Browse Topic: Intelligent transportation systems

Items (438)

Applying Real World Traffic Data on Machine Learning Methods for Graph Based Cooperation Strategies of Automated Vehicles

2025-01-0286To be published on 07/02/2025

With the increasing distribution of smart mobility systems, automated & connected vehicles are more and more interacting with each other and with smart infrastructure using V2X-communication. Hereby, the vehicles' position, driving dynamics data, or driving intention are exchanged. Previous research has explored graph-based cooperation strategies for automated vehicles in mixed traffic environments based on current V2X-communication standards. Thereby, the focus is set on cooperation optimization and maneuver negotiation. These strategies can be implemented through both centralized and decentralized computational approaches and are conflict-free by design. To enhance these previously established cooperation models, real-world traffic data is used to derive vehicle trajectories, providing a more accurate representation of actual traffic scenarios in order to enhance the practical application of the described methodology. Additionally, machine learning algorithms are employed to train

Flormann, Maximilian, Meyer, Felix, Henze, Roman

DISRUPT - Decentralized Intelligent System for Road User Prediction and Tracking

2025-01-0294To be published on 07/02/2025

We present DISRUPT, a research project to develop a cooperative traffic perception and prediction system based on networked infrastructure and vehicle sensors. Decentralized tracking and prediction algorithms are used to estimate the dynamic state of road users and predict their state in the near future. Compared to centralized approaches, which currently dominate traffic perception, decentralized algorithms offer advantages such as greater flexibility, robustness and scalability. Mobile sensor boxes are used as infrastructure sensors and the locally calculated status estimates are communicated in such a way that they can augment local estimates from other sensor boxes or vehicles. In addition, the information is transferred to a cloud that collects the local estimates and provides traffic visualization functionalities. The prediction module then calculates the future dynamic state based on neurocognitive behavior and a measure of a road user's risk of being involved in dangerous

Beutenmüller, Frank, Brostek, Lukas, Doberstein, Christian, Han, Longfei, Kefferpütz, Klaus, Obstbaum, Martin, Pawlowski, Antonia, Rössert, Christian, Sas-Brunschier, Lucas, Schön, Thilo, Sichermann, Jörg

Leveraging Advanced Data Technologies for Smart Traffic Management

EPR2025008

05/20/2025

The transportation industry is transforming with the integration of advanced data technologies, edge devices, and artificial intelligence (AI). Intelligent transportation systems (ITS) are pivotal in optimizing traffic flow and safety. Central to this are transportation management centers, which manage transportation systems, traffic flow, and incident responses. Leveraging Advanced Data Technologies for Smart Traffic Management explores emerging trends in transportation data, focusing on data collection, aggregation, and sharing. Effective data management, AI application, and secure data sharing are crucial for optimizing operations. Integrating edge devices with existing systems presents challenges impacting security, cost, and efficiency. Ultimately, AI in transportation offers significant opportunities to predict and manage traffic conditions. AI-driven tools analyze historical data and current conditions to forecast future events. The importance of multidisciplinary approaches and

Ercisli, Safak

V2P-Based Speed Guidance Method for Avoiding Vehicle–Pedestrian Collisions at Intersections

12-09-01-0006

05/14/2025

Conflicts between vehicles and pedestrians at unsignalized intersections occur frequently and often result in serious consequences. In order to alleviate traffic flow congestion at unsignalized intersections caused by accidents, reduce vehicle congestion time and waiting time, and improve intersection safety as well as intersection access efficiency, a speed guidance algorithm based on pedestrian-to-vehicle (P2V) and vehicle-to-pedestrian (V2P) communication technologies is proposed. The method considers the heading angle (direction of motion) of vehicles and pedestrians and combines the post encroachment time (PET) and time to collision (TTC) to determine whether there is a risk of collision, so as to guide the speed of vehicles. Network simulator NS3 and traffic flow simulation software SUMO are used to verify the effectiveness of the speed guidance strategy proposed in this article. The experimental findings demonstrate that the speed guidance strategy introduced in this article

Sun, Yuanyuan, Wang, Kan, Liu, Weizhen, Li, Wenli

Variable Speed Limit Control Based on Deep Deterministic Policy Gradient under Mixed Traffic Environment

12-09-01-0004

05/06/2025

The existing variable speed limit (VSL) control strategies rely on variable message signs, leading to slow response times and sensitivity to driver compliance. These methods struggle to adapt to environments where both connected automated vehicles (CAVs) and manual vehicles coexist. This article proposes a VSL control strategy using the deep deterministic policy gradient (DDPG) algorithm to optimize travel time, reduce collision risks, and minimize energy consumption. The algorithm leverages real-time traffic data and prior speed limits to generate new control actions. A reward function is designed within a DDPG-based actor-critic framework to determine optimal speed limits. The proposed strategy was tested in two scenarios and compared against no-control, rule-based control, and DDQN-based control methods. The simulation results indicate that the proposed control strategy outperforms existing approaches in terms of improving TTS (total time spent), enhancing the throughput efficiency

Ding, Xibin, Zhang, Zhaolei, Liu, Zhizhen, Tang, Feng

Letter from the Guest Editors

12-08-02-0011

05/06/2025

Letter from the Guest Editors

Liang, Ci, Törngren, Martin

Objectifying Visual Perception for Automotive Screens through Vibration Targets

2025-01-0043

05/05/2025

New mobility concepts with smart infrastructure have led to enhanced customer driving experience. The potential to develop safe cars with minimal driver intervention is a great need of the future. The cusp for fully autonomous driving has produced much technical talk, which has led to faster transition and adoption. One of the features that global OEMs have tried to focus on, is Human Machine Interface (HMI) solutions, popularly called display screens. The touchscreen HMIs are common in all mid-range budget cars. They offer driver support beyond just streaming music, including inputs for navigation, parking assistance, in-car technologies, Advanced Driver Assistance Systems (ADAS), and infotainment. Poor display screen visibility is a phenomenon observed when a vehicle is driven over different road surfaces. This paper presents a user-centric approach for the right design & development of the HMI for a vibration free driving experience. The mounting strategies for the display screens

