is presented in this paper to further improve the convergence rate and tracking precision of the closed-loop clutch system. This controller ensures that the pressure tracking error not only reaches the sliding manifold in finite time but converges to the equilibrium point in finite time as well. Moreover, considering the chattering phenomenon caused by high switching gains, a composite terminal SMC (TSMC) method based on uncertainty observer is proposed to reduce chattering. Using an extended state observer (ESO) technique, an uncertainty observer is designed for estimating the model uncertainties of pressure dynamics, pressure-reducing-valve flow dynamics, and estimation error of piston displacement. Then, a feedforward compensation term is combined with the terminal-sliding-mode feedback control. Thus, the composite terminal-sliding-mode controller may take smaller control gains for the whole pressure tracking procedure without sacrificing uncertainty compensation performance. Experimental results based on an xPC Target platform of the clutch system are provided to show the superiority of the proposed methods. [ABSTRACT FROM AUTHOR]

The downlink power allocation in a two-tier cellular network that consists of a macrocell network underlaid by multiple femtocell networks is addressed in this paper. This paper aims to maximize the transmission capacity of the femtocell networks while guaranteeing that the interference experienced at the macro base station does not exceed an interference constraint. We formulate a Bayesian Stackelberg game to model and analyze behaviors of macrocell and femtocell base stations (MBS and FBSs, respectively). In this game, the MBS is the leader, whereas the FBSs are the followers. The channel information between an FBS and its associated femtocell user is private information and is considered the type of the follower. The leader issues the price of interference charged to the followers first to maximize its own profit. Based on the price, the followers decide the strategies to maximize their payoffs defined as the difference between the transmission capacity and the cost of interference (CoI) paid to the leader. Using backward induction, we first analyze the follower game. The existence and uniqueness of the Bayesian Nash equilibrium (BNE) are examined, and the methods to obtain the BNE for a symmetric case are provided. Then, the leader game is analyzed. Finally, the numerical analysis is provided. [ABSTRACT FROM AUTHOR]

Recent research in intelligent transportation system focus on different possible use cases. Among them are the payment applications with cooperative intelligent transport system (C-ITS) equipment. In this paper, we deal about the tolling application performed with the C-ITS equipment. These latter use the ITS-G5 technology, the Roadside Unit (RSU) and the on-board unit (OBU) with features specified by the standardization Institute ETSI. To perform this service, we highlight two mains requirements: how to have a reliable geolocation of the vehicle when it crosses the tollgate and how to secure the communication during the transaction process. In this paper, we will address the methods that is propose to solve the issue of geolocation. The proposed method takes advantage of the communications data. It uses the principle of Differential - GPS mixed with Kalman filtering and extended Kalman filtering applied to the GPS and CAN bus measurements received by the RSU from the connected vehicles. The kinematic of the vehicle is taken into account to choose the suitable system model. Here as novelties, we discuss about the whole procedure to perform the geolocation process during the tolling service and we have results when we apply the dynamic model according to the real behavior of the vehicle when approaching and crossing the toll station. [ABSTRACT FROM AUTHOR]

Traffic volume measurement is critical in vehicular networks. Existing research on traffic volume measurement mainly focuses on single-point traffic statistics. In this paper, we switch our view from single-point to multi-point and study the important problem of privacy-preserving multi-point traffic volume measurement in vehicular cyber-physical systems (VCPSs), which complements the state of the art. While embracing automatic traffic data collection, which the VCPS provides through vehicle-to-infrastructure communications, we also need to accept the accompanying challenges: First, the privacy of all participating vehicles should be preserved as an inherent requirement of a VCPS; second, the measurement scheme should be efficient enough to fit today's large-scale vehicular networks. In this paper, we start from a novel scheme that measures traffic volume between two arbitrary points (locations) through variable-length bit array masking. Then, we extend the idea of variable-length bit array masking to address the more challenging problem of three-point traffic measurement and present a general framework to measure traffic among three or more locations. We also perform extensive simulations to demonstrate the superior performance, applicability, and scalability of our schemes. [ABSTRACT FROM PUBLISHER]

We are seeing a growth in the number of connected vehicles in Vehicular Ad-hoc Networks (VANETs) to achieve the goal of Intelligent Transportation System (ITS). This is leading to a connected vehicular network scenario with vehicles continuously broadcasting data to other vehicles on the road and the roadside network infrastructure. The presence of a large number of communicating vehicles greatly increases the number and types of possible anomalies in the network. Existing works provide solutions addressing specific anomalies in the network only. However, since there can be a multitude of anomalies possible in the network, there is a need for better anomaly detection frameworks that can address this unprecedented scenario. In this paper, we propose an anomaly detection framework for VANETs based on deep neural networks (DNNs) using a sequence reconstruction and thresholding algorithm. In this framework, the DNN architectures are deployed on the roadside units (RSUs) which receive the broadcast vehicular data and run anomaly detection tasks to classify a particular message sequence as anomalous or genuine. Multiple DNN architectures are implemented in this experiment and their performance is compared using key evaluation metrics. Performance comparison of the proposed framework is also drawn against the prior work in this area. Our best performing deep learning-based scheme detects anomalous sequences with an accuracy of 98%, a great improvement over the set benchmark. [ABSTRACT FROM AUTHOR]

