Browse Topic: Intelligent transportation systems

Items (415)

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

2025-01-8062To be published on 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 CAV enabled vehicles 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 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

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

2025-01-8083To be published on 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

2025-01-8098To be published on 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 the 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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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™ ASN File

J2735ASN_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™ SET File that includes the J2735 ASN File and the J2735 PDF (ZIP format) - download

V2X Core Technical Committee

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

A Novel Resource-Aware Distributed Cooperative Decision-Making Mechanism for Connected Automated Vehicles

12-07-04-0030

09/06/2024

This article proposes a new model for a cooperative and distributed decision-making mechanism for an ad hoc network of automated vehicles (AVs). The goal of the model is to ensure safety and reduce energy consumption. The use of centralized computation resource is not suitable for scalable cooperative applications, so the proposed solution takes advantage of the onboard computing resources of the vehicle in an intelligent transportation system (ITS). This leads to the introduction of a distributed decision-making mechanism for connected AVs. The proposed mechanism utilizes a novel implementation of the resource-aware and distributed–vector evaluated genetic algorithm (RAD-VEGA) in the vehicular ad hoc network of connected AVs as a solver to collaborative decision-making problems. In the first step, a collaborative decision-making problem is formulated for connected AVs as a multi-objective optimization problem (MOOP), with a focus on energy consumption and collision risk reduction as

Ghahremaninejad, Reza, Bilgen, Semih

An Efficient and Secure Wireless Controller Area Network for Autonomous Vehicle

2024-01-5089

09/02/2024

Controller area network (CAN) buses, the most common intravehicle network (IVN) standard, have been used for over 30 years despite their simple architecture for connecting electronic control units (ECUs). Weight, maintenance costs, mobility promotion, and wired connection complexity increase with ECU count, especially for autonomous vehicles. This paper aims to enhance wired CAN with wireless features for autonomous vehicles (AVs). The proposed solutions include modifying the traditional ECU architecture to become wireless, implementing a hidden communication environment using a unique complementary code keying (CCK) modulation equation and presenting a strategy for dealing with jamming signals using two channels. The proposed wireless CAN (WCAN) is validated using OPNET analysis for performance and reliability. The results show that the bit error rate (BER) and packet loss of the receiver ECU are stable between different CCK modifications, indicating the robustness of the basic

Ibrahim, Qutaiba, Ali, Zeina

A Methodology to Enhance Transparency for Trustworthy Artificial Intelligence for Cooperative, Connected, and Automated Mobility

12-08-01-0010

09/02/2024

In this research, we propose a set of reporting documents to enhance transparency and trust in artificial intelligence (AI) systems for cooperative, connected, and automated mobility (CCAM) applications. By analyzing key documents on ethical guidelines and regulations in AI, such as the Assessment List for Trustworthy AI and the EU AI Act, we extracted considerations regarding transparency requirements. Recognizing the unique characteristics of each AI system and its application sector, we designed a model card tailored for CCAM applications. This was made considering the criteria for achieving trustworthy autonomous vehicles, exposed by the Joint Research Centre (JRC), and including information items that evidence the compliance of the AI system with these ethical aspects and that are also of interest to the different stakeholders. Additionally, we propose an MLOps Card to share information about the infrastructure and tools involved in creating and implementing the AI system.

Cañas, Paola Natalia, Nieto, Marcos, Otaegui, Oihana, Rodriguez, Igor

V2X Misbehavior Reporting™ ASN file

J3287ASN_202408 (Current)08/30/2024

This set of Abstract Syntax Notation (ASN.1) modules contains the precise definitions of data structures used in the SAE J3287 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 using the Octet Encoding Rules (OER). For further information see the body of SAE J3287. You may also be interested in: V2X Misbehavior Reporting™ the J3287 PDF. V2X Misbehavior Reportingt™ SET File that includes the J3287 ASN File and the J3287 PDF (ZIP format) - download purchase only.