Adil, MD Shahzad, C M, Mithun, Mohammed, Riyazuddin, R, Prasath

A Comprehensive Review of Vehicle and Road Condition Estimation Techniques

10-09-02-0014

04/30/2025

This article reviews the key physical parameters that need to be estimated and identified during vehicle operation, focusing on two key areas: vehicle state estimation and road condition identification. In the vehicle state estimation section, parameters such as longitudinal vehicle speed, sideslip angle, and roll angle are discussed, which are critical for accurately monitoring road conditions and implementing advanced vehicle control systems. On the other hand, the road condition identification section focuses on methods for estimating the tire–road friction coefficient (TRFC), road roughness, and road gradient. The article first reviews a variety of methods for estimating TRFC, ranging from direct sensor measurements to complex models based on vehicle dynamics. Regarding road roughness estimation, the article analyzes traditional methods and emerging data-driven approaches, focusing on their impact on vehicle performance and passenger comfort. In the section on road gradient

Chen, Zixuan, Duan, Yupeng, Wu, Jinglai, Zhang, Yunqing

Infrastructure Enablers for Automation: Vol. 3

EPRCOMPV042024

04/30/2025

Coyner, Kelley, Bittner, Jason

A Novel Model for Short-Term Traffic Flow Prediction on Highways Utilizing Multi-Head Attention–BiLSTM–BiGRU

12-09-01-0002

04/24/2025

With the development of intelligent transportation systems and the increasing demand for transportation, traffic congestion on highways has become more prominent. So accurate short-term traffic flow prediction on these highways is exceedingly crucial. However, because of the complexity, nonlinearity, and randomness of highway traffic flows, short-term prediction of its flows can be difficult to achieve the desired accuracy and robustness. This article presents a novel architectural model that harmoniously fuses bidirectional long–short-term memory (BiLSTM), bidirectional gated recurrent unit (BiGRU), and multi-head attention (MHA) components. Bayesian optimization (BO) is also used to determine the optimal set of hyperparameters. Based on the PeMS04 dataset from California, USA, we evaluated the performance of the proposed model across various prediction intervals and found that it performs best within a 5-min prediction interval. In addition, we have conducted comparison and ablation

Chen, Peng, Wang, Tao, Ma, Changxi, Chen, Jun

Automated Vehicles and Infrastructure Enablers: Dedicated Lanes

EPR2025007

04/24/2025

Dedicated lanes provide a simpler operating environment for ADS-equipped vehicles than those shared with other roadway users including human drivers, pedestrians, and bicycles. This final report in the Automation and Infrastructure series discusses how and when various types of lanes whether general purpose, managed, or specialty lanes might be temporarily or permanently reserved for ADS-equipped vehicles. Though simulations and economic analysis suggest that widespread use of dedicated lanes will not be warranted until market penetration is much higher, some US states and cities are developing such dedicated lanes now for limited use cases and other countries are planning more extensive deployment of dedicated lanes. Automated Vehicles and Infrastructure: Dedicated Lanes includes a review of practices across the US as well as case studies from the EU and UK, the Near East, Japan, Singapore, and Canada. Click here to access the full SAE EDGETM Research Report portfolio.

Coyner, Kelley, Bittner, Jason

Portable Track-Based Connected Intersection Testing System for Connected and Automated Vehicles

2025-01-8062

04/01/2025

Connected and automated vehicle (CAV) technology is a rapidly growing area of research as more automakers strive towards safer and greener roads through its adoption. The addition of sensor suites and vehicle-to-everything (V2X) connectivity gives CAVs an edge on predicting lead vehicle and connected intersection states, allowing them to adjust trajectory and make more fuel-efficient decisions. Optimizing the energy consumption of longitudinal control strategies is a key area of research in the CAV field as a mechanism to reduce the overall energy consumption of vehicles on the road. One such CAV feature is autonomous intersection navigation (AIN) with eco-approach and departure through signalized intersections using vehicle-to-infrastructure (V2I) connectivity. Much existing work on AIN has been tested using model-in-loop (MIL) simulation due to being safer and more accessible than on-vehicle options. To fully validate the functionality and performance of the feature, additional

Hamilton, Kayla, Misra, Priyashraba, Ord, David, Goberville, Nick, Crain, Trevor, Marwadi, Shreekant

Research on Segmented Latency Testing Method for in-Vehicle Real-Time Systems Based on DDS Middleware

2025-01-8083

04/01/2025

With the rapid development of intelligent connected vehicles, their open and interconnected communication characteristics necessitate the use of in-vehicle Ethernet with high bandwidth, real-time performance, and reliability. DDS is expected to become the middleware of choice for in-vehicle Ethernet communication. The Data Distribution Service (DDS), provided by the Object Management Group (OMG), is an efficient message middleware based on the publish/subscribe model. It offers high real-time performance, flexibility, reliability, and scalability, showing great potential in service-oriented in-vehicle Ethernet communication. The performance of DDS directly impacts the stable operation of vehicle systems, making accurate evaluation of DDS performance in automotive systems crucial for optimizing system design. This paper proposes a latency decomposition method based on DDS middleware, aiming to break down the overall end-to-end latency into specific delays at each processing stage

Yu, Yanhua, Luo, Feng, Ren, Yi, Hou, Yongping

Impact of Connected and Automated Vehicles on Longitudinal and Lateral Performance of Heterogeneous Traffic Flow in Shared Autonomy on Two-Lane Highways