To alleviate possible traffic congestion during rush hours, an improved traffic rerouting strategy designed for (or interplayed with) the vehicular ad hoc networks (VANETs) is proposed in this paper. The proposed strategy considers not only the real-time traffic information but also the maximization of road utilization to reach an optimized solution. Furthermore, an alternative path selection is incorporated so that an efficient rerouting path is determined according to the destination of the vehicle, the density and the capacity of the road, and the speed. Via simulations, we can successfully show that our proposed strategy significantly outperforms the closely related strategies in the literature in terms of the travel time, CO $_{2}$ emission, and fuel consumption, in particular, when heavy traffic is involved. Specifically, our proposed strategy can gain up to 20% of reduction in the average travel time in a realistic scenario to be observed later. Additionally, our proposed strategy gains 19% at least of improvement in the case with different road networks and 28.6% at least of improvement in the case with different percentages of reduction of traffic lights as compared to the closely related strategies as shown in the Appendices given in the supplementary downloadable material. [ABSTRACT FROM AUTHOR]

Economical and environmentally friendly geocast (EEFG) uses traffic signals to communicate with approaching vehicles. The communication can be signal-to-vehicle (TLS2V) and vehicle-to-vehicle (V2V). Based on the information sent, the vehicle receiving the message adapts its speed to a recommended speed (SR ) , which helps the vehicle reduce fuel consumption and emissions. Our previous paper entitled “Optimization of Fuel Cost and Emissions Using V2V Communications” develops a model to determine the optimum SR . It also proposes heuristic expressions to compute the optimum or near-optimum SR. [ABSTRACT FROM PUBLISHER]

When it comes to content access using intervehicle communications (IVC), data will mostly flow through roadside units (RSUs) that deployed in our cities. Unfortunately, the RSU coverage is expected to be rather scattered. Instead of relying on RSUs only, this paper investigates the possibility of exploiting parked vehicles to extend the RSU service coverage. Our approach leverages optimization models aiming at maximizing the freshness of content that downloaders retrieve, the efficiency in the utilization of radio resources, and the fairness in exploiting the energy resources of parked vehicles. The latter is constrained to not excessively drain parked vehicle batteries. Our approach provides an estimate of the system performance, even in those cases where users may only be willing to lease a limited amount of their battery capacity to extend RSU coverage. Our optimization-based results are validated by comparing them against ns-3 simulations. Performance evaluation highlights that the use of parked vehicles enhances the efficiency of the content downloading process by 25%–35% and can offload more than half the data traffic from RSUs with respect to the case where only moving cars are used as relays. Such gains in performance come at a small cost in terms of battery utilization for the parked vehicles, and they are magnified when a backbone of parked vehicles can be formed. [ABSTRACT FROM AUTHOR]

In the last years, more and more research activities have focused on the application of Virtual Reality (VR) and related cutting-edge immersive technologies like Augmented Reality (AR) and Mixed Reality (MR) to vehicular systems for supporting the design of novel Human-Machine Interaction techniques, simulating the functioning of newly developed components, recreating hazardous test environments, establishing trust in Connected and Autonomous Vehicles (CAVs), etc. The benefits brought by immersive environments for supporting simulations in the field of vehicular technology have already been recognized by many companies and academic institutions worldwide, as confirmed by the huge investments and a large number of publications. As we approach the time in which immersive simulations will become fundamental for a wide range of applications in the field of vehicular technology, the goal of this Special Section was to attract high-quality submissions reporting state-of-the-art research activities targeted to next-generation vehicles and related infrastructures. The Special Section received 12 submissions, which were prescreened and reviewed by experts in the field. Four papers were ultimately accepted. [ABSTRACT FROM AUTHOR]

The fast development of intelligent transportation has paved the way for innovative techniques for highways, and an entirely new driving pattern of highway vehicular platooning might offer a solution to the persistent problem of road congestion, travel comfort, and road safety. In this vehicular platooning system, a platoon head vehicle provides platoon service to its user vehicles. However, some badly behaved platoon head vehicles may put the platoon in danger, which makes it crucial for user vehicles to distinguish and avoid them. In this paper, we propose a reliable trust-based platoon service recommendation scheme, which is called REPLACE, to help the user vehicles avoid choosing badly behaved platoon head vehicles. Specifically, at the core of REPLACE, a reputation system is designed for the platoon head vehicles by collecting and modeling their user vehicle's feedback. Then, an iterative filtering algorithm is designed to deal with the untruthful feedback from user vehicles. A detailed security analysis is given to show that our proposed REPLACE scheme is secure and robust against badmouth, ballot-stuffing, newcomers, and on–off attacks that exist in vehicular ad hoc networks (VANETs). In addition, we conduct extensive experiments to demonstrate the correctness, accuracy, and robustness of our proposed scheme. [ABSTRACT FROM PUBLISHER]

In this paper, an antenna synthesis process is investigated for Intelligent Transportation System (ITS) application. The wireless communication between Road Side Units (RSUs) and On Board Units (OBUs) located in the vehicles enables smart mobility. However, to support high speed vehicles and high data rates, a wider coverage area by the RSU antennas projected on the highway is required. This is defined by the radiation patterns of the RSU antennas. On a multi-lane highway, interference from the adjacent lanes is a critical issue which determines the communication reliability in terms of Single-to-Interference plus Noise Ratio (SINR). Hence, in this paper, an interference-aware antenna synthesis process is proposed and a planar array is optimized based on the objective function of obtaining a wide coverage area in terms of SINR distribution for a given modulation scheme. The proposed antenna array enhances the reliable communication area and provides improved SINR distribution across the lane for misaligned vehicles from the center of the lane. The results indicate that the proposed antenna array performs better than the existing designs in terms of communication reliability. [ABSTRACT FROM AUTHOR]

