Cooperative Vehicle Infrastructure Research Articles

Multi-lane traffic flow model based on cellular automaton fine-scale under cooperative vehicle infrastructure system

Microscopic traffic flow modeling is crucial for predicting future traffic demand and optimizing the design of transportation systems. At the microscopic level, a well-crafted model is capable of describing the interactions and dynamic variations among vehicles with greater accuracy. The cell scale in the classical cellular automaton (CA) multi-lane traffic flow model is a critical parameter to accurately express the location relationship of vehicles. In this paper, a new method based on Symmetric Two-lane Cellular Automaton (STCA), entitled STCA-X, is proposed for the cell scale selection. Firstly, the position, velocity, acceleration, and interaction of vehicle operation in the urban multi-lane environment are analyzed, and a feature model is built based on CA. One important drawback of the existing models is that they do not match the vehicle movement in the actual lane. To handle this problem, a fine-scale CA multi-lane model is developed. Secondly, a new traffic flow model is created by redefining several key behaviors such as road following, lane changing, and blocking in the STCA model. The experimental results are analyzed and compared with several of the state-of-the-art methods. Analysis of the simulation results proves that STCA-X improves vehicle lane change frequency and lane utilization efficiency. However, the enhancements in the model have resulted in increased computational complexity. Consequently, future research will delve into novel approaches to boost computational efficiency, thereby propelling the progress of traffic flow modeling technology.

Perception data fusion-based computation offloading in cooperative vehicle infrastructure systems

Perception data fusion-based computation offloading in cooperative vehicle infrastructure systems

PAFNet: Pillar Attention Fusion Network for Vehicle–Infrastructure Cooperative Target Detection Using LiDAR

With the development of autonomous driving, consensus is gradually forming around vehicle–infrastructure cooperative (VIC) autonomous driving. The VIC environment-sensing system uses roadside sensors in collaboration with automotive sensors to capture traffic target information symmetrically from both the roadside and the vehicle, thus extending the perception capabilities of autonomous driving vehicles. However, the current target detection accuracy for feature fusion based on roadside LiDAR and automotive LiDAR is relatively low, making it difficult to satisfy the sensing requirements of autonomous vehicles. This paper proposes PAFNet, a VIC pillar attention fusion network for target detection, aimed at improving LiDAR target detection accuracy under feature fusion. The proposed spatial and temporal cooperative fusion preprocessing method ensures the accuracy of the fused features through frame matching and coordinate transformation of the point cloud. In addition, this paper introduces the first anchor-free method for 3D target detection for VIC feature fusion, using a centroid-based approach for target detection. In the feature fusion stage, we propose the grid attention feature fusion method. This method uses the spatial feature attention mechanism to fuse the roadside and vehicle-side features. The experiment on the DAIR-V2X-C dataset shows that PAFNet achieved a 6.92% higher detection accuracy in 3D target detection than FFNet in urban scenes.

Proactive Effects of C-V2X-Based Vehicle-Infrastructure Cooperation on the Stability of Heterogeneous Traffic Flow

Proactive Effects of C-V2X-Based Vehicle-Infrastructure Cooperation on the Stability of Heterogeneous Traffic Flow

Perception Methods for Adverse Weather Based on Vehicle Infrastructure Cooperation System: A Review.

Environment perception plays a crucial role in autonomous driving technology. However, various factors such as adverse weather conditions and limitations in sensing equipment contribute to low perception accuracy and a restricted field of view. As a result, intelligent connected vehicles (ICVs) are currently only capable of achieving autonomous driving in specific scenarios. This paper conducts an analysis of the current studies on image or point cloud processing and cooperative perception, and summarizes three key aspects: data pre-processing methods, multi-sensor data fusion methods, and vehicle-infrastructure cooperative perception methods. Data pre-processing methods summarize the processing of point cloud data and image data in snow, rain and fog. Multi-sensor data fusion methods analyze the studies on image fusion, point cloud fusion and image-point cloud fusion. Because communication channel resources are limited, the vehicle-infrastructure cooperative perception methods discuss the fusion and sharing strategies for cooperative perception information to expand the range of perception for ICVs and achieve an optimal distribution of perception information. Finally, according to the analysis of the existing studies, the paper proposes future research directions for cooperative perception in adverse weather conditions.