2025-01-8098

04/01/2025

Intelligent transportation systems and connected and automated vehicles (CAVs) are advancing rapidly, though not yet fully widespread. Consequently, traditional human-driven vehicles (HDVs), CAVs, and human-driven connected and automated vehicles (HD-CAVs) will coexist on roads for the foreseeable future. Simultaneously, car-following behaviors in equilibrium and discretionary lane-changing behaviors make up the most common highway operations, which seriously affect traffic stability, efficiency and safety. Therefore, it’s necessary to analyze the impact of CAV technologies on both longitudinal and lateral performance of heterogeneous traffic flow. This paper extends longitudinal car-following models based on the intelligent driver model and lateral lane-changing models using the quintic polynomial curve to account for different vehicle types, considering human factors and cooperative adaptive cruise control. Then, this paper incorporates CAV penetration rates, shared autonomy rates

Wang, Tianyi, Guo, Qiyuan, He, Chong, Li, Hao, Xu, Yiming, Wang, Yangyang, Jiao, Junfeng

Traffic Event Extraction and Geographization from Natural Language Web Text

2025-01-7213

03/19/2025

Real-time traffic event information is essential for various applications, including travel service improvement, vehicle map updating, and road management decision optimization. With the rapid advancement of Internet, text published from network platforms has become a crucial data source for urban road traffic events due to its strong real-time performance and wide space-time coverage and low acquisition cost. Due to the complexity of massive, multi-source web text and the diversity of spatial scenes in traffic events, current methods are insufficient for accurately and comprehensively extracting and geographizing traffic events in a multi-dimensional, fine-grained manner, resulting in this information cannot be fully and efficiently utilized. Therefore, in this study, we proposed a “data preparation - event extraction - event geographization” framework focused on traffic events, integrating geospatial information to achieve efficient text extraction and spatial representation. First

Hu, Chenyu, Wu, Hangbin, Wei, Chaoxu, Chen, Qianqian, Yue, Han, Huang, Wei, Liu, Chun, Fu, Ting, Wang, Junhua

Background Filtering and Target Detection for Traffic Objects with Roadside LiDAR Point Cloud Data

2025-01-7193

03/19/2025

Roadside perception technology is an essential component of traffic perception technology, primarily relying on various high-performance sensors. Among these, LiDAR stands out as one of the most effective sensors due to its high precision and wide detection range, offering extensive application prospects. This study proposes a voxel density-nearest neighbor background filtering method for roadside LiDAR point cloud data. Firstly, based on the relatively fixed nature of roadside background point clouds, a point cloud filtering method combining voxel density and nearest neighbor is proposed. This method involves voxelizing the point cloud data and using voxel grid density to filter background point clouds, then the results are processed through a neighbor point frame sequence to calculate the average distance of the specified points and compare with a distance threshold to complete accurate background filtering. Secondly, a VGG16-Pointpillars model is proposed, incorporating a CNN

Liu, Zhiyuan, Rui, Yikang

Artificial Intelligence Use Cases for Ground Vehicle Applications

J3312_202502 (Current)

02/25/2025

This SAE Technical Information Report identifies use cases for AI technology applications to ground vehicles and transportation infrastructure. Whenever applicable, functional definitions and noted issues and concerns are provided in consistent with the current industry mobility practices and published peer-reviewed literature.

Artificial Intelligence

Toward a Methodology for Safety—Performance Trade-Off Analysis for Connected Automated Vehicles Supported by a Smart Infrastructure

12-08-02-0020

02/24/2025

Introducing connectivity and collaboration promises to address some of the safety challenges for automated vehicles (AVs), especially in scenarios where occlusions and rule-violating road users pose safety risks and challenges in reconciling performance and safety. This requires establishing new collaborative systems with connected vehicles, off-board perception systems, and a communication network. However, adding connectivity and information sharing not only requires infrastructure investments but also an improved understanding of the design space, the involved trade-offs and new failure modes. We set out to improve the understanding of the relationships between the constituents of a collaborative system to investigate design parameters influencing safety properties and their performance trade-offs. To this end we propose a methodology comprising models, analysis methods, and a software tool for design space exploration regarding the potential for safety enhancements and requirements

Fornaro, Gianfilippo, Törngren, Martin, Gaspar Sánchez, José Manuel

Lightweight Algorithm Optimization of Visual SLAM on Embedded Computing Devices for Intelligent Transportation Systems

2025-01-7125

02/21/2025

In the context of intelligent transportation vehicle perception, embedded computing devices serve as the primary computing platform, facing the challenge of the traditional visual SLAM(Simultaneous Localization and Mapping) framework's high computational demands for environmental feature points. To address issues such as point cloud drift errors in long-term, large-scale road traffic perception tasks and the high mismatch rate of feature point tracking in traffic scenes with numerous dynamic objects, this work proposes an optimized feature point mismatch elimination method for the visual odometry module based on the ORB-SLAM3 framework. Additionally, an efficient visual vector dictionary loading and matching algorithm for repetitive keyframes is designed for the loop closure detection module. In the feature point mismatch elimination calculation of the visual odometry module, a feature confidence index is introduced to eliminate mismatched feature points of dynamic traffic objects

Weichao, Zhang, Shi, Xiaomeng

Maintenance Scheduling Optimization of Urban Non-intrusive Road Intelligent Transportation System Equipment under the Autonomous Driving Environment

2025-01-7148

02/21/2025

The increasing traveling demands are putting higher pressure on urban networks, where the efficient driving modes highly depend on various non-intrusive ITS equipment for interaction, which asks for higher maintenance scheduling plans minimizing network loss. Current studies have researched methodologies with the aspects of deterministic methods and metaheuristic algorithms under different scenarios, but lack the simulation considering maintenance work type, urban traffic characteristics as well as the ITS equipment. This study aims to optimize the maintenance scheduling plan of urban ITS systems by using the genetic algorithm (GA) and Dijkstra algorithm, as well as other judgmental algorithms to minimize traffic delays caused by maintenance activities, and presents a novel method to assess economic losses. A mixed integer programming model is established simulating the real urban network while considering multiple constraints, including the route selection principle, network updating

