Dedicated short-range communication (DSRC) and 4G-LTE are two widely used candidate schemes for Connected Vehicle (CV) applications. It is thus of great necessity to compare these two most viable communication standards and clarify which one can meet the requirements of most V2X scenarios with respect to road safety, traffic efficiency, and infotainment. To the best of our knowledge, almost all the existing studies on comparing the feasibility of DRSC or LTE in V2X applications use software-based simulations, which may not represent realistic constraints. In this paper, a Connected Vehicle test-bed is established, which integrates the DSRC roadside units, 4G-LTE cellular communication stations, and vehicular on-board terminals. Three Connected Vehicle application scenarios are set as Collision Avoidance, Traffic Text Message Broadcast, and Multimedia File Download, respectively. A software tool is developed to record GPS positions/velocities of the test vehicles and record certain wireless communication performance indicators. The experiments have been carried out under different conditions. According to our results, 4G-LTE is more preferred for the nonsafety applications, such as traffic information transmission, file download, or Internet accessing, which does not necessarily require the high-speed real-time communication, while for the safety applications, such as Collision Avoidance or electronic traffic sign, DSRC outperforms the 4G-LTE.

/pdf/dsrc-versus-4g-lte-for-connected-vehicle-applications-a-33vlpsm21n.pdf

DSRC Versus 4G-LTE For Connected Vehicle Applications: A Study on Field Experiments of Vehicular Communication Performance

In this paper, we investigate the effects of heterogeneous connectivity structures and information delays on the dynamics of connected vehicle systems (CVSs), which are composed of vehicles equipped with connected cruise control (CCC) as well as conventional vehicles. First, a general framework is presented for CCC design that incorporates information delays and allows a large variety of connectivity structures. Then, we present delay-dependent criteria for plant stability and head-to-tail string stability of CVSs. The stability conditions are visualized by using stability diagrams, which allow one to evaluate the robustness of vehicle networks against information delays. To achieve modular and scalable design of large networks, we also propose a motif-based approach. Our results demonstrate the advantages of CCC vehicles in improving traffic efficiency, but also show that increasing the penetration of CCC vehicles does not necessarily improve the robustness if the connectivity structure or the control gains are not appropriately designed.

Motif-Based Design for Connected Vehicle Systems in Presence of Heterogeneous Connectivity Structures and Time Delays

Autonomous braking through vehicle precise decision-making and control to reduce accidents is a key issue, especially in the early diffusion phase of autonomous vehicle development. This paper proposes a deep reinforcement learning (DRL)-based autonomous braking decision-making strategy in an emergency situation. Three key influencing factors, including efficiency, accuracy and passengers' comfort, are fully considered and satisfied by the proposed strategy. First, the vehicle lane-changing process and the braking process are analyzed in detail, which include the critical factors in the design of the autonomous braking strategy. Second, we propose a DRL process that determines the optimal strategy for autonomous braking. Particularly, a multi-objective reward function is designed, which can compromise the rewards achieved of different brake moments, the degree of the accident, and the comfort of the passenger. Third, a typical actor-critic (AC) algorithm named deep deterministic policy gradient (DDPG) is adopted for solving the autonomous braking problem, which can improve the efficiency of the optimal strategy and be stable in continuous control tasks. Once the strategy is well trained, the vehicle can automatically take optimal braking behavior in an emergency to improve driving safety. Extensive simulations validate the effectiveness and efficiency of our proposal in terms of learning effectiveness, decision-making accuracy and driving safety.