Development and experiment of an intelligent connected cooperative vehicle infrastructure system based on multiple V2I modes and BWM-IGR method

To increase the efficiency and safety of expressway, this paper constructed a new intelligent connected cooperative vehicle infrastructure system and its effectiveness was verifid from both data and practical applications. Firstly, considering the convenience of using intelligent networking systems for public transportation, a new intelligent connected cooperative vehicle infrastructure system architecture was proposed by incorporating mobile communication methods. Then, the new system was illustrated from road side unit (RSU), on board unit (OBU) and data interaction. Additionally, to verify the effectiveness of the system, this paper proposes a two-stage model named Transformer Embedded Clustering- Hierarchical Density-Based Spatial Clustering of Applications with Noise (TEC-HDBSCAN) model to identify outliers in the trajectory data of vehicles collected by the system and obtain the speed sequence of the vehicle. Finally, data from actual testing scenarios was collected and a Best Worst Method-Improved Gray Relational (BWM-IGR) model was built to verify the effectiveness of the system. The results show that the established intelligent networked transportation system can effectively guide vehicles and collect data with high accuracy.

Expressway ETC Transaction Data Anomaly Detection Based on TL-XGBoost

China’s widely adopted expressway ETC system provides a feasible foundation for realizing co-operative vehicle–infrastructure integration, and the accuracy of ETC data, which forms the basis of this scheme, will directly affect the safety of driving. Therefore, this study focuses on the abnormal data in an expressway ETC system. This study combines road network topology data and capture data to mine the abnormal patterns of ETC data, and it designs an abnormal identification model for expressway transaction data based on TL-XGBoost. This model categorizes expressway ETC abnormal data into four distinct classes: missing detections, opposite lane detection, duplicated detection and reverse trajectory detection. ETC transaction data from a southeastern Chinese province were used for experimentation. The results validate the model’s effectiveness, achieving an accuracy of 98.14%, a precision of 97.59%, a recall of 95.44%, and an F1-score of 96.49%. Furthermore, this study conducts an analysis and offers insights into the potential causes of anomalies in expressway ETC data.

Temporal variations in urban road network traffic performance during the early application of a cooperative vehicle infrastructure system: Evidence from the real world

A cooperative vehicle infrastructure system (CVIS) can promote the sustainable development of urban road traffic systems. This study collected over 7 million vehicle operating data points using a CVIS in the early stage over 28 days. The data were divided into two categories based on the CVIS type: passive CVIS (P-CVIS), which does not utilize a speed-guidance function, and active CVIS (A-CVIS), which incorporates a speed-guidance function. By comprehensively utilizing traffic flow parameter relationships, the Two-Fluid model, and vehicle energy flow theory, this study analyzed the impact of the CVIS in the early stage on traffic efficiency and energy consumption in the long-term cycle. The correlation between the different evaluation variables was also analyzed. The results indicate that the A-CVIS is more effective than the P-CVIS at improving traffic efficiency. However, driver unfamiliarity with the speed-guidance function results in increased vehicle acceleration, decreased travel comfort, and increased vehicle and network energy consumption. Furthermore, as time progresses, drivers become more proficient with the CVIS, resulting in a gradual improvement in traffic efficiency but a continuous increase in traffic energy consumption. Additionally, the study suggests that drivers may require at least a week to understand the basic skills of using an A-CVIS. This study can contribute to the development and application of eco-intelligent transportation system.

Electric vehicle charging scheduling control strategy for the large-scale scenario with non-cooperative game-based multi-agent reinforcement learning

With the popularity of electric vehicles (EVs), electric vehicle charging scheduling control in the complex urban environment has become a hot research issue, especially the use of multi-agent reinforcement learning (MARL) to solve game problems in the process of EV scheduling. In a complex and unstable environment involving high-speed dynamic changes of vehicles, the traditional methods have poor learning effects. In this paper, a multi-agent charge scheduling control framework with the cooperative vehicle infrastructure system (CVIS) is proposed to solve the game problems between electric vehicle charging stations (EVCSs) and EVs. To achieve effective control of electric vehicle charging stations in large-scale road networks, a new multi-agent A2C algorithm based on a non-cooperative game (NCG-MA2C) is proposed. In the proposed NCG-MA2C algorithm, the state representation is based on the K-nearest neighbor multi-head attention mechanism, the action definition is based on the proposed adaptive service price adjustment strategy, and spatio-temporal discount joint reward stable learning convergence. The results show that the proposed algorithm has good performance in increasing the efficiency of EVCSs while reducing EV charging cost compared with the fixed strategy, the greedy strategy, the independent Q learning (IQL) algorithm, the multi-agent Q-learning algorithm (MAIQL), and the multi-agent deep deterministic policy gradient (MADDPG) algorithm. The NCG-MA2C algorithm has strong extensibility and validity in the complex urban environment.