Intelligent transport systems (ITS) rely upon the connectivity, cooperation and automation of vehicles aimed at the improvement of safety and efficiency of the transport system. Connectivity, which is a key component for the practical implementation of vehicular light communications (VeLC) systems in ITS, must be carefully studied prior to design and implementation. In this paper, we carry out a performance evaluation study on the use of different vehicle taillights (TLs) as the transmitters in a VeLC system. We show that, the transmission coverage field of view and the link span depend on TLs illumination patterns and the transmit power levels, respectively, which fail to meet the typical communication distances in vehicular environments. This paper proposes an infrared-based VeLC system to meet the transmission range in daytimes under Sunlight noise. We show that, at the forward error correction bit error rate limit of 3.8 × 10−3, the communication distances of the proposed link are 63, 72, and > 89 m compared with 4.5, 5.4 and 6.3 m for BMW's vehicle TL at data rates of 10, 6, and 2 Mbps, respectively. [ABSTRACT FROM AUTHOR]

Although vehicles are the most commonly used means of transportation in our daily lives, their computational power and memory resources are often ignored. The new paradigm, Internet of Vehicles, has turned on-road vehicles into intelligent service providers, making full use of on-board processing and storage capabilities. With the advent of 6 G technology, e.g., millimeter wave and terahertz, fast and massive data transfers become possible between moving objects. In this paper, we consider a scenario where vehicles act as mobile edge caching nodes and form a local cloud, to provide popular contents for nearby users via 6 G communication. To maximize the efficiency of content allocation under the limitation of short communication range, we first predict the driving trajectories of vehicles with a Markov chain model and estimate the popularity level of contents in each urban area. Next, we design a scoring system based on the above predictions to compare users’ satisfaction and computational efficiency of different algorithms. Finally, we find a subset of vehicles with appropriate content to serve the demand in the area. The simulation experiments demonstrate that the proposed scheme outperforms the conventional methods by providing a cost-efficient content allocation solution with lower delay. [ABSTRACT FROM AUTHOR]

Intelligent transportation is an emerging technology that integrates advanced sensors, network communication, data processing, and automatic control technologies to provide great convenience for our daily lives. With the increasing popularity of intelligent transportation, its security issues have also attracted much attention. SQL injection attack is one of the most common attacks in the intelligent transportation system. It has characteristics of various types, fast mutations, hidden attacks, etc., and leads to great harm. Most of the current SQL detection methods are based on manually defined features. The detection results are heavily dependent on the accuracy of feature extraction, so it cannot cope with the increasingly complex SQL injection attacks in the intelligent transportation system. In order to solve this problem, this paper proposes a long short-term memory based SQL injection attack detection method, which can automatically learn the effective representation of data, and has a strong advantage to confront with complex high-dimensional massive data. In addition, this paper proposes an injection sample generation method based on data transmission channel from the perspective of penetration. This method can formally model SQL injection attack and generate valid positive samples. It can effectively solve the over-fitting problem caused by insufficient positive samples. The experimental results show that the proposed method improves the accuracy of the SQL injection attack detection and reduces the false positive rate, which is better than several related classical machine learning algorithms and commonly used deep learning algorithms. [ABSTRACT FROM AUTHOR]

As Vehicular Ad hoc Network (VANET) features random topology and accommodates freely connected nodes, it is important that the cooperation among the nodes exists. This paper proposes a trust model called Implicit Web of Trust in VANET (IWOT-V) to reason out the trustworthiness of vehicles. Such that untrusted nodes can be identified and avoided when we make a decision regarding whom to follow or cooperate with. Furthermore, the performance of Cooperative Intelligent Transport System (C-ITS) applications improves. The idea of IWOT-V is mainly inspired by web page ranking algorithms such as PageRank. Although there does not exist explicit link structure in VANET because of random topology and dynamic connections, social trust relationship among vehicles exists and an implicit web of trust can be derived. To accomplish the derivation, two algorithms are presented, i.e., BayesTrust and VehicleRank. They are responsible for deriving the local and global trust relationships, respectively. The simulation results show that IWOT-V can accurately identify trusted and untrusted nodes if enough local trust information is collected. The performance of IWOT-V affected by five threat models is demonstrated, and the related discussions are also given. [ABSTRACT FROM AUTHOR]

A method to locate the position of the target vehicle with the aid of infrared signal-direction discrimination for short-range vehicle-to-vehicle communications is proposed in this paper. With the use of two one-dimensional (1-D) signal-direction discriminators mounted on a vehicle with a lateral separation between them to measure the coming directions of the signal emitted from another target vehicle, the position of the target vehicle relative to this detecting vehicle can be located by triangulation. The aforementioned 1-D signal-direction discriminator is composed of two planar receiving modules forming a specific geometric structure, which leads to different responsivities of these individual receiving modules for a signal incident from a definite direction. The coming direction of the signal is determined by comparing the signal strengths received by these individual receiving modules. With a proposed hardware and software implementation, the position of the target vehicle can be accurately determined within a longitudinal range of 50 m. Owing to the restriction of the width of the vehicle, the separation of the signal-direction discriminators mounted on the vehicle cannot exceed 1.8 m. This leads to the consequence that in the remote region any small inaccuracy of the measured signal directions may cause very large position error. However, our measured results reveal a specific characteristic of the distribution of the measured positions for the target vehicle at a definite location. This allows us to design deliberate statistical algorithms to accurately extract the position of the target vehicle in the remote region. Using the simplest statistical algorithm, we are able to locate the position of the target vehicle with acceptable accuracy within a longitudinal range of 80 m. In this paper, we show the feasibility and applicability of this method. Furthermore, improvement is foreseeable with more deliberate design of hardware and software. [ABSTRACT FROM AUTHOR]