Pei, Haoyi, Ji, Yanjie, Chen, Ziang

Identification and Quantitative Analysis Technique of Road Obstacle Safety Risks

2025-01-7201

02/21/2025

The transition from manual to autonomous driving introduces new safety challenges, with road obstacles emerging as a prominent threat to driving safety. However, existing research primarily focuses on vehicle-to-vehicle risk assessment, often overlooking the significant risks posed by static or dynamic road obstacles. In this context, developing a system capable of real-time monitoring of road conditions, accurately identifying obstacle positions and characteristics, and assessing their associated risk levels is crucial. To address these gaps, this study proposes a comprehensive process for rapid obstacle identification and risk quantification, composed of three main components: road obstacle event detection and feature extraction, risk quantification and level assessment, and output of warning information and countermeasures. First, a rapid detection method suited for highway scenarios is proposed based on the YOLOv5 model, enabling fast detection and classification of obstacles in

Chen, Tingting, Chen, Leilei, Yu, Wenlu, Chen, Daoxie

Tunnel Traffic Anomaly Detection via Multi-Modal LLMs

2025-01-7129

02/21/2025

Tunnels play a crucial role in urban transportation, yet they frequently encounter various incidents during operation. Manual video inspections and sensor-based systems are inefficient and limited in accurately detecting and addressing these issues. The emergence of artificial intelligence has led to the development of object detection models such as YOLO, which have shown promise in real-time anomaly detection. However, these single-modality models achieve suboptimal results when dealing with complex events. Multi-modal large language models (LLMs) offer a potential solution, with their ability to process and understand information from different modalities. This paper develops a novel tunnel traffic anomaly detection method that combines single-modal models and multi-modal LLMs. The proposed system first employs YOLO for an initial detection round and then utilizes a specially designed LLM with an effective prompt and a data filtering strategy tailored for traffic tunnel scenarios

Liu, Hongyu, Zhou, Ruohan, Bai, Jiayang, Li, Yuanqi

A Cooperative Control Approach of Transit Signal and Trajectory Optimization for Bus Priority at Connected Intersection

2025-01-7196

02/21/2025

This paper proposes a cooperative control of transit signal priority and speed guidance strategy for connected buses at intersections, which aims to reduce travel delays and improve driving comfort. The connected bus could pass the intersection with the green light while minimizing the impact on the social traffic flow or reduce bus waiting time at intersections. The cooperative optimal problem is described as a mixed-integer programming problem. Serval simulation tests are conducted in SUMO platform, which is proved that the total passenger delay is reduced and the average vehicle cumulative queue length at intersections is reduced, and the bus travel efficiency is improved.

Wang, Xiaoliang, Ma, Shufang, Yu, Qin, Song, Wen, Peng, Hongrui, Hu, Yiming

Design of a Ship Path Planning Scheme Incorporating Wind and Current Effects with COLREGS Compliance

2025-01-7177

02/21/2025

Path planning algorithms are critical technologies for intelligent ship systems, as scientifically optimized paths enable safe navigation and efficient avoidance of waterborne obstacles. To address the limitations of current ship path planning models, which often fail to adequately consider the combined effects of wind, current, and the International Regulations for Preventing Collisions at Sea (COLREGS), this study proposes an enhanced path planning method. The method integrates environmental factors, such as wind and current, and COLREGS into an improved Artificial Potential Field(APF) framework. Specifically, the influence of wind and current is modeled as "environmental forces," while the navigation constraints imposed by COLREGS are transformed into virtual obstacles, generating corresponding repulsive forces to refine the algorithm. Simulation experiments conducted under both single-ship and multi-ship scenarios validate the feasibility and effectiveness of the proposed approach

Shangqing, Feng, Jinli, Xiao, Langxiong, Gan, Geng, Chen, Hui, Li, Guanliang, Zhou

Investigating the Effect of Traffic Network Information on the Evolution of Traffic Flow in Intelligent Transportation Systems under Herd Effect

2025-01-7215

02/21/2025

Intelligent transportation has emerged as a critical paradigm in the transportation sector, underscoring the growing significance of digital information. The extent to which travelers comprehend transportation network information fundamentally influences the dynamics of traffic flow evolution. Traditional random user equilibrium models assume that travelers possess knowledge of segment flow information; however, they fail to account for route flow information. To date, research has yet to investigate how travelers’ decision-making behaviors are altered following the acquisition of route flow information. When endowed with such information, travelers frequently demonstrate behaviors influenced by the bandwagon effect, adjusting their routes to conform to the choices of the majority. This behavioral modification disrupts the existing equilibrium, resulting in a continued evolution of traffic flow until a new stable state is achieved. To examine the implications of transportation network

Zhou, Bojian, Yu, Yaofeng, Li, Shihao, Li, Kangjiao

RCS Measurement and Modeling of Typical Traffic Participants Based on Millimeter-Wave Radar for Autonomous Vehicles

2025-01-7199

02/21/2025

At present, 77GHz millimeter-wave (MMW) radar has become a critical sensor in intelligent transportation systems due to its all-weather detection capability, which enables it to resist complex weather and light interference. Radar cross section (RCS) is a significant characteristic of radar, greatly impacting the detection quality of traffic targets across various traffic scenarios. RCS is usually measured in an anechoic chamber to establish a model of the RCS of typical traffic participants. However, due to large target fluctuations and multi-angle scattering centers of targets, representing the RCS characteristics of typical traffic participants with a single point is challenging. Taking global vehicle target (GVT), pedestrian target and cyclist target as examples, this paper proposes a method for measuring and modeling the RCS features of typical traffic participants. For the static RCS features of targets, we measured the RCS of the target under different viewing angles in an

