Intelligent Roadside Research Articles

PDT-YOLO: A Roadside Object-Detection Algorithm for Multiscale and Occluded Targets.

To tackle the challenges of weak sensing capacity for multi-scale objects, high missed detection rates for occluded targets, and difficulties for model deployment in detection tasks of intelligent roadside perception systems, the PDT-YOLO algorithm based on YOLOv7-tiny is proposed. Firstly, we introduce the intra-scale feature interaction module (AIFI) and reconstruct the feature pyramid structure to enhance the detection accuracy of multi-scale targets. Secondly, a lightweight convolution module (GSConv) is introduced to construct a multi-scale efficient layer aggregation network module (ETG), enhancing the network feature extraction ability while maintaining weight. Thirdly, multi-attention mechanisms are integrated to optimize the feature expression ability of occluded targets in complex scenarios, Finally, Wise-IoU with a dynamic non-monotonic focusing mechanism improves the accuracy and generalization ability of model sensing. Compared with YOLOv7-tiny, PDT-YOLO on the DAIR-V2X-C dataset improves mAP50 and mAP50:95 by 4.6% and 12.8%, with a parameter count of 6.1 million; on the IVODC dataset by 15.7% and 11.1%. We deployed the PDT-YOLO in an actual traffic environment based on a robot operating system (ROS), with a detection frame rate of 90 FPS, which can meet the needs of roadside object detection and edge deployment in complex traffic scenes.

Research on Over-the-Horizon Perception Distance Division of Optical Fiber Communication Based on Intelligent Roadways.

With the construction and application of more and more intelligent networking demonstration projects, a large number of advanced roadside digital infrastructures are deployed on both sides of the intelligent road. These devices sense the road situation in real time through algorithms and transmit it to edge computing units and cloud control platforms through high-speed optical fiber transmission networks. This article proposes a cloud edge terminal architecture system based on cloud edge cooperation, as well as a data exchange protocol for cloud control basic platforms. The over-the-horizon scene division and optical fiber network communication model are verified by deploying intelligent roadside devices on the intelligent highway. At the same time, this article uses the optical fiber network communication algorithm and ModelScope large model to model inference on real-time video data. The actual data results show that the StreamYOLO (Stream You Only Look Once) model can use the Streaming Perception method to detect and continuously track target vehicles in real-time videos. Finally, the method proposed in this article was experimentally validated in an actual smart highway digital infrastructure construction project. The experimental results demonstrate the high application value and promotion prospects of the fiber optic network in the division of over the horizon perception distance in intelligent roadways construction.

CPS-Based Event-Triggered Control for Connected Vehicles With Intelligent Roadside Equipment

CPS-Based Event-Triggered Control for Connected Vehicles With Intelligent Roadside Equipment

Vehicle Detection Using the Light Weight Model Based on YOLOv5

Vehicle detection is important for the administration because it’s an important part of the intelligent traffic management system. It can help to make the best decision for the government and provide the data for intelligent roadside equipment. And can reduce road congestion and emissions, reduce the incidence of traffic accidents, improve road safety and reduce damage caused by accidents. The current method of object detection needs large computing resources and makes the roadside equipment costly or unable to meet the computing resources. So, this paper proposed a light weight detection model based on YOLOv5. This model reduces the parameter and FLOPs by using the deepthwise separable convolution CBAM to improve the performance of vehicle detection. And this paper reduces the input image size and halves the feature channel to reduce the amount of computation. The experiment result shows that the proposed model is 8 times smaller than YOLOv5-s while the mAP only decreases 6% on the UA-DETRAC dataset, significantly outperforming the other methods. This paper provided an idea for implementing a light weight model and a way to reduce the application cost of roadside smart devices.

Battery-Powered RSU Running Time Monitoring and Prediction Using ML Model Based on Received Signal Strength and Data Transmission Frequency in V2I Applications

