Cooperative Vehicle Infrastructure System Research Articles

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

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

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.

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.

A Cooperative Positioning Enhancement for Self-Powered Vehicular Networks With V2I Trustworthy Data Sharing

The emergence of connected vehicles (CV) and cooperative vehicle-infrastructure systems (CVIS) have paved the way for new innovative prospects for improving the safety and efficiency of transportation systems. Unleashing the communication and positioning capabilities of connected vehicles and roadside units’ storage and computing capabilities, the CVIS can enhance the positioning signal coverage and improve the positioning accuracy of the vehicles with low positioning accuracy by sharing the positioning error correction model. In particular, based on the particle swarm optimization algorithm, we first design an improved deep neural network to train the positioning error model. Second, the selection crossover factors of the genetic algorithm are used to optimize the network. Then, based on the consensus algorithms, the positioning error model is shared and stored in the blockchain network to ensure the security of vehicles and roadside units that provide positioning correction information. Finally, the trained network is used to predict and correct the positioning errors of other vehicles with low positioning accuracy. Additionally, the proposed method’s performance and effectiveness in terms of accuracy, timeliness, and security are verified in different scenarios.

A cloud-based eco-driving solution for autonomous hybrid electric bus rapid transit in cooperative vehicle-infrastructure systems: A dynamic programming approach

Efficient public transportation has always intrigued extensive research. Aiming to improve the commuting efficiency and fuel economy of the autonomous hybrid electric buses in the Bus Rapid Transit (BRT), a cloud-based eco-driving solution adopting dynamic programming and model predictive control is proposed in this paper. This solution contains an upper-level cloud-based scheduling strategy and a lower-level onboard predictive energy management, which is conceived to function in a Cyber-physical system of the cooperative vehicle-infrastructure system. The scheduling model carefully considered coupled spatiotemporal constraints for the driving of autonomous BRT buses, including traffic lights, traffic regulations, stations, and ride comfort. The onboard energy management leverages the pre-planned scheduling information to achieve near-optimal fuel economy. The eco-driving solution is examined in three scenarios with intersections, stations, and ramps. Simulation results show that the proposed method can deal with different spatiotemporal limits along the route, with virtually no non-essential stops and sudden acceleration or braking, and achieves 97%–98% energy-saving potential compared with the baseline performance.

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.

Joint Optimization of Platoon Control and Resource Scheduling in Cooperative Vehicle-Infrastructure System

Vehicle platooning technology is essential in achieving group consensus, on-road safety, and fuel-saving. Meanwhile, Vehicle-to-Infrastructure (V2I) communication significantly facilitates the development of connected vehicles. However, the coupled effects of the longitudinal vehicle's mobility, platoon control and V2I communication may result in a low reliable communication network between the platoon vehicle and the roadside unit, there is a tradeoff between the platoon control and communication reliability. In this paper, we investigate a bi-objective joint optimization problem where the first objective is to maximize the success probability of data transmission (communication reliability) and the second objective function is to minimize the traffic oscillation flow. The vehicle's mobility state of the platoon vehicle affects the channel capacity and transmission performance. In this context, we deeply explore the relationship between control signals and resource scheduling and theoretically deduce a closed-form expression of the optimal communication reliability objective. Through this closed expression, we transform the bi-objective model into a single objective MPC model by using <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\epsilon$</tex-math></inline-formula> -constraint method. We design an efficient algorithm for solving the joint optimization model and prove the convergence. To verify the effectiveness of the proposed method, we finally evaluate the spacing error, speed error, and resource scheduling of platooning vehicles through simulation experiments in two experimental scenarios. The results show that the proposed control-communication co-design can improve the platoon control performance while satisfying the high reliability of V2I communications.

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.

A Cooperative Vehicle-Infrastructure System for Road Hazards Detection With Edge Intelligence

Road hazards (RH) have always been the cause of many serious traffic accidents. These have posed a threat to the safety of drivers, passengers, and pedestrians, and have also resulted in significant losses to people and even to the economies of countries. Hence, road hazards detection (RHD) could play an essential role in intelligent transportation systems (ITSITS). The cooperative vehicle-infrastructure systems (CVIS) coordinate the communication between vehicles and roadside infrastructures. Onboard computing devices (OCD), then, make fast analyses and decisions based on road conditions. In this study, an RHD solution based on CVIS is proposed. Firstly, a high-performance heavy action detection model is selected. Using a meta-learning paradigm, critical features are generalized from a few-shot RH data. Secondly, we designed a lightweight RHD model to ensure its smooth inference on an OCD. Thirdly, we use a knowledge distillation (KD) framework to progressively distill the features of the complex model and the privileged information of the data into the lightweight one. Experimental results demonstrate that the model can effectively detect RH and obtain an accuracy of 90.2% with an inference time of 14.7ms.