Liu, Tengyu, Shi, Weigang, Tong, Panpan, Bi, Xin

A Car-following Model Considering Acceleration of Multiple Vehicles for Mixed Traffic Flow

2025-01-7195

02/21/2025

The practice of vehicle platooning for managing mixed traffic can greatly enhance safety on the roads, augment overall traffic flow, and boost fuel efficiency, garnering considerable focus in transportation. Existing research on vehicle platoon control of mixed traffic has primarily focused on using the state information of the leading or head vehicle as control input for following vehicles without accounting for the driving variability of Human-driven Vehicles (HDVs), which does not conform to the driving conditions of vehicles in reality. Inspired by this, this paper presents a car-following model for Connected and Automated Vehicles (CAVs) that utilizes communication with multiple preceding vehicles in mixed traffic. The study further investigates the impact of parameters such as the speed and acceleration of preceding vehicles on the car-following behavior of CAVs, as well as the overall effect of different CAV penetration rates on mixed traffic flow. Firstly, a mixed-vehicle

Peng, Fuke, Huang, Xin

Study on Variable Speed Limit Control Strategy of Highway under Intelligent Connected Environment

2025-01-7189

02/21/2025

This paper presents a novel variable speed limit control strategy based on an Improved METANET model aimed at addressing traffic congestion in the bottleneck areas of expressways while considering the impact of an intelligent connected environment. Traffic flow simulation software was employed to compare the outcomes of the traditional variable speed limit model with those derived from the proposed strategy. The results indicated that under three scenarios—main road, ramp, and lane closure—with a 100% penetration rate of intelligent connected vehicles, the average delay for vehicles utilizing the new model decreased by 9.37%, 11.11%, and 7.22%, respectively. This study offers an innovative approach to highway variable speed limits under an intelligent connected environment.

Qi, Tiancheng, Qu, Xinhui, Gu, Haiyan, Sang, Zheming, Ning, Fangyue

Ecological Driving for Connected and Automated Vehicles Considering Driver Acceptance at Signalized Intersection in a Mixed Traffic Flow

2025-01-7123

02/21/2025

This work aims to design an ecological driving strategy for connected and automated vehicles (CAVs) at an isolated signalized intersection in a mixed traffic flow of CAVs and human-driven vehicles (HVs). Actually, from existing experiments and theories, we can obtain that stochasticity of HVs plays a nontrivial role in traffic flow, including the drivers’ driving personality style and the interaction between HV and CAV. To consider the uncertainty of HVs, we propose driver acceptance to describe the interaction between HV and CAV with the increase of CAV market penetration rate (MPR). Then, to estimate the arrival time of CAV accurately, we propose an improved LWR method integrating the vehicle to V2X data and detector data. The problem is formulated as a multi-objective optimization model and solved by NSGA-II. Our study indicates that multi-objective performance benefits depend on inflow rate, the MPR, and the drivers’ acceptance towards CAVs. The results show that traffic efficiency

Wang, Xiaoliang, Ma, Shufang, Yu, Qin, Song, Wen, Peng, Hongrui, Hu, Yiming

Research on Digital Twin Model Construction Method for Smart Highway

2025-01-7175

02/21/2025

To facilitate the construction of a robust transport infrastructure, it is essential to implement a digital transformation of the current highway system. The concept of digital twins, which are virtual replicas of physical assets, offers a novel approach to enhancing the operational efficiency and predictive maintenance capabilities of highway networks. The present study begins with an exhaustive examination of the demand for the smart highway digital twin model, underscoring the necessity for a comprehensive framework that addresses the multifaceted aspects of digital transformation. The framework, as proposed, is composed of six integral components: spatiotemporal data acquisition and processing, multidimensional model development, model integration, application layer construction, model iteration, and model governance. Each element is critical in ensuring the fidelity and utility of the digital twin, which must accurately reflect the dynamic nature of highway systems. The

Zhang, Yawen, Cai, Xianhua

An Integrated Lane Change Control Method for Freeway Segments with Lane Drop in a Connected Mixed Traffic Environment

2025-01-7190

02/21/2025

Segment with lane drops are very important in freeway systems since they are major constrains to traffic flow and safety. The frequency of capacity reductions and higher safety risks is proportional to an increase in lane-changing actions, which worsen traffic congestion, decrease road capacity, and increase the risk of an accident. Traditional traffic management strategies that rely on physical structures and driver’s decision making often fail under such conditions. This paper provides a detailed lane change control strategy specific to freeway segments with lane reduction in the connected and autonomous vehicle (CAV) environment. The strategy combines both centralized and decentralized techniques to improve the vehicle’s lane-changing behavior and density. A cellular transmission model of lane-level is proposed for the centralized control of the linked vehicles based on the ratio of the driver compliance. The model derives the density equation and transforms the lane-changing

Ma, Yuheng, Guo, Xiucheng, Zhang, Yiming, Cao, Jieyu

Application of Key Node Identification Based on Revised Penalized Local Structural Entropy in Traffic Flow Prediction

2025-01-7174

02/21/2025

In intelligent transportation systems (ITS), traffic flow prediction is a necessary tool for effective traffic management. By identifying and extracting key nodes in the network, it is possible to achieve efficient traffic flow prediction of the whole network using “partial” nodes, as the key nodes contain essential information about changes in the state of the traffic network. This paper proposes a key node identification method based on revised penalty local structure entropy (RPLE) for specific traffic networks. This method takes into account the influence of node distance and traffic flow on identifying important nodes within the traffic network. By introducing a modified penalty term and a comprehensive weight, it achieves a certain level of accuracy in traffic flow prediction using data from key nodes in the network. We compared the RPLE method with different key node identification methods and combined it with different prediction models to compare the traffic flow prediction

Shu, Xin, Ran, Bin

Research on Bridge Concrete Crack Damage Prediction Method Based on Deep Learning Temporal Model