The application of the Internet of Things (IoT), vehicles to infrastructure (V2I) communication and intelligent roadside units (RSU) are promising paradigms to improve road traffic safety. However, for the RSUs to communicate with the vehicles and transmit the data to the remote location, RSUs require enough power and good network quality. Recent advances in technology have improved lithium-ion battery capabilities. However, other complementary methodologies including battery management systems (BMS) have to be developed to provide an early warning sign of the battery's state of health. In this paper, we have evaluated the impact of the received signal strength indication (RSSI) and the current consumption at different transmission frequencies on a static battery-based RSU that depends on the global system for mobile communications (GSM)/general packet radio services (GPRS). Machine learning (ML) models, for instance, Random Forest (RF) and Support Vector Machine (SVM), were employed and tested on the collected data and later compared using the coefficient of determination (R2). The models were used to predict the battery current consumption based on the RSSI of the location where the RSUs were imposed and the frequency at which the RSU transmits the data to the remote database. The RF was preferable to SVM for predicting current consumption with an R2 of 98% and 94%, respectively. It is essential to accurately forecast the battery health of RSUs to assess their dependability and running time. The primary duty of the BMS is to estimate the status of the battery and its dynamic operating limits. However, achieving an accurate and robust battery state of charge remains a significant challenge. Referring to that can help road managers make alternative decisions, such as replacing the battery before the RSU power source gets drained. The proposed method can be deployed in other remote WSN and IoT-based applications.

Objects Detection With 3-D Roadside LIDAR Under Snowy Weather

Rain and snowfall will increase noise, change the resolution of objects in the point cloud and present great challenges to the accurate recognition of traffic objects. Accordingly, this article proposes an efficient real-time method for roadside 3-D light detection and ranging (LIDAR) background point cloud extraction and object segmentation under snowy weather. We first use a historical point cloud sequence to quickly construct a background model, extract the background point cloud from the current frame by using a background difference method and update the background model in real-time. Then, the noise caused by snowfall in the non-background point cloud is filtered based on the beam density difference of the object point clouds. Finally, the remaining object point cloud is accurately segmented based on the proposed hierarchical object clustering method. We use an intelligent roadside system equipped with 3-D roadside LIDAR to collect point cloud data in a snowfall environment and evaluate the proposed method qualitatively and quantitatively. Experimental results show that the proposed method effectively avoids the problem of under-segmentation and over-segmentation of object point clouds under snowy conditions, and the precision and recall rate of traffic object segmentation reached 96.41% and 95.02%, respectively, thereby indicating a significant improvement in the accuracy and reliability of traffic object detection using roadside LIDAR under snowy conditions.

Travel Matrix Enabled Delta-Based Roadside Units Deployment for Vehicular Ad Hoc Networks: A Case of Kigali City

Placements of Road Side Units (RSUs) are an important issue of vehicular networks in urban areas. The merged cost of procurement, installation, and maintenance of intelligent RSUs is high, and therefore, cost-effective deployment strategies are necessary. In this article, we propose a scheme that optimally deploys intelligent roadside units using a travel matrix scheme based on the classical delta strategy where urban vehicles are involved in RSUs communication in the course of their travel times. Four (4) vehicular communication modes are studied, namely, (a) travel matrix based on delta RSUs deployment communication, (b) road intersection-based RSUs deployment communication, (c) road segmentation, and (d) free vehicle-to-vehicle communication. A baseline algorithm is suggested to determine the optimal locations of RSUs in terms of their geographical positions. A travel matrix technique is proposed to trace vehicles’ routeways and travel times in some points of interest (POI). Our intention is to seek an approach that reduces the required number of RSUs and ensures greater network performance effectiveness in terms of packets delivery ratio, throughput, message delay, and jitter; from our study, travel matrix delta-based placement of RSUs becomes the best in our case study scenario. The simulation results indicate that the travel matrix deployment is a suitable deployment scheme in the case study area since it can reduce the number of RSUs while enhancing the vehicular communication abilities under different vehicle density scenarios.

Short-Term Traffic State Prediction Based on Mobile Edge Computing in V2X Communication

Real-time and reliable short-term traffic state prediction is one of the most critical technologies in intelligent transportation systems (ITS). However, the traffic state is generally perceived by single sensor in existing studies, which is difficult to satisfy the requirement of real-time prediction in complex traffic networks. In this paper, a short-term traffic prediction model based on complex neural network is proposed under the environment of vehicle-to-everything (V2X) communication systems. Firstly, a traffic perception system of multi-source sensors based on V2X communication is proposed and designed. A mobile edge computing (MEC)-assisted architecture is then introduced in a V2X network to facilitate perceptual and computational abilities of the system. Moreover, the graph convolutional network (GCN), the gated recurrent unit (GRU), and the soft-attention mechanism are combined to extract spatiotemporal features of traffic state and integrate them for future prediction. Finally, an intelligent roadside test platform is demonstrated for perception and computation of real-time traffic state. The comparison experiments show that the proposed method can significantly improve the prediction accuracy by comparing with the existing neural network models, which consider one of the spatiotemporal features. In particular, for comparison results of the traffic state prediction and the error value of root mean squared error (RMSE) is reduced by 39.53%, which is the greatest reduction in error occurrences by comparing with the GCN and GRU models in 5, 10, 15 and 30 min respectively.