Priority of Emergency Vehicle Dynamic Right-Of-Way Control Method in Networked Environment

This paper proposes a dynamic right-of-way priority control approach for emergency vehicles (PDR-EVs) to improve their efficiency on basic road sections in the city based on a cooperative vehicle infrastructure system. Specifically, a movable physical function area was set in front of the EVs to prohibit connected vehicles (CVs) from entering a lane or to request them to change lanes to avoid a collision. Setting up a dynamic monitoring area at the EV’s front end affords real-time monitoring of the CV’s headway distribution in the inner lane. Moreover, a lane change request is sent when the CVs enter the buffer area, and the traversal search method predicts the optimal time and rate of speed to change the lane change and guides the CVs ahead of the EVs to merge into the target gap. Extensive simulations using the SUMO platform revealed that the priority of the dynamic right-of-way (PDR) control method reduced the average delay of the EVs by more than 70%, given that the road saturation did not exceed 0.8 and hardly increased the delay of the CVs (not more than 8%). Moreover, the simulations revealed that the long buffer area was suitable for low-volume conditions, and the short one was suitable for high-volume conditions. The proposed methodology fully employs the road space resources and enhances the EV’s operating efficiency on basic road sections while considering the CV’s operating efficiency.

Deadline-Aware Scheduling for Transmitted RSU Packets in Cooperative Vehicle-Infrastructure Systems

In Cooperative Vehicle Infrastructure System (CVIS), the roadside unit (RSU) obtains many kinds of monitoring data through observation equipment carried by the RSU. The monitoring data from RSUs are transmitted to an RSU that is connected to the backbone network using the “store–carry–forward” scheme through the mobile vehicle. The monitoring data obtained by RSUs are timely, and different types of monitoring data have corresponding timelines. Reducing end-to-end delays to ensure more packets can be transmitted before deadlines is challenging. In this paper, we propose a Distributed Packet Scheduling Scheme for Delay-Packets Queue Length Tradeoff System (DDPS) in CVIS to solve the multi-RSU-distributed packet transmission problem. We also establish the vehicle speed state, vehicle communication quantity prediction, data arrival, and end-to-end delay minimization models. After Lyapunov’s optimization theory transformed the optimization model, a knapsack problem was described. The simulation results verified that DDPS reduced the end-to-end average delay and ensured the data queue’s stability under packet deadline conditions.

Research on Truck Lane Management Strategies for Platooning Speed Optimization and Control on Multi-Lane Highways

Automated truck platooning has become an increasingly popular research subject, and its applicability to highways is considered one of the earliest possible landing scenarios for automated driving. However, there is a lack of research regarding the combination of truck platooning technology and truck lane management strategy on multilane highways in the environment of a cooperative vehicle–infrastructure system (CVIS). For highway weaving sections under the CVIS environment, this paper proposes a truck platooning optimal speed control model based on multi-objective optimization. Through a combination of model predictive control and the cell transmission model, this approach considers the bottleneck cell traffic flow, overall vehicle travel time, and truck platooning fuel consumption as objectives. By conducting experiments on a mixed traffic flow simulation platform, the multi-lane management strategies and optimal speed control effect were evaluated through different scenarios. This study also determined the appropriate proportion of truck platooning for an exclusive lane and to increase truck lanes, thus providing effective lane management decision support for highway managers.

An RSU Deployment Scheme for Vehicle-Infrastructure Cooperated Autonomous Driving

For autonomous driving vehicles, there are currently some issues, such as limited environmental awareness and locally optimal decision-making. To increase the capacity of autonomous cars’ environmental awareness, computation, decision-making, control, and execution, intelligent roads must be constructed, and vehicle–infrastructure cooperative technology must be used. The Roadside unit (RSU) deployment, a critical component of vehicle–infrastructure cooperative autonomous driving, has a direct impact on network performance, operation effects, and control accuracy. The current RSU deployment mostly uses the large-spacing and low-density concept because of the expensive installation and maintenance costs, which can accomplish the macroscopic and long-term communication functions but fall short of precision vehicle control. Given these challenges, this paper begins with the specific requirements to control intelligent vehicles in the cooperative vehicle–infrastructure environment. An RSU deployment scheme, based on the improved multi-objective quantum-behaved particle swarm optimization (MOQPSO) algorithm, is proposed. This RSU deployment scheme was based on the maximum coverage with time threshold problem (MCTTP), with the goal of minimizing the number of RSUs and maximizing vehicle coverage of communication and control services. Finally, utilizing the independently created open simulation platform (OSP) simulation system, the model and algorithm’s viability and effectiveness were assessed on the Nguyen–Dupuis road network. The findings demonstrate that the suggested RSU deployment scheme can enhance network performance and control the precision of vehicle–infrastructure coordination, and can serve as a general guide for the deployment of RSUs in the same application situation.