2025-01-7126

02/21/2025

Intelligent Structural Health Monitoring (SHM) of bridge is a technology that utilizes advanced sensor technology along with professional bridge engineering knowledge, coupled with machine vision and other intelligent methods for continuously monitoring and evaluating the status of bridge structures. One application of SHM technology for bridges by way of machine learning is in the use of damage detection and quantification. In this way, changes in bridge conditions can be analyzed efficiently and accurately, ensuring stable operational performance throughout the lifecycle of the bridge. However, in the field of damage detection, although machine vision can effectively identify and quantify existing damages, it still lacks accuracy for predicting future damage trends based on real-time data. Such shortfall l may lead to late addressing of potential safety hazards, causing accelerated damage development and threatening structural safety. To tackle this problem, this study designs a deep

Xu, Weidong, Cai, C.S., Xiong, Wen, Zhu, Yanjie

CogniGuardianRoadscape: Advancing Safety through AI-Driven Roadway Audits

2025-28-0019

02/07/2025

The escalation of road infrastructure anomalies, such as speed breakers and potholes, presents a formidable challenge to vehicular safety, efficient traffic management, and road maintenance strategies worldwide. In addressing these pervasive issues, this paper proposes an advanced, integrated approach for the detection and classification of speed breakers and potholes. Utilizing a sophisticated blend of deep learning methodologies and enhanced image processing techniques, our solution leverages Object Detection to analyze and interpret real-time visual data captured through advanced vehicle-mounted camera systems. This research meticulously details the comprehensive process involved in the development of this system, including the acquisition and preprocessing of a vast, varied dataset representative of numerous road types, conditions, and environmental factors. Through rigorous training, testing, and validation phases, the model demonstrates remarkable proficiency in recognizing and

Thangaraju, Shanmuganathan, Nagarajan, Meenakshi, Ganesan, Maragatham, Raja, Selvakumar, Sirotiya, Aviral, Jasrotia, Bhargav

Research on Invalid Target Filtering and Target Tracking Algorithm Optimization in Millimeter-Wave Radar Technology

2025-01-7035

01/31/2025

The application of millimeter-wave radar technology in autonomous driving has become increasingly widespread with the rapid development of intelligent transportation systems. However, millimeter-wave radar is easily affected by environmental noise, multipath reflections, and electromagnetic interference, resulting in a large number of invalid target signals that reduce the system’s detection accuracy and safety. We proposes a method for filtering invalid targets based on interference signal characteristics and an Adaptive Interactive Multiple Model Kalman Filter (IMM-KF) target tracking algorithm. First, we effectively filter out empty targets, ghost targets, and false targets through a threshold method and lifecycle assessment, achieving a filtering rate exceeding 99.8%. Second, the improved Adaptive IMM-KF algorithm, combined with the Hungarian algorithm, associates and tracks multiple targets. The root mean square error (RMSE) of our methods is reduced by 7.07% and 8.05% compared to

Liu, Qi, Song, Kang, Xie, Hui, Meng, Chunyang

Low-Cost Fusion Odometry Algorithm Based on 4D Radar and Pseudo-LiDAR: Bridging the Gap between 4D Radar and Images in 3D Space

2024-01-7031

12/13/2024

Recently, four-dimensional (4D) radar has shown unique advantages in the field of odometry estimation due to its low cost, all-weather use, and dynamic and static recognition. These features complement the performance of monocular cameras, which provide rich information but are easily affected by lighting. However, the construction of deep radar visual odometry faces the following challenges: (1) the 4D radar point cloud is very sparse; (2) due to the penetration ability of 4D radar, it will produce mismatches with pixels when projected onto the image plane. In order to enrich the point cloud information and improve the accuracy of modal correspondence, this paper proposes a low-cost fusion odometry method based on 4D radar and pseudo-LiDAR, 4DRPLO-Net. This method proposes a new framework that uses 4D radar points and pseudo-LiDAR points generated by images to construct odometry, bridging the gap between 4D radar and images in three-dimensional (3D) space. Specifically, the pseudo

Huang, Minqing, Lu, Shouyi, Zhuo, Guirong

A Fuzz Testing Method Based on DDPM for Intelligent Connected Vehicles CAN Communication

2024-01-7044

12/13/2024

Modern vehicles are increasingly integrating electronic control units (ECUs), enhancing their intelligence but also amplifying potential security threats. Vehicle network security testing is crucial for ensuring the safety of passengers and vehicles. ECUs communicate via the in-vehicle network, adhering to the Controller Area Network (CAN) bus protocol. Due to its exposed interfaces, lack of data encryption, and absence of identity authentication, the CAN network is susceptible to exploitation by attackers. Fuzz testing is a critical technique for uncovering vulnerabilities in CAN network. However, existing fuzz testing methods primarily generate message randomly, lacking learning from the data, which results in numerous ineffective test cases, affecting the efficiency of fuzz testing. To improve the effectiveness and specificity of testing, understanding of the CAN message format is essential. However, the communication matrix of CAN messages is proprietary to the Original Equipment

Shen, Lin, Xiu, Jiapeng, Zhang, Zhuopeng, Yang, Zhengqiu

Group Intelligent Decision Optimization Based on Fireworks Algorithm

2024-01-7047

12/13/2024

This study delves into the application of the fireworks algorithm (FWA) based on swarm intelligence decision in multi-device resource scheduling. By simulating the process of fireworks explosion, this algorithm efficiently searches for global optimal solutions, demonstrating good stability and optimization performance. In comparison to traditional heuristic algorithms, FWA shows advantages such as simplicity, local coverage, and robustness when addressing multi-device resource scheduling issues. Through experimental validation and result analysis, we conclude that the resource optimization model based on FWA exhibits significant superiority in multi-device resource scheduling, enabling faster identification of global optimal solutions and maintaining consistent optimization outcomes. Moreover, FWA displays high robustness and is applicable to various types of resource scheduling problems, particularly excelling in multi-device collaborative scenarios. In summary, this research presents