The highway vehicular ad hoc networks, where vehicles are wirelessly interconnected, rely on the multihop transmissions for end-to-end communications. This, however, is severely challenged by the unreliable wireless connections, signal attenuation, and channel contentions in the dynamic vehicular environment. To overcome the network dynamics, selecting appropriate relays for end-to-end connections is important. Different from the previous efforts (e.g., clustering and cooperative downloading), this paper explores the existence of stable vehicles and propose building a stable multihop transmission backbone network in the highway vehicular ad hoc network. This paper is composed of three parts. First, by analyzing the real-world vehicle traffic traces, we observe that the large-size vehicles, e.g., trucks, are typically stable with low variations of mobility and stable channel condition of low signal attenuation; this makes their interconnections stable in both connection time and transmission rate. Second, by exploring the stable vehicles, we propose a distributed protocol to build a multihop backbone link for end-to-end transmissions, accordingly forming a two-tier network architecture in highway vehicular ad hoc networks. Finally, to show the resulting data performance, we develop a queueing analysis model to evaluate the end-to-end transmission delay and throughput. Using extensive simulations, we show that the proposed transmission backbone can significantly improve the reliability of multihop data transmissions with higher throughput, less transmission interruptions, and end-to-end delay. [ABSTRACT FROM AUTHOR]

Collision avoidance and platooning applications require vehicle localization at cm-level accuracy and at least 50 Hz rate for full autonomy. The RADAR/LIDAR and camera based methods currently used for vehicle localization do not satisfy these requirements, necessitating complementary technologies. Visible light positioning (VLP) is a highly suitable complementary technology due to its high accuracy and high rate, exploiting the line-of-sight propagation feature of the visible light communication (VLC) signals from LED head/tail lights. However, existing vehicular VLP algorithms impose restrictive requirements, e.g., use of high-bandwidth circuits, road-side lights and certain VLC modulation strategies, and work for limited relative vehicle orientations, thus, are not feasible for general use. This paper proposes a VLC-based vehicle localization method that eliminates these restrictive requirements by a novel VLC receiver design and associated vehicular VLP algorithm. The VLC receiver, named QRX, is low-cost/size, and enables high-rate VLC and high-accuracy angle-of-arrival (AoA) measurement, simultaneously, via the usage of a quadrant photodiode. The VLP algorithm estimates the positions of two head/tail light VLC transmitters (TX) on a neighbouring vehicle by using AoA measurements from two QRXs for localization. The algorithm is theoretically analyzed by deriving its Cramer-Rao lower bound on positioning accuracy, and simulated localization performance is evaluated under realistic platooning and collision avoidance scenarios. Results demonstrate that the proposed method performs at cm-level accuracy and up to 250 Hz rate within a 10 m range under realistic harsh road and channel conditions, demonstrating its eligibility for collision avoidance and safe platooning. [ABSTRACT FROM AUTHOR]

Vehicle-to-vehicle (V2V) communication has been designed to afford improvements in the traffic congestion and traffic accidents by directly exchanging information between nearby vehicles. However, V2V communication may have difficulty providing service reliability due to interference between V2V links and vehicle-to-cellular user equipment links. In this paper, we consider the transmit power control algorithm to minimize interference among cellular users and vehicles in the context of V2V communication underlaid uplink cellular networks. First, we formulate the problem, which is an NP-hard combinatorial optimization problem with linear constraints. Addressing this problem with traditional optimization methods is ineffective; therefore, we design and train a deep neural network to address this optimization problem. Computational complexity analyses and simulation results reveal that the proposed algorithm outperforms weighted minimum mean squared error (WMMSE), fixed transmit power, and Dinkelbach's methods, and achieves near-global optimum with lower computation complexity than the exhaustive search (ES). [ABSTRACT FROM AUTHOR]

In the environment of intelligent and connected vehicles, the information sharing level among vehicles and infrastructure get comprehensively improved. Aiming at the cooperative merging control at highway ramp, we present a two-stage hybrid cooperative control framework considering communication time delays including centralized decision-making for grouping with sequencing and distributed consensus control. The centralized controller is based on a grouping strategy and a cost function for minimizing total travel time and delays and determining the optimal vehicle passage order. Then we model the merging vehicles formation as a multi-agent system, which also could be viewed as a distributed controlling system. Combining with the multi-agent consensus method, it could achieve the desired inter-vehicle distance and speed. The proof tool is the Lyapunov-Krasovskii stability theorem in terms of convergence and formation stability. Finally, we design three simulation scenarios through varying the number of vehicles. Meanwhile, in order to verify the resistance of the algorithm to communication time delays, three conditions with respect to no time delays, heterogeneous time delays, and homogeneous time delays are considered. Simulation result verifies the advantage of the proposed method compared with the Hamilton control method. The efficiency of the proposed method is impervious in three different types of communication time delays and has approximately 30%, 25%, 15% improvement over that of the Hamiltonian method, respectively. Furthermore, the adaptation and effectiveness of the proposed method is demonstrated under different volumes of traffic flow and communication delays. [ABSTRACT FROM AUTHOR]