Predictive Cruise Control Under Cloud Control System for Urban Bus Considering Queue Dissipation Time

The driving conditions of urban consecutive signalized intersections are one of the main research scenarios for vehicle speed trajectory optimization, and typical for bus driving, where frequent acceleration and deceleration before and after the intersection can intensify the energy consumption of the bus. Prior research has predictive cruise controlled under Intelligent Transportation System, which is not feasible to directly communicate with controllers' units of Intelligent Connected Vehicles. Besides, the effect of queue dissipation is a topic that has received less attention in recent related work. Therefore, this paper proposes a vehicle-cloud hierarchical architecture based on Cloud Control System at first, under which a predictive cruise control for urban buses is deployed. Given the impact of intersection queue length and dissipation time on vehicle driving, a queue dissipation time estimation model based on shockwave theory is proposed to predict changes in intersection traffic state. The queue dissipation time equivalent to the extension of the red-light window is reflected in the constraints of the Receding Distance Horizon Dynamic Programming (RDHDP) algorithm for solving the optimal control problem. Eventually, comparison simulations, a segment of realistic trip between adjacent stops, are presented. The results show that the proposed method saves 44.94%-56.74% of energy consumption and at least 26.8s of waiting time compared to human drivers, and 22.72%-41.27% of energy consumption compared to vehicle with the Intelligent Vehicle Infrastructure Cooperative Systems.

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.

Construction of Real-time Dynamic Reversible Lane Safety Control Model in Intelligent Vehicle Infrastructure Cooperative System

ABSTRACT Reversible lanes play an important role in alleviating urban traffic congestion and improving the utilization of road resources. However, most of the reversible lanes currently implemented are timed and fixed, and there is little research on the safety of reversible lanes without isolation facilities. In view of the existing problems of reversible lanes, this paper proposed a real-time dynamic reversible lane safety control model. The influencing factors of reversible lane safety control are analyzed first, and then determine model parameters of the no-entry zone and the buffer zone in the conflict area. Moreover, model parameters of emptying, exit, and recovery zones in the operation process were determined as well. Additionally, this paper discussed the length of reversible lanes and the timing of signals. The surrogate safety assessment model (SSAM) is adopted to analyze vehicle trajectory data for traffic simulation under different traffic flows. The experiment results show that the proposed reversible lane safety control scheme can keep the vehicle’s traffic conflict rate below 5%, which can effectively ensure the safe operation of the reversible lane.

Multivariate Effectiveness of Ecolane and Ecohmi Based Cooperative Vehicle-Infrastructure System

Multivariate Effectiveness of Ecolane and Ecohmi Based Cooperative Vehicle-Infrastructure System

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.

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.

A Novel Spatio-Temporal Synchronization Method of Roadside Asynchronous MMW Radar-Camera for Sensor Fusion

Roadside sensors, such as camera and millimeter-wave (MMW) radar, provide traffic information beyond the visual range of intelligent vehicles in cooperative vehicle-infrastructure systems. Unlike onboard equipment, roadside sensors are affiliated with different systems and lack synchronization in both space and time. In this paper, we propose a novel spatio-temporal synchronization method of asynchronous roadside MMW radar–camera for sensor fusion, which utilizes features of the scenario to extract lane line corner points to pre-calibrate the camera. Based on the consistent time flow rate of the separate sensors, multiple virtual detection lines are set up to match the time headway of successive vehicles and conduct objective matching to track data. Finally, a synchronization optimization model is formulated and a constrained nonlinear minimization solver is applied to tune the parameters. Measure data from Donghai Bridge in Shanghai is applied to verify the feasibility and effectiveness of the method. The results determine that there are 33 frames (33*40 ms) of temporal deviation between the camera and the radar in this case. After the synchronization, the average spatial deviation is reduced from 2.47 m to 0.42 m in the X-direction and 64.06 m to 2.34 m in the Y-direction, respectively. This study provides an economical and effective way to solve the problem of spatio-temporal synchronization of roadside sensors.

A Novel Information Theory-Based Metric for Evaluating Roadside LiDAR Placement

Intelligent vehicle infrastructure cooperative systems can essentially address the bottleneck of the limited perception range of autonomous vehicles. This is achieved by the real-time perception of road traffic using roadside mounted sensors, which is receiving increasing attention. Light detection and ranging (LiDAR) has progressively been deployed on the roadside due to its advantage of remaining unaffected by lights and the capability to obtain 3-D information about traffic participants. However, a practical evaluation criterion is currently lacking for assessing the merits of roadside LiDAR placement. In this study, we propose an information-theoretic LiDAR mounting placement evaluation method, which simplifies the complex problem of LiDAR mounting placement to a simple parameter calculation problem. We characterize the amount of information detected by LiDAR in the region of interest (ROI) by introducing the per voxel point clouds’ metric. The resulting metric, named the maximum density gain, quantifies the effect of LiDAR mounting placements on perception performance. A bidirectional eight-lane traffic scenario is constructed in Unity3D, and datasets of simulated LiDARs are collected under two traffic conditions: with and without obstruction. The Pointpillars is used to test the collected dataset, which is a point cloud-based 3-D object detection algorithm. The results of Pointpillars and our proposed method are compared to test the validity of the proposed method. The experimental results show that our proposed method is compatible with the outcomes of Pointpillars while being simpler and computationally efficient. The proposed LiDAR mounting placement evaluation method can offer a reliable calculation reference for the mounting placement of roadside LiDARs.