This study explores the dynamics of a gated memory car-following system, with a focus on the challenges encountered when training models using fine-grained spatiotemporal data. To address the issues of redundant gradient updates and limited generalization inherent in traditional sequential training methods, a novel Spatiotemporal-Decoupled Training (SDT) method is proposed. This method enhances gradient variance by decoupling temporal dependencies and mixing trajectory segments from different vehicles, thereby improving model generalization performance and achieving a zero collision rate on test dataset. Experimental validation is carried out using three datasets (HighD, NGSIM-I80 and Lyft) and two basic models (GRU and LSTM) to assess the effectiveness of the proposed method. The results demonstrate significant improvements in model performance, including an 80% reduction in generalization error on the HighD dataset, a 14% reduction on the NGSIM-I80 dataset a 57% reduction on Lyft dataset, and the achievement of a Zero-collision rate on all test datasets, showcasing the potential of the SDT method for intelligent driving systems. Our code and experimental configurations are publicly available on GitHub to facilitate reproducibility and comparison:https://github.com/LiangzgJlu/Spatiotemporal-Decoupled-Training

Monocular 3D object detection remains a challenging task due to the inherent limitations of single-view depth perception. While conventional methods treat all parts of the Region of Interest (RoI) uniformly, we argue that different RoI regions hold varying importance for accurate detection. This study introduces the Multi-Scale Grid Attention (MSGA) mechanism to investigate the significance of RoI regions at multiple scales. Furthermore, we propose a novel probabilistic post-processing method to enhance detection robustness by effectively utilizing the probabilistic properties of depth estimation during inference. Our approach achieves state-of-the-art performance on the KITTI and Waymo datasets, demonstrating significant improvements in detection accuracy and robustness.

Vehicular edge computing (VEC) is emerging as a promising architecture of vehicular networks (VNs) by deploying the cloud computing resources at the edge of the VNs. However, efficient resource management and task offloading in the VEC network is challenging. In this work, we first present a hierarchical framework that coordinates the heterogeneity among tasks and servers to improve the resource utilization for servers and service satisfaction for vehicles. Moreover, we formulate a joint resource allocation and task offloading problem (JRATOP), aiming to jointly optimize the intra-VEC server resource allocation and inter-VEC server load-balanced offloading by stimulating the horizontal and vertical collaboration among vehicles, VEC servers, and cloud server. Since the formulated JRATOP is NP-hard, we propose a cooperative resource allocation and task offloading algorithm named BARGAIN-MATCH, which consists of a bargaining-based incentive approach for intra-server resource allocation and a matching method-based horizontal-vertical collaboration approach for inter-server task offloading. Besides, BARGAIN-MATCH is proved to be stable, weak Pareto optimal, and polynomial complex. Simulation results demonstrate that the proposed approach achieves superior system utility and efficiency compared to the other methods, especially when the system workload is heavy.

Connected and Automated Vehicle (CAV) is a newgeneration vehicle equipped with advanced on-board sensors, controllers, actuators, and other devices. In recent years, CAVs have gradually become the main trend of vehicle development, and people’s choice of travel mode has begun to change from traditional cars to CAVs. Cellular Vehicle-to-Everything (C-V2X) connects vehicles to everything and allows transportation participants to interconnect through modern communication technologies. In C-V2X method, cooperative lane change is one of the important parts. The challenge of cooperative lane change has two main parts, one is how to determine a safe and efficient passing strategy, and the other is how to determine a reasonable communication method. To solve the challenge, in this paper, we use V2X communication as the basis of vehicle information exchange, transform the two-vehicle lane-changing problem into a Markov Decision Process (MDP), and then solve it through Q-learning and DQN to obtain the ideal lane-changing decision.