V2X Security Technical Committee

V2X Misbehavior Reporting

J3287_202408 (Current)

08/30/2024

This standard provides a specification of a general misbehavior report format suitable for reporting misbehavior observed by a system running SAE V2X applications, and specific report contents for specific instances of misbehavior. It also provides an overview of the architecture of a system-wide misbehavior management service for the V2X system and positions the misbehavior reporting services within that architecture.

V2X Security Technical Committee

V2X Misbehavior Reporting™ SET File

J3287SET_202408 (Current)08/30/2024

This set of Abstract Syntax Notation (ASN.1) modules contains the precise definitions of data structures used in the SAE J3287 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 using the Octet Encoding Rules (OER). For further information see the body of SAE J3287. You may also be interested in: V2X Misbehavior Reporting™ the J3287 PDF. V2X Misbehavior Reporting™ J3287 ASN File that includes the ASN file (ZIP format) - download purchase only.

V2X Security Technical Committee

Next-gen Urban Buses: Autonomy and Connectivity

EPR2024019

08/26/2024

The deployment of autonomous urban buses brings with it the hope of addressing concerns associated with safety and aging drivers. However, issues related autonomous vehicle (AV) positioning and interactions with road users pose challenges to realizing these benefits. This report covers unsettled issues and potential solutions related to the operation of autonomous urban buses, including the crucial need for all-weather localization capabilities to ensure reliable navigation in diverse environmental conditions. Additionally, minimizing the gap between AVs and platforms during designated parking requires precise localization. Next-gen Urban Buses: Autonomy and Connectivity addresses the challenge of predicting the intentions of pedestrians, vehicles, and obstacles for appropriate responses, the detection of traffic police gestures to ensure compliance with traffic signals, and the optimization of traffic performance through urban platooning—including the need for advanced communication

Hsu, Tsung-Ming

Automated Vehicles and Infrastructure Enablers: Cybersecurity

EPR2024018

08/26/2024

While weaponizing automated vehicles (AVs) seems unlikely, cybersecurity breaches may disrupt automated driving systems’ navigation, operation, and safety—especially with the proliferation of vehicle-to-everything (V2X) technologies. The design, maintenance, and management of digital infrastructure, including cloud computing, V2X, and communications, can make the difference in whether AVs can operate and gain consumer and regulator confidence more broadly. Effective cybersecurity standards, physical and digital security practices, and well-thought-out design can provide a layered approach to avoiding and mitigating cyber breaches for advanced driver assistance systems and AVs alike. Addressing cybersecurity may be key to unlocking benefits in safety, reduced emissions, operations, and navigation that rely on external communication with the vehicle. Automated Vehicles and Infrastructure Enablers: Cybersecurity focuses on considerations regarding cybersecurity and AVs from the

Coyner, Kelley, Bittner, Jason

Research on Automated Testing of In-Vehicle Time-Sensitive Network Time Synchronization Mechanism

12-07-04-0027

08/21/2024

With the development of automotive intelligence and networking, the communication architecture of automotive network is evolving toward Ethernet. To improve the real-time performance and reliability of data transmission in traditional Ethernet, time-sensitive network (TSN) has become the development direction of next-generation of automotive networks. The real-time advantage of TSN is based on accurate time synchronization. Therefore, a reliable time synchronization mechanism has become one of the key technologies for the application of automotive Ethernet technology. The protocol used to achieve accurate time synchronization in TSN is IEEE 802.1AS. This protocol defines a time synchronization mechanism suitable for automotive Ethernet. Through the master clock selection algorithm, peer link delay measurement, and clock synchronization and calibration mechanism, the time of each node in the vehicle network is synchronized to a reference master clock. In addition, the protocol clearly

Guo, Yi, Luo, Feng, Wang, Zitong, Gan, Haotian, Wu, Mingzhi, Liu, Hongqian

Firefighting Vehicle Dispatch and Route Planning Model Based on Priority Passage Rights