This paper studies user-specific clustering for a downlink cloud radio access network (C-RAN), where a central unit (CU), connected to all base stations (BSs) via limited-capacity backhaul links, coordinates the BSs to form cooperative clusters for every user. By taking into account the training overhead for channel estimation in C-RAN, we design the clustering scheme aimed at maximizing the average net throughput of the network subject to the constraint on backhaul capacity, where a hybrid coordinated multipoint (CoMP) transmission mode is considered. The proposed clustering scheme can be operated in a semi-dynamic manner merely based on large-scale channel information, has low computational complexity, and performs close to the optimal scheme found by exhaustive searching. Under two special cases where the backhaul capacity is very stringent and unlimited, the proposed scheme is then tailored for pure coordinated beamforming (CB) mode and pure joint transmission (JT) mode to further reduce the clustering complexity. Simulation results show that the proposed semi-dynamic clustering schemes are superior to the dynamic clustering scheme due to the reduction of required training overhead. [ABSTRACT FROM AUTHOR]

How to support multimedia services for people on the road is a pressing issue. Relying on the vehicle-to-infrastructure (V2I) communications, the limited wireless resources and vehicle sojourn time make it quite challenging to schedule the transmissions of multiple vehicles to ensure high efficiency and quality. In this paper, the scheduling of multimedia transmissions over drive-thru Internet is investigated. A utility model is devised to map the throughput to user's satisfaction level. The objective of the scheduling problem is to maximize the total utility. Then, the optimization problem is formulated as a finite-state decision problem with the assumption that future arrival information is known, and it is solved by a searching algorithm as the benchmark. To obtain a real-time solution, a practical heuristic algorithm based on the concept of utility potential is devised. We further implemented the solution and conducted extensive simulations using NS-3, and the simulation results show that the proposed heuristic algorithm can outperform the state-of-the-art one; therefore, it can effectively make scheduling decisions to achieve both higher utility and efficiency and better fairness. [ABSTRACT FROM AUTHOR]

Automotive collision avoidance (CA) systems help drivers to avoid collisions through autonomous interventions by braking or steering. If the decision to intervene is made too early, the intervention can become a nuisance to the driver, and if the decision is made too late, the safety benefits of the intervention will be reduced. The decision to intervene is commonly based on a threat function. The dimensionality of the input state space for the threat function is, in general, very large, making exhaustive evaluation in real vehicles intractable. This paper presents a method for efficient estimation of a conservative bound on CA system performance, i.e., the worst-case performance. Closed-form expressions are derived for the worst-case performance, in terms of early or unnecessary interventions, with regard to longitudinal or lateral prediction and measurement errors. In addition, we derive closed-form expressions for robust avoidance scenarios, in which no unnecessary intervention will occur. For a system example, numerical results show how decision timing and robustness depend on scenario and system parameters. The method can be used for defining system requirements, system verification, system tuning, or system sensitivity analysis with regard to scenario variations and sensor measurement errors. [ABSTRACT FROM AUTHOR]

In this paper, we propose a heterogeneous framework to deliver smart grid (SG) data cost effectively. The data generated by distributed SG loads, and generation units should be delivered to the utility control center (UCC) within the tolerated delay, which is crucial for SG applications. To this end, a heterogeneous communication framework is proposed, where the cellular network (CN) provides ubiquitous yet expensive data transmission, and vehicle-assisted device-to-device (D2D) communications are leveraged to offload the CN by delivering the delay-tolerant SG data in a store–carry–forward fashion with low cost. To improve the offloading and cost performance of the proposed framework, we put effort in the following aspects: 1) optimal forwarding schemes to optimally select vehicles to carry and forward the data; and 2) mode selection and dynamic resource allocation to maximize the amount of data delivered by D2D communications, reduce the cost of SG data delivery, and guarantee the fairness among SG users. Simulation results are given to validate proposed approaches and demonstrate that the proposed framework is efficient in saving cost for the utility and offloading the CN. [ABSTRACT FROM AUTHOR]

Smart scheduling can be used to reduce infrastructure-to-vehicle energy costs in delay-tolerant vehicular networks. In this paper, we show that, by combining this with vehicle-to-vehicle (V2V) forwarding, downlink (DL) traffic schedules can be generated, whose energy costs are lower than that in the single-hop case. This is accomplished by having the roadside units (RSUs) dynamically forward packets through vehicles, which are in energy-favorable locations. This paper considers both constant bit rate (CBR) and variable bit rate (VBR) air-interface options. We first derive offline schedulers for the DL RSU energy usage when V2V forwarding is added to RSU-to-vehicle communication. Both in-channel and off-channel forwarding cases are considered. The CBR and VBR cases are obtained using integer linear programming (ILP) and time-expanded graph (TEG) formulations, respectively. These schedulers provide lower bounds on energy performance and are used for comparisons with a variety of proposed online scheduling algorithms. The first algorithm is based on a greedy local optimization (GLOA). A version of this algorithm, which uses a minimum-cost flow graph (MCFG) scheduler, is also introduced. A more sophisticated algorithm is then proposed, which is based on a finite-window group optimization (FWGO). Results from various experiments show that the proposed algorithms can generate traffic schedules with much improved DL energy requirements compared with the case where V2V packet forwarding is not used. The performance improvements are particularly strong when under heavy loading conditions and when the variation in vehicle communication requirements or vehicle speed is high. Results that compare the proposed algorithms with conventional nonenergy-aware schedulers are also presented. [ABSTRACT FROM PUBLISHER]