CAVs (connected and autonomous vehicles) are developing quickly and changing how people drive. Vehicle-to-anything (V2X) technology is rekindling corporate interest as 5G and 6G technologies take off. The absence of a reliable functional testing approach is one of the main issues with current technology. Currently, testing scenario libraries are created manually by testers, which has the drawback of being scarce and ineffective. Traditional automated generating algorithms provide limited-coverage scenarios that do not account for the influence of sensors. Our contributions to solving these issues are as follows. First, we extract the roads in the research region from OpenStreetMap (OSM), filter them, and annotate them using hierarchical clustering of feature values, which creates a static road library. Second, reinforcement learning is used to model dynamic situations using a partly observable Markov decision process (POMDP) in conjunction with sensor inputs. The creation process can be run concurrently with functional tests. Third, the efficiency of simulation testing is increased by integrating the static road library and the dynamic scenario section to produce a sizable library of test scenarios. This increases the realism and coverage of the library. The experimental results show that the proposed scene construction method is well suited for use in SUMO, VTD and other simulators, and has a 388% improvement in scenario coverage compared to the traditional method.

C-V2X, as an end-to-end wireless communication network for intelligent transportation system, may lead to degradation of its communication quality in the case of high vehicle density, and the large-scale field test provides a test environment for its possible situation. whether the communication performance of C-V2X system in the large-scale field test can meet the technical requirements is the premise of whether C-V2X system can be applied and promoted in China. At this stage, the data after the large-scale test is only analyzed in terms of time delay and packet error rate(PER), and only the analysis of these two indicators is not able to judge whether the equipment meets the technical indicators in the large-scale scenario. In this paper, we propose an analysis and evaluation scheme for the data after the communication performance of vehicle devices in large-scale tests, add the evaluation indexes of interval delay jitter and throughput, and propose some improvement schemes for the existing test data saving format.

The complex traffic environment of the intersection region makes traffic accidents occur frequently in this section. Intelligent network vehicles can give early warning of possible dangers through communication between vehicles and other traffic participants, so as to reduce the occurrence of traffic accidents. At present, many automobile enterprises are studying the landing application of vehicle to everything (V2X). This paper focuses on the application requirements of intersection collision warning (ICW) to V2X. Testing is an integral part of vehicle internet and can ensure safe and effective use of vehicle internet.In this paper, we describe in detail the testing process and results of communications performance of vehicle-to-vehicle (V2V) at an obstructed intersection in a closed test field where there are a lot of cars as a background. In addition, we have proposed a message forwarding mechanism based on vehicle to vehicle to infrastructure to vehicle (V2I2V), and the test results show that V2I2V performs better than V2V at an intersection with obstacles.

In the communication of devices based on C-V2X, packet error rate (PER) is an important metric to measure the communication performance of a device. As for the packet loss phenomenon, we usually focus on why the receiver did not successfully receive the message, and rarely focus on whether the transmitter actually sent the message.We usually consider the messages sending situation recorded by the application layer as the messages that should be received by the receiver (packets that are known to be not sent by the application layer due to application layer congestion control, etc., are not in the scope of this paper). However, in the actual communication process, there are some discrepancies between the real packets sent from the bottom layer and the application layer's records. In the 2020 C-V2X Large-scale Pilot Demonstration, when we analyzed the results and calculated the received PER of the devices, we were confused whether some of the devices did not send all the packets successfully. Based on this confusion, we defined the concept of transmitter traffic to represent the actual packet sending situation of the device. We designed a method to calculate transmitter traffic by using the "large-scale" data available, and conducted statistics on the transmitter traffic of more than 40 terminal companies, more than 10 chip module companies, and more than 50 participating devices. We analyzed the statistical results, and analyzed the possible reasons for the unsuccessful transmitter traffic.