12-08-02-0014

07/11/2024

Efficient fire rescue operations in urban environments are critical for saving lives and reducing property damage. By utilizing connected vehicle systems (CVS) for firefighting vehicles planning, we can reduce the response time to fires while lowering the operational costs of fire stations. This research presents an innovative nonlinear mixed-integer programming model to enhance fire rescue operations in urban settings. The model focuses on expediting the movement of firefighting vehicles within intricate traffic networks, effectively tackling the complexities associated with collaborative dispatch decisions and optimal path planning for multiple response units. This method is validated using a small-scale traffic network, providing foundational insights into parameter impacts. A case study in Sioux Falls shows its superiority over traditional “nearest dispatch” methods, optimizing both cost and response time significantly. Sensitivity analyses involving clearance speed, clearance time

Wei, Shibo, Gu, Yu, Liu, Han

Graph based Cooperation Strategies for Automated Vehicles in Mixed Traffic

2024-01-2982

07/02/2024

In the context of urban smart mobility, vehicles have to communicate with each other, surrounding infrastructure, and other traffic participants. By using Vehicle2X communication, it is possible to exchange the vehicles’ position, driving dynamics data, or driving intention. This concept yields the use for cooperative driving in urban environments. Based on current V2X-communication standards, a methodology for cooperative driving of automated vehicles in mixed traffic scenarios is presented. Initially, all communication participants communicate their dynamic data and planned trajectory, based on which a prioritization is calculated. Therefore, a decentralized cooperation algorithm is introduced. The approach of this algorithm is that every traffic scenario is translatable to a directed graph, based in which a solution for the cooperation problem is computed via an optimization algorithm. This solution is either computed decentralized by various traffic participants, who share and

Flormann, Maximilian, Henze, Roman

Lane-Changing Behavior’s Impact on Platoon Dispersion in Mixed Autonomous Vehicle Traffic: Insights from Simulation

12-08-02-0013

07/01/2024

This research investigates platoon dispersion characteristics in mixed-traffic flow of autonomous and human-driven vehicles. It presents a cellular automata-based platoon dispersion model. The study’s key findings are as follows: platoon dispersion initially increases and then decreases with the rise in autonomous vehicle proportions. When the autonomous vehicle proportion is approaching 100%, platoon dispersion descends rapidly and is completely eliminated while the proportion is 100%. Compared to platoon consisting entirely of human-driven vehicles, the peak value of standard deviation of vehicle speed is 1.71 times and the travel time drops by 38.19% when the proportion is 1. Moreover, the lane-changing behavior enhances platoon speed, acceleration, and space utilization at micro- and macrolevels by optimizing space resource allocation within the platoon. The study employs a two-lane mixed-flow platoon dispersion model that assumes uniform vehicle characteristics and prioritizes

Lu, Ting, Liu, Chenghao, Lin, Sitong, Song, Wenjing

Cooperative Infrastructure CDA Feature: Cooperative Permissive Left Turn Across Opposing Traffic with Infrastructure Guidance

J3282_202406 (Current)

06/26/2024

This report provides a concept of operations needed to evaluate a CDA Feature for a permissive left turn across opposing traffic, with infrastructure guidance. The Feature uses CDA cooperation levels including status-sharing and agreement-seeking, and a set of test scenarios (functional, logical, and concrete) is developed to evaluate this CDA Feature.

Cooperative Driving Automation(CDA) Committee

Deep Learning Algorithms for Longitudinal Driving Behavior Prediction: A Comparative Analysis of Convolutional Neural Network and Long–Short-Term Memory Models

12-07-04-0025

06/13/2024

In the realm of transportation science, the advent of deep learning has propelled advancements in predicting longitudinal driving behavior. This study explores the application of deep neural network architectures, specifically long–short-term memory (LSTM) and convolutional neural networks (CNNs), recognized for their effectiveness in handling sequential data. Using a 3-s temporal window that includes past vehicle progress, speed, and acceleration, the proposed model, a hybrid LSTM–CNN architecture, predicts the vehicle’s speed and progress for the next 6 s. The approach achieves state-of-the-art performance, particularly within a 4 s horizon, but remains competitive even for longer-term predictions. This is achieved despite the simplicity of its input space, which does not include information about vehicles other than the target vehicle. As a result, while its performance may decrease slightly for longer-term predictions due to the lack of environmental information, it still offers