In this paper, a resource allocation problem is formulated to maximize the throughput of vehicular user equipments (VUEs) in both licensed, and unlicensed frequency bands under constraints of reliability, and latency for vehicular communications as well as the Quality of Service (QoS) for WiFi network based on a network system with coexisting VUEs, and WiFi user equipments (WUEs). In the system, the VUEs are able to access to the unlicensed frequency bands, and the interference among the VUEs, and WUEs are mitigated by the listen-before-talk (LBT) scheme or the duty cycle scheme. Due to the mixed integer nonlinear programming (MINLP) objective in the optimization problem, the problem is hard to be solved directly. Instead, we solve the problem by two steps. The number of VUEs offloaded to unlicensed frequency bands as well as the time factor for duty cycle scheme is firstly determined, and then, the optimization problem is converted into a convex optimization problem, which is solved by the proposed Lagrange Duality Method (LDM). Numerical results show the efficiency of our proposed application scenario compared to case with only LTE system or only WiFi system. Moreover, compared to traditional Greedy algorithm, the proposed algorithm performs better in terms of throughput with a guaranteed QoS of WUEs. [ABSTRACT FROM AUTHOR]

Rear-end collision prediction has gained an increasing attention for safety improvement in smart cities. It is urgent to design efficient warning strategies for rear-end collisions which is one of the main causes of traffic accidents. The existing researches have been conducted to predict the collisions. Learning-based methods are proposed to solve this complicated issue, which the traditional methods are difficult to solve. However, due to some limitations in terms of the feature extraction and prediction performance, back-propagation learning methods are facing challenge. In this paper, we proposed a novel Rear-end Collision Prediction Mechanism with deep learning method (RCPM), in which a convolutional neural network model is established. In RCPM, the dataset is smoothed and expanded based on genetic theory to alleviate the class imbalance problem. The preprocessed dataset is divided into training and testing sets as the input to train our convolutional neural network model. The experimental results show that compared with the Honda, Berkeley and multi-layer perception neural-network-based algorithms, RCPM effectively improves performance to predict rear-end collisions. [ABSTRACT FROM AUTHOR]

Timely and reliable information sharing among autonomous vehicles (AVs) provides a promising approach for reducing traffic congestion and improving traffic efficiency in future intelligent transportation systems. In this paper, we consider millimeter-wave (mmWave) based multi-hop vehicle-to-vehicle (V2V) communications to facilitate ultra-reliable low-latency information sharing among AVs. We propose a novel framework for performance analysis and design of relay selection schemes in mmWave multi-hop V2V communications, while taking into account the mmWave signal propagation characteristics, road topology, and traffic conditions. In particular, considering the minimum tracking distance requirement of road traffic, the headway, i.e., the distance between adjacent AVs, is modeled as shifted-exponential distribution. Moreover, we model the communication path losses using the Manhattan distance metric in the taxicab geometry, which can more accurately capture the characteristics of mmWave signal propagation in urban grid roads than conventional Euclidean distance geometry. Based on the proposed model, we investigate the latency and reliability of mmWave multi-hop V2V communications for three widely adopted relay selection schemes, i.e., random with forward progress (RFP), most forward with fixed radius (MFR), and nearest with forward progress (NFP), respectively. Furthermore, we propose a novel relay selection scheme for joint optimization of the single-hop forward progress (FP) and single-hop latency according to the AVs’ instantaneous locations and an estimate of the residual multi-hop latency. Simulation results show that, by balancing the current single-hop latency and the residual multi-hop latency for the multi-hop V2V network, the proposed relay selection scheme significantly outperforms the MFR, NFP and RFP in both multi-hop transmission latency and reliability of mmWave V2V communications. [ABSTRACT FROM AUTHOR]

In recent years, intelligent transportation systems are a vital part of the development of smart cities. In intelligent transportation systems, it is necessary for each vehicle to know the states of all other running vehicles within a range, depends on the road speed limit. The states include location, travelling direction, speed, and possibly other useful information. Knowing the states of surrounding vehicles is critical in high-risk locations, such as traffic intersections and roundabouts. The development of 5G and its technologies, such as massive multiple-input multiple-output antenna, enables a new communication model, termed vehicle-to-infrastructure edge network (V2IEN), to achieve the above communication goal. In this paper, we study the optimization of the data throughput of the proposed V2IENs in 5G with the Time Division Duplex scheme. We show that due to the nature of information flow in the proposed V2IENs, the uplink and downlink traffic loads are asymmetric. This asymmetric characteristic allows maximizing the degrees of freedom of the V2IENs by optimizing the time-slot resources for uplink and downlink transmission. In this work, we present the sum-DoF capacity of the V2IENs with proof. We demonstrate that a significant DoF gain is achieved for the V2IENs by carefully allocate the system transmission time resources. We further propose an iterative algorithm for network sum-rate maximization via the optimization of the vehicle and base station precoders. Numerical results demonstrate that a careful design of the precoders at the base station and at the vehicles can considerably improve the V2IENs performance. [ABSTRACT FROM AUTHOR]

This paper studies the network connectivity problem in a vehicular network and focuses on the two-way connectivity of vehicles moving on a one-way highway road with one entry and exit. The entry and exit separates the road into two segments, where vehicle arrivals follow a Poisson process. At the entry and exit, a vehicle may enter or exit from the road, or drive through the entry and exit. A connectivity analytical model is derived for analyzing the connectivity probability of vehicles moving on the road. The analytical model takes into account several parameters that may affect the connectivity probability, including a vehicle's arrival rate and moving speed as well as the probability that a vehicle drives through the entry and exit. Moreover, it considers two entry and exit scenarios: one with no roadside unit (RSU) deployed, and the other with one RSU deployed. The analytical model is justified through simulation results and the impacts of different parameters on the connectivity probability are investigated based on the derived analytical model. [ABSTRACT FROM PUBLISHER]