This article proposes a new solution for end-to-end autonomous mining operations: Internet of Things (IoT)-based parallel mining, consisting of the concept definition, the solution given, and the concrete realization. The proposed parallel mining is inspired by the artificial societies (A) for modeling, computational experiments (C) for analysis, and parallel execution (P) for control (ACP) approach. The basic framework of parallel mining is given and its advantages are expounded. Then, the solution of parallel mining is proposed, which is mainly composed of four parts: 1) the management and control center for autonomous mining; 2) the autonomous transportation platform of truck; 3) the semiautonomous mining/shovel platform; and 4) the remote takeover platform. Key technologies of IoT-based parallel mining are discussed in detail, namely, network communication, virtual parallel mining construction, mining environment perception over-the-horizon for the moving area and obstacle detection, collaborative decision making, planning, and control for unmanned mining equipment, and parallel taking-over and remote control. Finally, the performance of IoT-based parallel mining, including fusion perception, collaborative decision making, planning, and control, is evaluated. The realization of parallel mining can fundamentally improve the safety of personnel and equipment, reduce the cost of mining operation, and increase the production rate.

Vehicular ad hoc networks (VANETs) can improve the safety during the traffic by enabling cooperative communication among the vehicles. The media access control (MAC) protocol should be well designed so that cooperative messages can be exchanged efficiently and reliably. Because vehicles move fast on the road, the network topology changes rapidly, which makes it harder to design the MAC protocol. This article introduces SCMAC, a slotted-contention-based time-division multiple access MAC protocol. SCMAC combines the advantages of the contention-based protocols and the contention-free protocols, and hence, can accommodate different traffic densities and channel conditions. Each time slot is divided into two periods: 1) reservation period (RP) and 2) transmission period (TP) in the protocol. Nodes compete in the RP to confirm whether the channel can be used before the transmission can take place in the TP. Analysis and simulation results are also presented to evaluate the performance of SCMAC in various scenarios. The results show that SCMAC can adapt different traffic densities and channel conditions and can provide more real time and efficient services compared to the other protocols.

Distributed cooperative control requires that every participant shares a consistent view of objectives and the world. Information is periodically disseminated over a noisy time-varying network topology so that all the agents asymptotically converge to a common value. However, the strict global consensus is of excessive resource consumption and not mandatory for the majority of coordination tasks. To better satisfy such quantitative requirements of consensus in the practical multi-agent systems, this paper proposes a real time and distance-driven consensus quantification model especially for C-ITS applications. This model encodes agents' spatial location distribution into their mutual consensus quantification through introducing their inter-distance into consensus calculation. Accordingly, this paper proposes a distance-driven-consensus-based power adaptive control method as a practical use case of the quantitative framework of consensus, by which agents can autonomously optimize the transmit power through balancing the desired consensus benefit and power cost according to the real timely predicted local consensus. We perform extensive numerical calculations to investigate the effectiveness and the applicability of the consensus quantification framework and the power adaptive control method. The results show that the model can effectively capture the real time consensus fluctuation as the multi-agent systems evolve and can provide reliable decision basis to cooperative control, in such way to restrict the consensus extent to a target value and to tradeoff between the anticipated consensus level and the paid cost accordingly.

Mobile social networks (MSNs) are getting increasingly popular day by day. With the help of MSNs people can connect with each other online as well as offline. One of the famous applications of MSNs is profile matchmaking in which users share their interests in order to find mutual friends. However, the user of such application can be a victim of various privacy related attacks due to the revelations of user's personal interests during profile matchmaking. More recent dimension of MSN is encounter-based social networks. In this scenario, the users make social contacts while sharing a common location and time. Mobile devices record the encounter information when two users are within physical proximity and at a later time, their encounter history should be matched when they try to find each other again. In this paper, we propose a privacy preserving matchmaking protocol in which users share the encounter information and later utilize a cloud server to post encounter information in order to privately match their profiles with unknowns to whom they shared the encounter. In our protocol, neither the users nor the cloud server is able to discover the interests of either participants during matchmaking. To the best of our knowledge, this approach is the first attempt that combines encounter based social networks and fine-grained profile matchmaking. Towards the end of the paper, we present security analysis as well as performance evaluation that shows the effectiveness and feasibility of our protocol.