Chen, Wangjie, Li, Wenlong, Zhu, Weiqiang, Shi, Songhua, Zhou, Mingyu, Fan, Zhenhong

Diagnostics of Automotive Service-Oriented Architectures with SOVD

2024-01-7036

12/13/2024

The term Software-Defined Vehicle (SDV) describes the vision of software-driven automotive development, where new features, such as improved autonomous driving, are added through software updates. Groups like SOAFEE advocate cloud-native approaches – i.e., service-oriented architectures and distributed workloads – in vehicles. However, monitoring and diagnosing such vehicle architectures remain largely unaddressed. ASAM’s SOVD API (ISO 17978) fills this gap by providing a foundation for diagnosing vehicles with service-oriented architectures and connected vehicles based on high-performance computing units (HPCs). For service-oriented architectures, aspects like the execution environment, service orchestration, functionalities, dependencies, and execution times must be diagnosable. Since SDVs depend on cloud services, diagnostic functionality must extend beyond the vehicle to include the cloud for identifying the root cause of a malfunction. Due to SDVs’ dynamic nature, vehicle systems

Boehlen, Boris, Fischer, Diana, Wang, Jue

Unified Multi-Modal Multi-Agent Cooperative Perception Framework for Intelligent Transportation Systems

2024-01-7028

12/13/2024

Cooperative perception has attracted wide attention given its capability to leverage shared information across connected automated vehicles (CAVs) and smart infrastructure to address the occlusion and sensing range limitation issues. To date, existing research is mainly focused on prototyping cooperative perception using only one type of sensor such as LiDAR and camera. In such cases, the performance of cooperative perception is constrained by individual sensor limitations. To exploit the multi-modality of sensors to further improve distant object detection accuracy, in this paper, we propose a unified multi-modal multi-agent cooperative perception framework that integrates camera and LiDAR data to enhance perception performance in intelligent transportation systems. By leveraging the complementary strengths of LiDAR and camera sensors, our framework utilizes the geometry information from LiDAR and the semantic information from cameras to achieve an accurate cooperative perception

Meng, Zonglin, Xia, Xin, Zheng, Zhaoliang, Gao, Letian, Liu, Wei, Zhu, Jiaqi, Ma, Jiaqi

SAE J3016 as a Learning Device for the Driving Automation Community: Technical, Socio-technical, and Systemic Learning

EPR2024029

12/12/2024

It is the tenth anniversary of SAE International’s Surface Vehicle Recommended Practice effort SAE J3016 to establish a nomenclature standard for driving automation systems and levels of automation. While not exhaustive, this report covers motivation, initiation, and continued development of J3016 regarding driving automation systems, noting that J3016 evolved as a learning device that facilitated the evolution of driving automation systems. It initially worked by establishing common terminology for the technical learning in the field, but over time, J3016 expanded to recognize the human roles in driving automation systems, with later iterations considering broader transportation ecosystems, including fleet operations and remote assistance centers. SAE J3016 as a Learning Device for the Driving Automation Community: Technical, Socio-technical, and Systemic Learning emphasizes ongoing learning to integrate diverse insights about technical, social, and socio-technical challenges of

Eley IV, T.C., King, John L., Lyytinen, Kalle, Nickerson, Jeffrey V.

Self-Organisation Theory Based Trajectory Optimisation Method for CAVs in Diverging Area

2024-01-7007

11/15/2024

The highway diverging area is a crucial zone for highway traffic management. This study proposes an evaluation method for traffic flow operations in the diverging area within an Intelligent and Connected Environment (ICE), where the application of Connected and Automated Vehicles (CAVs) provides essential technical support. The diverging area is first divided into three road sections, and a discrete state transition model is constructed based on the discrete dynamic traffic flow model of these sections to represent traffic flow operations in the diverging area under ICE conditions. Next, an evaluation method for the self-organization degree of traffic flow is developed using the Extended Entropy Chaos Degree (EECD) and the discrete state transition model. Utilizing this evaluation method and the Deep Q-Network (DQN) algorithm, a short-term vehicle behavior optimization method is proposed, which, when applied continuously, leads to a vehicle trajectory optimization method for the

Fang, Zhaodong, Qian, Pinzheng, Su, Kaichun, Qian, Yu, Leng, Xiqiao, Zhang, Jian

CPTPS: A Cyber-Physical Trajectory Planning System for Trajectory Planning of Autonomous Vehicles in Unstructured Space

2024-01-7025

11/15/2024

Internet of vehicles (IoV) system as a typical application scenario of smart city, trajectory planning is one of the key technologies of the system. However, there are some unstructured spaces such as road shoulders and slopes pose challenges for trajectory planning of connected-automated vehicle (CAV). Therefore, this paper addresses the problem of CAV trajectory planning affected by unstructured space. Firstly, based on cyber-physical system (CPS), the cyber-physical trajectory planning system (CPTPS) framework was built. A high-precision digital twin CAV is established based on the physical properties and geometric constraints of CAV, and the digital model is mapped to cyber space of the CPTPS. In order to further reduce the energy consumption of the CAV during driving and the time spent from the start to the end, a model was established. Further, based on the sand cat swarm hybrid particle swarm optimization algorithm (SCSHPSO), global path planning for connected-automated vehicles