A method of vehicle-infrastructure cooperative perception based vehicle state information fusion using improved kalman filter

For the purpose of overcoming the technical bottlenecks and limitations of autonomous vehicles on the information perception, and improving the sensing range and performance of vehicle driving environment and traffic information, a framework of vehicle-infrastructure cooperative perception for the Cooperative Automated Driving System is proposed in this paper. Taking the vehicle state information as an example, it also introduced a calculation method of data fusion for vehicle-infrastructure cooperative perception. Besides, considering that the intelligent roadside equipment may appear short-term sensing failure, the proposed method improved the traditional Kalman Filter to output position information even when the roadside fails. Compared with the vehicle-only perception, the simulation experiments verified that the proposed method could improve the average positioning accuracy under the normal condition and the intelligent roadside failure by 18% and 19%, respectively. The proposed framework provided a solution for coordinating and fusing perception intelligence and functions between connected automated vehicles, intelligent infrastructure and intelligent control system. The proposed improved Kalman Filter method provides flexible strategies for practical application.

Demonstrations of Cooperative Perception: Safety and Robustness in Connected and Automated Vehicle Operations.

Cooperative perception, or collective perception (CP), is an emerging and promising technology for intelligent transportation systems (ITS). It enables an ITS station (ITS-S) to share its local perception information with others by means of vehicle-to-X (V2X) communication, thereby achieving improved efficiency and safety in road transportation. In this paper, we present our recent progress on the development of a connected and automated vehicle (CAV) and intelligent roadside unit (IRSU). The main contribution of the work lies in investigating and demonstrating the use of CP service within intelligent infrastructure to improve awareness of vulnerable road users (VRU) and thus safety for CAVs in various traffic scenarios. We demonstrate in experiments that a connected vehicle (CV) can “see” a pedestrian around the corners. More importantly, we demonstrate how CAVs can autonomously and safely interact with walking and running pedestrians, relying only on the CP information from the IRSU through vehicle-to-infrastructure (V2I) communication. This is one of the first demonstrations of urban vehicle automation using only CP information. We also address in the paper the handling of collective perception messages (CPMs) received from the IRSU, and passing them through a pipeline of CP information coordinate transformation with uncertainty, multiple road user tracking, and eventually path planning/decision-making within the CAV. The experimental results were obtained with manually driven CV, fully autonomous CAV, and an IRSU retrofitted with vision and laser sensors and a road user tracking system.

Cooperative control strategies to stabilise the freeway mixed traffic stability and improve traffic throughput in an intelligent roadside system environment

Previous studies used an adaptive time gap strategy to stabilise the head-to-tail string stability by connected and automated vehicles (CAVs). However, this strategy can bring an adverse effect on traffic throughput. Moreover, at a lower market penetration rate of CAVs, it is also difficult to implement a larger adaptive time gap to dampen the unstable traffic conditions. To this end, this study proposes cooperative control strategies to improve the mixed traffic throughput and stabilise the head-to-tail string stability in an intelligent road system (IRS) environment. The mixed traffic was composed of autonomous vehicles (AVs) with string instability/stability, and CAVs with low penetration. The feasible stable control parameters of AVs and CAVs are computed and implemented into the cooperative control strategies. The results show that the cooperative control strategies have better performances in improving traffic throughput and stabilising the overall head-to-tail string stability. Moreover, analytical solutions and simulation experiments are conducted to verify the effectiveness of the proposed control strategies subject to exogenous disturbances. The cooperative control strategies can be potentially applied to alleviate the mixed traffic problems, with the advanced connection and automation technologies in an IRS environment.