With the emergence of in-vehicle applications, providing the required computational capabilities is becoming a crucial problem. This paper proposes a framework named autonomous vehicular edge (AVE) for edge computing on the road, with the aim of increasing the computational capabilities of vehicles in a decentralized manner. By managing the idle computational resources on vehicles and using them efficiently, the proposed AVE framework can provide computation services in dynamic vehicular environments without requiring particular infrastructures to be deployed. Specifically, this paper introduces a workflow to support the autonomous organization of vehicular edges. Efficient job caching is proposed to better schedule jobs based on the information collected on neighboring vehicles, including GPS information. A scheduling algorithm based on ant colony optimization is designed to solve this job assignment problem. Extensive simulations are conducted, and the simulation results demonstrate the superiority of this approach over competing schemes in typical urban and highway scenarios. [ABSTRACT FROM AUTHOR]

Most researches on the Internet of Vehicles (IoV) in the 5G era are focused on the low latency and the massive transmitted data. However, the 5G base station has relatively a small coverage and a high cost, which is the main reason to hinder the development of the 5G applications on the IoV. In this paper, we try to put forward some feasible solutions for this problem. We will use two physical layer communication techniques, the Coherent Beamforming (CB) technique and the Successive Interference Cancellation (SIC) technique, to jointly optimize the throughput of vehicles data transmission and the network infrastructure cost. We first establish the mathematical model based on CB and SIC technology and prove that it cannot be solved directly. Then, we design the Road and CB-nodes Assignment (RCA) algorithm and the Uninstalling Data Scheduling (UDS) algorithm to solve the problem. RCA is based on CB and SIC technology to reasonably arrange CB-nodes, so that CB-nodes set can send data from vehicles to the base station and the base station can receive multiple sets of data at the same time. UDS schedules vehicles based on various factors to optimize the total amount of data transferred by vehicles and improve throughput. In simulations, we conduct some comparative experiments, which are the not using Algorithm 3 scheme, the CB scheme, the SIC scheme, the common scheme and the Relay Task Offloading Scheme in Vehicular Edge Computing algorithm (RVEC Algorithm). Results show that our proposed method has the advantages of cost saving and throughput improvement. [ABSTRACT FROM AUTHOR]

In recent years, the growing development of Connected Autonomous Vehicles (CAV), Intelligent Transport Systems (ITS), and 5G communication networks have led to the advent of Autonomous Intersection Management (AIM) systems. AIMs present a new paradigm for CAV control in future cities, taking control of CAVs in scenarios where cooperation is necessary and allowing safe and efficient traffic flows, eliminating traffic signals. So far, the development of AIM algorithms has been based on basic control algorithms, without the ability to adapt or keep learning new situations. To solve this, in this paper we present a new advanced AIM approach based on end-to-end Multi-Agent Deep Reinforcement Learning (MADRL) and trained using Curriculum through Self-Play, called advanced Reinforced AIM (adv.RAIM). adv.RAIM enables the control of CAVs at intersections in a collaborative way, autonomously learning complex real-life traffic dynamics. In addition, adv.RAIM provides a new way to build smarter AIMs capable of proactively controlling CAVs in other highly complex scenarios. Results show remarkable improvements when compared to traffic light control techniques (reducing travel time by 59% or reducing time lost due to congestion by 95%), as well as outperforming other recently proposed AIMs (reducing waiting time by 56%), highlighting the advantages of using MADRL. [ABSTRACT FROM AUTHOR]

To commercialize wirelesselectric vehicle charging (WEVC), three critical issues must be resolved: communication between the vehicle assembly (VA) and the ground assembly (GA), metal object detection (MOD) on the GA coil, and position detection (PD) of the VA. This paper proposes a comprehensive system that integrates MOD and PD with VA-GA near-field communication (NFC). A common high-frequency aided carrier is used not only for NFC, but also for MOD and PD. In addition, an array of dual-purpose detection coils is proposed as a sensor for MOD and PD. As a result, the three functions listed above share numerous components and circuits, greatly simplifying the system structure. Furthermore, because the high-frequency aided carrier can operate with or without a power carrier, the preceding three functions are independent of system power status. The proposed method has been validated using a 3.3 kW WEVC prototype operating at 85 kHz. The experimental results show that the proposed system is capable of performing both online and offline NFC, MOD, and PD with high sensitivity while having no impact on wireless charging efficiency. [ABSTRACT FROM AUTHOR]

The dual-function radar communication (DFRC) is an essential technology in Internet of Vehicles (IoV). Consider that the road-side unit (RSU) employs the DFRC signals to sense the vehicles’ position state information (PSI), and communicates with the vehicles based on PSI. The objective of this paper is to minimize the maximum communication delay among all vehicles by considering the estimation accuracy constraint of the vehicles’ PSI and the transmit power constraint of RSU. By leveraging convex optimization theory, two iterative power allocation algorithms are proposed with different complexities and applicable scenarios. Simulation results indicate that the proposed power allocation algorithm converges and can significantly reduce the maximum transmit delay among vehicles compared with other schemes. [ABSTRACT FROM AUTHOR]