A Decision-Making Strategy for Vehicle Autonomous Braking in Emergency via Deep Reinforcement Learning

The existing rear-end collision warning systems (CWS) that involve the variable perception–reaction time (PRT) have some negative effects on the collision warning performance due to the poor adaptive capability for the influence of different PRTs. To deal with the related problems, several studies have been conducted based on nonparametric approaches. However, the previous nonparametric methods are of doubtful validity with different PRTs. Moreover, there is a lack of consideration for the criterion to split the real-time data into training and testing sets in terms of enhancing the algorithm performance. In this paper, we propose multilayer perceptron neural-network-based rear-end collision warning algorithm (MCWA) to develop a real-time CWS without any influence of human PRTs. Through a sensitivity analysis, the optimal criterion for splitting real-time data into training and prediction is found in terms of a tradeoff between training time and algorithm accuracy. Comparison study demonstrates that the proposed algorithm outperforms other previous algorithms for predicting the potential rear-end collision by detecting severe deceleration in advance.

Real-Time Rear-End Collision-Warning System Using a Multilayer Perceptron Neural Network

IEEE 802.11p Wireless Access in the Vehicular Environment (WAVE) has been serving as the de facto wireless protocol for a Vehicular Ad hoc Network (VANET) with the explosive growth of vehicular applications. These applications require guaranteed Quality-of-Service (QoS). However, the fundamental channel access mechanism of WAVE, Enhanced Distributed Channel Access (EDCA), is not able to provide guaranteed QoS due to the unpredictable random access. To remedy this problem, we propose a novel channel access scheme, called Earliest Deadline First based Carrier Sense Multiple Access (EDF-CSMA). EDF-CSMA based on EDCA dynamically adjusts the priority of real-time streaming to avoid collision and introduces an admission control policy according to time constraints to provide guaranteed QoS in multi-channel environments. An analytical model is carried out to study and compare the channel utilization of EDF-CSMA and QoS-aware Hybrid Coordination Function (HCF) controlled channel access (HCCA) method. The result shows that 60 percent of channel utilization is improved by EDF-CSMA. Additionally, real video-based simulations are conducted to evaluate the performance of EDF-CSMA and the existing EDCA method. The results show that EDF-CSMA reaches better QoS support than EDCA while maintaining efficient channel utilization.

V2V QoS Guaranteed Channel Access in IEEE 802.11p VANETs

Dedicated short-range communication (DSRC) is an emerging technology that allows vehicles to communicate with each other. The rear-end collision warning system based on DSRC has its unique advantages. However, there are problems (e.g., high rates of false alarms and missing alarms in emergency warnings) in the system due to uncertain measurement errors. In this paper, we propose to address the problems by establishing a robust rear-end collision warning model without using expensive high-end devices. Simulations have shown that high rates (up to 56%) of missing alarms occur in the vehicle kinematics (VK) model, as well as false alarms (most of which exceed 70%) in the VK model with maximum compensation (VK-MC). Pertaining to these rates, a novel model based on the neural network (NN) approach is implemented. Through training and validation, the NN model is able to provide emergency warnings with an improved performance of false alarm probability under 20% and the missing alarm probability under 10% for all test cases.

Research on a DSRC-Based Rear-End Collision Warning Model

Mobile edge computing plays an irreplaceable role in 5G network and IoT technology. In addition, it is very urgent for MEC to find an effective task offloading decision and resource allocation method. Although there are many methods, they rarely consider the situation that the uneven processing capacity of MEC server leads to excessive task concentration during the process of offloading. In this article, a cooperative game is proposed to optimize the offloading and resource allocation schemes. The cost function is calculated by calculating the time and power consumption of tasks on different devices, and the task priority is determined by the difference between local calculation and MEC server calculation. In addition, the Lagrange multiplier method is used to allocate various resources required by the task. At the same time, the high-priority task is offloaded. On this basis, the task offloading scheme required by each server is obtained through multi-server cooperative game. The simulation results show that compared with the traditional algorithm, this scheme can not only reduce the total system overhead by about 31 % while meeting the task requirements, but also increase the system load rate by about 20 %. It maximizes the use of MEC server resources and balances the load between MEC servers with different computing capabilities.

Binfu Xiang

Papers

Research on a DSRC-Based Rear-End Collision Warning Model

Task Offloading and Resource Allocation Scheme based on Cooperative Game