Ma, Shizi, Ma, Zhitao, Shi, Ying, Yang, Zhongkai, Lai, Daoyin, Qi, Zhiguo

Surrogate Basic Safety Messages for Testing and Evaluation Purposes Only

J2945/8_202411 (Current)11/06/2024

This document includes the initial recommendations for a V2X-equipped host vehicle (eHV) or Roadside Equipment (RSE) that can broadcast a surrogate basic safety message (SBSM) on behalf of an unequipped remote vehicle (uRV). This document is published for testing only, using a Test PSID (e.g., 0x7f) and not the PSID used by SAE J2945/1 and SAE J3161/1 for standard basic safety message (BSM) transmission (0x20). The basic principle is that the host system of the V2X capable device, whether on a host vehicle or an RSE, can determine whether detected vehicles are V2X enabled or not, and if not, formulate and broadcast messages on their behalf that describe their status to other V2X enabled devices. This document is based extensively on the V2V safety communication requirements in SAE J2945/1 and SAE J3161/1 and the supporting message set details in SAE J2735. The initial version of this document is an information report. If feedback from implementations, testing, and simulation becomes

V2X Vehicular Applications Technical Committee

Rating the Surrounding Vehicle-to-Everything Field, Based on Channel Utilization and Information Influence

12-08-03-0025

10/22/2024

Automated vehicles (AVs) can get additional information from infrastructure and other vehicles via vehicle-to-everything (V2X) communication. However, how can an AV decide if the surrounding V2X field can reliably provide qualitative, relevant, and trustworthy information? Related research analyzes V2X performance from various angles. However, not only are there identified open gaps in the analysis of loaded channels, but there has also not yet been an effort to design a lightweight metric for rating the quality of the surrounding V2X field. Hence, this work aims to close this existing performance measurement gap and develop a metric for rating the quality of the surrounding V2X field. This article first highlights the gaps identified in performance analysis before closing them with a dedicated measurement campaign. Next, it combines these findings with related research to design a straightforward V2X field rating metric. The resulting V2X field rating metric is a starting point for

Pilz, Christoph, Kuschnig, Lukas, Steinberger, Alina, Sammer, Peter, Piri, Esa, Couturier, Christophe, Neumayr, Thomas, Schratter, Markus, Steinbauer-Wagner, Gerald

Multi-sensor Fusion-based Vehicle Localization

EPR2024023

10/21/2024

Multi-sensor fusion (MSF) is believed to be a promising tool for vehicular localization in urban environments. Due to the differences in principles and performance of various onboard vehicle sensors, MSF inevitably suffers from heterogeneous sources and vulnerability to cyber-attacks. Therefore, an essential requirement of MSF is the capability of providing a consumer-grade solution that operates in real-time, is accurate, and immune to abnormal conditions with guaranteed performance and quality of service for location-based applications. In other words, an MSF algorithm depends heavily on data synchronization, cost, an accurate process model, a prior knowledge of covariance matrices, integrity assessments, and security against cyber-attacks. Multi-sensor Fusion-based Vehicle Localization addresses trending technologies in MSF-based vehicle localization and outlines some insights into the unsettled issues and their potential solutions. The discussions and outlook are presented as a

Guo, Ge, Liu, Jiageng, Liu, Guangheng

Integration of AI-Powered Vehicles with Smart City Infrastructure to Transform the Future of Automotive World

2024-28-0028

10/17/2024

Artificial Intelligence (AI) has emerged as a transformative force across various industries, revolutionizing processes and enhancing efficiency. In the automotive domain, AI's adaption has ushered in a new era of innovation and driving advancements across manufacturing, safety, and user experience. By leveraging AI technologies, the automotive industry is undergoing a significant transformation that is reshaping the way vehicles are manufactured, operated, and experienced. The benefits of AI-powered vehicles are not limited to their manufacturing, operation, and enhancing the user experience but also by integrating AI-powered vehicles with smart city infrastructure can unlock much more potential of the technology and can offer numerous advantages such as enhanced safety, efficiency, growth, and sustainability. Smart cities aim to create more livable, resilient, and inclusive communities by harnessing innovation through technologies like Internet of Things (IoT), devices, data

Shrimal, Harsh

Integrated Control of Intelligent Vehicle Driving Stability Using Three-Dimensional Phase Space

10-09-01-0002

10/16/2024

To enhance vehicle dynamic stability during driving, we developed a three-dimensional phase space model that incorporates the sideslip angle of center of mass, yaw rate, and lateral load transfer rate. This model enabled real-time evaluation and active control of vehicle stability. First, longitudinal and lateral controllers were implemented to ensure precise vehicle trajectory. Second, a hierarchical control strategy was designed to actively manage the desired sideslip angle, yaw rate, and roll angle based on the vehicle’s destabilizing conditions, thereby maintaining the vehicle within a stable state space. We simulated and tested the stability analysis methods and integrated control strategies for both cars and trucks under DLC (double lane change) and CDC (circular driving condition) scenarios using joint simulations with CarSim/TruckSim and Simulink. The proposed integrated stability control strategy, which combined MPC-based trajectory tracking with direct yaw moment control and

Lai, Fei, Xiao, Hao, Huang, Chaoqun

V2X Communications Message Set Dictionary

J2735_202409 (Current)

09/16/2024

This SAE Standard specifies a message set, and its data frames and data elements, for use by applications that use vehicle-to-everything (V2X) communications systems.

V2X Core Technical Committee

V2X Communications Message Set Dictionary™ SET File

J2735SET_202409 (Current)09/16/2024

This set of Abstract Syntax Notation (ASN.1) modules contains the precise definitions of data structures used in the SAE J2735 standard. Using the ASN.1 specification, a compiler tool can produce libraries that enable software applications to use the data structures and produce corresponding encodings (UPER is typically used to encode J2735 messages). The J2735 Standard specifies a message set, data frames and data elements, specifically for use by V2X applications. Although the scope of the standard is focused on V2X, the message set may also be used in other types wireless communications systems. In some cases the specific requirements for use of messages, data frames and data elements are defined in other standards. Refer to the SAE J2735 standard for more information. You may also be interested in: V2X Communications Message Set Dictionary™ the J2735 PDF. V2X Communications Message Set Dictionary™ J2735 ASN File that includes the ASN file (ZIP format) - download purchase only.

V2X Core Technical Committee

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