Vehicular ad hoc network (VANET) is an application of intelligent transportation system (ITS) with emphasis on improving traffic safety as well as efficiency. VANET can be thought as a subset of mobile ad hoc network (MANET) where vehicles form a network by communicating with each other (V2V) or with infrastructure (V2I). Vehicles broadcast not only traffic messages but also safety critical messages such as electronic emergency braking light (EEBL). A misuse of this application may result in a traffic accident and loss of life at worse. This situation makes vehicles' authentication a necessary requirement in VANET. During authentication, vehicle's privacy related data such as vehicle and owner's identity and location information should be kept private in order to prevent an attacker from stealing this information. This paper presents a cloud-assisted conditional privacy preserving authentication (CACPPA) protocol for VANET. CACPPA is a hybrid approach that utilizes both the concept of pseudonym-based approaches and group-signaturebased approaches but cleverly avoids the inherent drawbacks of these approaches. CACPPA neither requires a vehicle to manage a certificate revocation list nor does it require vehicle to manage any groups. In fact an efficient cloud-based certification authority is used to assist vehicles getting credentials and subsequently using them during authentication. CACPPA provides conditional anonymity that a vehicle's anonymity preserved only until it honestly follows the protocol. Furthermore, we analyze CACPPA with various attack scenarios, present a computational and communication cost analysis as well as comparison with existing approaches to show its feasibility and robustness.

Abstract The Notice of Proposed Rulemaking 13‐22 released by Federal Communications Commission unlocks the Dedicated Short Range Communication (DSRC) spectrum for Wi‐Fi availability, which undoubtedly brings unpredictable effects to the new‐emerging vehicular applications and services. To efficiently harmonize the spectrum operation between DSRC and Wi‐Fi networks, several dynamic spectrum‐sharing schemes are already proposed to improve the spectral efficiency over a limited bandwidth situation and as well to satisfy the ever‐increasing demand for bandwidth resource. Different from most previous literature that mainly focused on the performance analysis of cellular‐network‐centric spectrum sharing, we aim to analyze the performance of the mainstream dynamic spectrum‐sharing schemes specially designed for the coexistence of DSRC and Wi‐Fi networks against various combinations of network parameters through a hybrid network model and performance indicators. We employ the Poisson point process to model a hybrid network where DSRC vehicles and Wi‐Fi devices coexist, and introduce the performance indicators of spectrum efficiency and data rate to assess the utility of different spectrum sharing candidates. Through the presented hybrid model and performance indicators, we collect extensive numerical and simulation results to investigate four typical spectrum allocation schemes for DSRC and Wi‐Fi coexistence, that is non‐sharing scheme, original sharing scheme, and Qualcomm's and Cisco's proposals, respectively. The results show that the dynamic spectrum sharing in the 5.9‐GHz band can significantly raise the performance of Wi‐Fi network without excessively degrading the DSRC system, and especially the Cisco's proposal prefers to protect the DSRC profit while the Qualcomm's draft favors Wi‐Fi exclusively. Copyright © 2016 John Wiley & Sons, Ltd.

Information exchange is not easily secured in the emergency cases where the normal telecommunication infrastructure might have been collapsed. When vehicles are moving on a highway, communications between the vehicles and the base stations always result in a high delay that causes a vehicle to fail to verify all the messages received from the neighbors in real time. These situations may result in message losses and even security risks. To address these issues, we propose a scheme that combines the technologies of trusted network connect and multi-secret sharing to securing authentication and verification for ad hoc vehicles SAV4AV, in which a new vehicle is permitted to flexibly join in a platoon through collaborating with t existing vehicles and thereby to accomplish identity authentication and integrity verification. We list several possible attacks and provide a detailed security analysis on how to avoid these threats in SAV4AV. Moreover, we perform extensive simulations to investigate the performance of SAV4AV against various network scenarios with respect to time consumption and network throughput. Copyright © 2014 John Wiley & Sons, Ltd.