CenterLoc3D: monocular 3D vehicle localization network for roadside surveillance cameras

Monocular 3D vehicle localization is an important task for vehicle behaviour analysis, traffic flow parameter estimation and autonomous driving in Intelligent Transportation System (ITS) and Cooperative Vehicle Infrastructure System (CVIS), which is usually achieved by monocular 3D vehicle detection. However, monocular cameras cannot obtain depth information directly due to the inherent imaging mechanism, resulting in more challenging monocular 3D tasks. Currently, most of the monocular 3D vehicle detection methods still rely on 2D detectors and additional geometric constraint modules to recover 3D vehicle information, which reduces the efficiency. At the same time, most of the research is based on datasets of onboard scenes, instead of roadside perspective, which is limited in large-scale 3D perception. Therefore, we focus on 3D vehicle detection without 2D detectors in roadside scenes. We propose a 3D vehicle localization network CenterLoc3D for roadside monocular cameras, which directly predicts centroid and eight vertexes in image space, and the dimension of 3D bounding boxes without 2D detectors. To improve the precision of 3D vehicle localization, we propose a multi-scale weighted-fusion module and a loss with spatial constraints embedded in CenterLoc3D. Firstly, the transformation matrix between 2D image space and 3D world space is solved by camera calibration. Secondly, vehicle type, centroid, eight vertexes, and the dimension of 3D vehicle bounding boxes are obtained by CenterLoc3D. Finally, centroid in 3D world space can be obtained by camera calibration and CenterLoc3D for 3D vehicle localization. To the best of our knowledge, this is the first application of 3D vehicle localization for roadside monocular cameras. Hence, we also propose a benchmark for this application including a dataset (SVLD-3D), an annotation tool (LabelImg-3D), and evaluation metrics. Through experimental validation, the proposed method achieves high accuracy with A{P_{3D}} of 51.30%, average 3D localization precision of 98%, average 3D dimension precision of 85% and real-time performance with FPS of 41.18.

Safe, Efficient, and Comfortable Autonomous Driving Based on Cooperative Vehicle Infrastructure System.

Traffic crashes, heavy congestion, and discomfort often occur on rough pavements due to human drivers' imperfect decision-making for vehicle control. Autonomous vehicles (AVs) will flood onto urban roads to replace human drivers and improve driving performance in the near future. With the development of the cooperative vehicle infrastructure system (CVIS), multi-source road and traffic information can be collected by onboard or roadside sensors and integrated into a cloud. The information is updated and used for decision-making in real-time. This study proposes an intelligent speed control approach for AVs in CVISs using deep reinforcement learning (DRL) to improve safety, efficiency, and ride comfort. First, the irregular and fluctuating road profiles of rough pavements are represented by maximum comfortable speeds on segments via vertical comfort evaluation. A DRL-based speed control model is then designed to learn safe, efficient, and comfortable car-following behavior based on road and traffic information. Specifically, the model is trained and tested in a stochastic environment using data sampled from 1341 car-following events collected in California and 110 rough pavements detected in Shanghai. The experimental results show that the DRL-based speed control model can improve computational efficiency, driving efficiency, longitudinal comfort, and vertical comfort in cars by 93.47%, 26.99%, 58.33%, and 6.05%, respectively, compared to a model predictive control-based adaptive cruise control. The results indicate that the proposed intelligent speed control approach for AVs is effective on rough pavements and has excellent potential for practical application.

Roadside sensor network deployment based on vehicle-infrastructure cooperative intelligent driving

The sensor network for intelligent roadways, comprised of devices like cameras, laser radars, millimeter-wave radars, and weather stations, is an integral part of the roadside digital infrastructure. One of the main challenges in building intelligent highway sensor networks is to create a controllable, manageable, and useable sensor network with multi-modal sensors deployed on highways. This network should not only facilitate global and scene sensing but also enable collaborative sensing and control functions. Therefore, this study aims to define the concept, main features, and technical connotation of Vehicle-Infrastructure Cooperative Intelligent Driving (VICID). It also outlines the development of a cloud-native cloud control platform for intelligent roadways and refines the technology requirements and indices. This platform is designed to support open services for innovative applications, such as addressing bottlenecks, managing roadworks zones, and implementing dynamic lane assignments for automated driving. Lastly, we introduce Beijing's highway pilot projects, which can serve as a guide and reference for designing and constructing sensor network equipment for intelligent roadways in China, as well as for key technology research and development.