In this paper, a novel part-based pedestrian detection algorithm is proposed for complex traffic surveillance environments. To capture posture and articulation variations of pedestrians, we define a hierarchical grammar model with the and–or graphical structure to represent the decomposition of pedestrians. Thus, pedestrian detection is converted to a parsing problem. Next, we propose clustered poselet models, which use the affinity propagation clustering algorithm to automatically select representative pedestrian part patterns in keypoint space. Trained clustered poselets are utilized as the terminal part models in the grammar model. Finally, after all clustered poselet activations in the input image are detected, one bottom-up inference is performed to effectively search maximum a posteriori (MAP) solutions in the grammar model. Thus, consistent poselet activations are combined into pedestrian hypotheses, and their bounding boxes are predicted. Both appearance scores and geometry constraints among pedestrian parts are considered in inference. A series of experiments is conducted on images, both from the public TUD-Pedestrian data set and collected in real traffic crossing scenarios. The experimental results demonstrate that our algorithm outperforms other successful approaches with high reliability and robustness in complex environments. [ABSTRACT FROM PUBLISHER]

In intelligent transportation systems (ITSs), the communication between the base station (BS) and the vehicles can form a virtual multiple-input multiple-out put (VMIMO) vehicle communication system. This paper addresses the issue of channel estimation in a virtual MIMO system that simultaneously adopt a smart antenna (SA). First, based on quaternion theory, a novel algorithm named quaternion noncircular MUSIC (QNC-MUSIC) is proposed, and direction of arrival (DOA) estimation is achieved for circular and noncircular signals. Then, a dimension reduction procedure is performed for the parameter search by employing the partial derivative of the spectrum function with respect to the noncircular phase; thus, a novel dimensional reduction noncircular MUSIC (DRNC-MUSIC) algorithm with a low computational complexity is proposed for DOA and polarization estimation of circular signals and noncircular signals. Finally, numerical examples are provided to demonstrate the parameter estimation performance of the proposed algorithm. [ABSTRACT FROM AUTHOR]

The introduction of light emitting diodes (LED) in automotive exterior lighting systems provides opportunities to develop viable alternatives to conventional communication and sensing technologies. Most of the advanced driver-assist and autonomous vehicle technologies are based on Radio Detection and Ranging (RADAR) or Light Detection and Ranging (LiDAR) systems that use radio frequency or laser signals, respectively. While reliable and real-time information on vehicle speeds is critical for traffic operations management and autonomous vehicles safety, RADAR or LiDAR systems have some deficiencies especially in curved road scenarios where the incidence angle is rapidly varying. In this paper, we propose a novel speed estimation system so-called the Visible Light Detection and Ranging (ViLDAR) that builds upon sensing visible light variation of the vehicle's headlamp. We determine the accuracy of the proposed speed estimator in straight and curved road scenarios. We further present how the algorithm design parameters and the channel noise level affect the speed estimation accuracy. For wide incidence angles, the simulation results show that the ViLDAR outperforms RADAR/LiDAR systems in both straight and curved road scenarios. [ABSTRACT FROM AUTHOR]

Urban freeway traffic control is of great importance for traffic management and intelligent transportation systems. Various approaches have been proposed to relieve urban freeway traffic jam, among which, ALINEA, a ramp metering strategy, is commonly implemented with fixed triggering threshold and static controller parameter. However, such a strategy may not be able to effectively alleviate the traffic congestion while maintaining certain ramp throughput due to two reasons: i). the congestion threshold can be time-varying due to different factors, such as segment ID, weather condition, time, and etc. ii). The congestion evolution patterns are time-varying even for the same segment. In this paper, based on over 890 million records of vehicles collected on ramps in Hangzhou, China, we established dynamic congestion threshold for each road segment with external factors. Based on such dynamic congestion threshold, we further clustered the congestion evolution patterns, and designed adaptive ramp controller which could switch the controller parameter according to the predicted congestion evolution pattern. Finally, in order to show the performance among different strategies, we introduced three baseline groups, which are ‘without Controller’, ‘ALINEA controller’, and ‘Direct RBF(radial basis function)-neural network controller’, respectively.The evaluation of proposed controller design over real large-scale data indicated that our method achieves 8.4%(7.2%), 4.62%(9.48%) efficiency improvement in terms of average speed in km/h (average traffic flow in veh/h) than the performance with normal ALINEA controller and RBF-neural network controller respectively. [ABSTRACT FROM AUTHOR]

The future of intelligent transportation system (ITS) is expected to be composed of connected and autonomous vehicles (CAVs), the development of which will have great impact on people's everyday life. Unfortunately, this progress will be accompanied by all kinds of potential threats and attacks rising in CAV network. As a legacy from traditional wireless networks, jamming attack is still one of the major and serious threats to vehicle-to-vehicle (V2V) communications. In this paper, we investigate the anti-jamming V2V communication in CAV networks through power control in conjunction with channel selection. Bringing into play a brain-inspired research tool called cognitive dynamic system (CDS), the general structure of cognitive risk control (CRC) is well-tailored to analyze and address the jamming problem. Specifically, power control is carried out first using reinforcement learning, the result of which is then examined by a module called task-switch control. Based on the risk assessment, a multi-armed bandit (MAB) problem is formulated to perform the channel-selection process when necessary. Through continuous perception-action cycles (PACs), the feature of predictive adaptation is realized for the legitimate vehicle in its behavioral interactions with the jammer. Simulation results have shown that the proposed method has desirable performance in terms of several evaluation metrics. [ABSTRACT FROM AUTHOR]