An Automatic Car Accident Detection Method Based on Cooperative Vehicle Infrastructure Systems

Car accidents cause a large number of deaths and disabilities every day, a certain proportion of which result from untimely treatment and secondary accidents. To some extent, automatic car accident detection can shorten response time of rescue agencies and vehicles around accidents to improve rescue efficiency and traffic safety level. In this paper, we proposed an automatic car accident detection method based on Cooperative Vehicle Infrastructure Systems (CVIS) and machine vision. First of all, a novel image dataset CAD-CVIS is established to improve accuracy of accident detection based on intelligent roadside devices in CVIS. Especially, CAD-CVIS is consisted of various kinds of accident types, weather conditions and accident location, which can improve self-adaptability of accident detection methods among different traffic situations. Secondly, we develop a deep neural network model YOLO-CA based on CAD-CVIS and deep learning algorithms to detect accident. In the model, we utilize Multi-Scale Feature Fusion (MSFF) and loss function with dynamic weights to enhance performance of detecting small objects. Finally, our experiment study evaluates performance of YOLO-CA for detecting car accidents, and the results show that our proposed method can detect car accident in 0.0461 seconds (21.6FPS) with 90.02% average precision (AP). In additionally, we compare YOLO-CA with other object detection models, and the results demonstrate the comprehensive performance improvement on the accuracy and real-time over other models.

Huawei Launches First C-V2X RSU

The intelligent roadside device can support low-latency connected vehicle data broadcasting and exchange over Uu and PC5 interfaces

MODELLING OF DYNAMIC SPEED LIMITS USING THE MODEL PREDICTIVE CONTROL

The article considers the issues of traffic management using intelligent system “Car-Road” (IVHS), which consist of interacting intelligent vehicles (IV) and intelligent roadside controllers. Vehicles are organized in convoy with small distances between them. All vehicles are assumed to be fully automated (throttle control, braking, steering). Proposed approaches for determining speed limits for traffic cars on the motorway using a model predictive control (MPC). The article proposes an approach to dynamic speed limit to minimize the downtime of vehicles in traffic.

Dependable Traffic Control Strategies for Urban and Freeway Networks

Traffic congestion is a growing problem in many countries around the world. It has been recognized that instead of constructing more roads and freeways to counter this problem it is prudent to improve the utilization of existing road network through a judicious combination of advances in control engineering, communication and information technology. The traffic control architecture proposed in this paper is a combination of communicating Urban Traffic Control Architecture (UTCA) and Freeway Traffic Control Architecture (FTCA). The UTCA combines context-awareness, Cyber-Physical Systems (CPS) principles, and Autonomic Computing System (ACS) principles to optimize traffic congestion and enforce safety in urban traffic network. The UTCA includes a network of adaptive intersection traffic controllers and their immediate supervisory systems, who are also networked. The central piece of each traffic controller is an arbiter, which is a mini CPS. It is aware of the traffic dynamics at the intersection managed by it, by virtue of continuous input from monitoring sensors. Due to this context-awareness ability and its communication ability to exchange traffic information with its neighbors, it can execute policy-based reactions in order to enable safe and efficient traffic throughput at its intersection. Each urban traffic supervisory system is designed with ACS principles in order to minimize downtime caused by environmental emergencies and maximize security of the subsystem under it. A supervisory subsystem will also collect global traffic flow information and contextual constraints from its neighbors. Based on this input it will modify policies and communicate them to its traffic controller for timely adaptation. The urban traffic flowing into freeway traffic will be mediated by Intelligent Ramp Meters (IRM). An IRM interacts with the urban traffic control system and its nearest Intelligent Roadside Unit (IRSU) to regulate the flow of traffic from urban to freeway network. The FTCA consists of a network of mutually interacting IRSUs which monitor traffic flow, communicate with IRMs for providing traffic guidance for freeway drivers. An IRSU will communicate with the vehicles in the zone managed by it in order to provide information on rerouting when road and weather conditions warrant it. It also facilitates exchange of information between vehicles, guide them in lane changes and maintaining safe distance in order to avoid collision.

The Optimization Layout Method of Intelligent Roadside Sensor System in Traffic Management and Control

The vehicle sensor information and road side sensor information will be collaborative used in traffic management and control. In order to improve the comprehensiveness and economy of the traffic and road conditions’ information collection, we focus on the intelligent roadside system in this paper. Firstly, we analyse the functions of the intelligent roadside system. Through the analysis of the detection range, detection accuracy, price and applicable conditions of similar sensor, we delineate the selection range of the intelligent roadside sensor. Then we determine the layout scheme of the testing equipment sensors for different functions according to different types of network structure. Finally, we apply similarity analysis to optimize the configuration density to reduce system cost by selecting the sensor layout-intensive sections.