Safety benefit of cooperative control for heterogeneous traffic on-ramp merging

The safety of heterogeneous traffic is a vital topic in the oncoming era of autonomous vehicles (AVs). The cooperative vehicle infrastructure system (CVIS) is considered to improve heterogeneous traffic safety by connecting and controlling AVs cooperatively, and the connected AVs are so-called connected and automated vehicles (CAVs). However, the safety impact of cooperative control strategy on the heterogeneous traffic with CAVs and human-driving vehicles (HVs) has not been well investigated. In this paper, based on the traffic simulator SUMO, we designed a typical highway scenario of on-ramp merging and adopted a cooperative control method for CAVs. We then compared the safety performance for two different heterogeneous traffic systems, i.e. AV and HV, CAV and HV, respectively, to illustrate the safety benefits of the cooperative control strategy. We found that the safety performance of the CAV and HV traffic system does not always outperform that of AV and HV. With random departSpeed and higher arrival rate, the proposed cooperative control method would decrease the conflicts significantly whereas the penetration rate is over 80%. We further investigated the conflicts in terms of the leading and following vehicle types, and found that the risk of a AV/CAV followed by a HV is twice that of a HV followed by another HV. We also considered the safety effect of communication failure, and found that there is no significant impact until the packet loss probability is greater than 30%, while communication delay’s impact on safety can be ignored according to our experiments.

Connected eco‐driving for electric buses along signalized arterials with bus stops

AbstractWith the rapid development of the cooperative vehicle infrastructure system, eco‐approach and departure has been attracting increasing attention for mitigating the impact of signalized intersections, which helps improve the energy efficiency of equipped vehicles. However, existing studies have rarely considered the balance of electric bus energy consumption and traveling time. Meanwhile, the joint effect of bus stops and signalized intersections, and their interactions were not discussed widely with connected and automated electric buses (CAEBs). In this study, an integrated CAEB trajectory planning methodology with the balance of traveling time and energy consumption is proposed. A bi‐level programming model is developed to obtain the optimal trajectories of CAEBs. Numerical experiments are conducted to analyse and compare the model with two baseline models. The results demonstrate the optimal performance of the derived scheme in terms of bus energy consumption, with approximately 30–50% energy savings. Furthermore, model sensitivity analysis is conducted to quantify the impacts of the related system parameters. The findings also reveal that the entry time, conversion coefficient, and final speed have significant impacts on electric bus energy consumption along signalized arterials with bus stops.

Effects of Cooperative Vehicle Infrastructure System on Driver’s Visual and Driving Performance Based on Cognition Process

Effects of Cooperative Vehicle Infrastructure System on Driver’s Visual and Driving Performance Based on Cognition Process

Intelligent Computation Offloading for MEC-Based Cooperative Vehicle Infrastructure System: A Deep Reinforcement Learning Approach

In the cooperative vehicle infrastructure system, the road side unit (RSU) equipped with a mobile edge computing (MEC) server and sensors could provide vehicle infrastructure cooperation services for vehicles, such as optimization and cooperative driving, enhanced visibility, and so on. In view of this, the MEC server needs to fuse the sensing information from sensors on the vehicles and RSU, respectively. In the case of bad channel conditions, uploading the raw sensing information from the vehicles results in high uplink transmission latency. To deal with it, the vehicles can process the information locally and just deliver the results to the RSU. However, due to the limited computing resources on the vehicles, the processing accuracy of the raw information on the vehicles is lower than that on the MEC server. Besides, processing locally leads to higher vehicle energy consumption. Thus, in this paper, we aim to jointly optimize execution latency, processing accuracy, and energy consumption of the MEC-based cooperative vehicle infrastructure system. Firstly, we design the terminal machine learning task model and the edge machine learning task model on the vehicle side and RSU side, respectively. Then, we formulate a long-term multi-objective optimization problem. Owing to the stochastic traffic and time-varying communication conditions, we reformulate it as a Markov decision process and propose a two-stage deep reinforcement learning-based offloading and resource allocation (TDORA) strategy to determine the task offloading and the transmit power of each vehicle. Simulation results demonstrate the efficacy of the proposed strategy.