Lucente, Giovanni, Maarssoe, Mikkel Skov, Kahl, Iris, Schindler, Julian

Facial Recognition Software and Privacy Law in Transportation Technology

EPR2024011

05/07/2024

Data privacy questions are particularly timely in the automotive industry as—now more than ever before—vehicles are collecting and sharing data at great speeds and quantities. Though connectivity and vehicle-to-vehicle technologies are perhaps the most obvious, smart city infrastructure, maintenance, and infotainment systems are also relevant in the data privacy law discourse. Facial Recognition Software and Privacy Law in Transportation Technology considers the current legal landscape of privacy law and the unanswered questions that have surfaced in recent years. A survey of the limited recent federal case law and statutory law, as well as examples of comprehensive state data privacy laws, is included. Perhaps most importantly, this report simplifies the balancing act that manufacturers and consumers are performing by complying with data privacy laws, sharing enough data to maximize safety and convenience, and protecting personal information. Click here to access the full SAE EDGETM

Eastman, Brittany

Vehicle Experience and Human Interaction: Vol. 1

EPRCOMPV172023

05/03/2024

Connected and autonomous vehicles (CAVs) and their productization are a major focus of the automotive and mobility industries as a whole. However, despite significant investments in this technology, CAVs are still at risk of collisions, particularly in unforeseen circumstances or “edge cases.” It is also critical to ensure that redundant environmental data are available to provide additional information for the autonomous driving software stack in case of emergencies. Additionally, vehicle-to-everything (V2X) technologies can be included in discussions on safer autonomous driving design. Recently, there has been a slight increase in interest in the use of responder-to-vehicle (R2V) technology for emergency vehicles, such as ambulances, fire trucks, and police cars. R2V technology allows for the exchange of information between different types of responder vehicles, including CAVs. It can be used in collision avoidance or emergency situations involving CAV responder vehicles. The

Abdul Hamid, Umar Zakir, Roth, Christian, Nickerson, Jeffrey, Lyytinen, Kalle, King, John Leslie

Research on Speed Guidance Strategy at Continuous Signal Intersection Based on Vehicle–Road Coordination Technology

12-07-04-0023

04/13/2024

With the rapid growth of automobile ownership, traffic congestion has become a major concern at intersections. In order to alleviate the blockage of intersection traffic flow caused by signals, reduce the length of vehicle congestion and waiting time, and for improving the intersection access efficiency, therefore, this article proposes a vehicle speed guidance strategy based on the intersection signal change by combining the vehicle–road cooperative technology. The randomness of vehicle traveling speed in the road is being considered. According to the vehicle traveling speed, a speed guidance model is established under different conditions. Finally, the effectiveness of the speed guidance strategy in this article is verified through experimental simulation, and the benefits of the intersection with intelligent control and traditional control are compared, and the experimental results show that the intelligent control method in this article can effectively reduce vehicle congestion and

Li, Wenli, Li, An, Ren, Yongpeng, Wang, Kan

Research on Vehicle Type Recognition Based on Improved YOLOv5 Algorithm

2024-01-1992

04/09/2024

As a key technology of intelligent transportation system, vehicle type recognition plays an important role in ensuring traffic safety,optimizing traffic management and improving traffic efficiency, which provides strong support for the development of modern society and the intelligent construction of traffic system. Aiming at the problems of large number of parameters, low detection efficiency and poor real-time performance in existing vehicle type recognition algorithms, this paper proposes an improved vehicle type recognition algorithm based on YOLOv5. Firstly, the lightweight network model MobileNet-V3 is used to replace the backbone feature extraction network CSPDarknet53 of the YOLOv5 model. The parameter quantity and computational complexity of the model are greatly reduced by replacing the standard convolution with the depthwise separable convolution, and enabled the model to maintain higher accuracy while having faster reasoning speed. Secondly, the attention mechanism in

Liu, XinHong

Trajectory Tracking Control of Unmanned Vehicle Formation Based on Full-Order Sliding Mode

2024-01-1993

04/09/2024

A novel control method based on full-order sliding mode is proposed in this paper to solve the trajectory tracking control problem of unmanned vehicle formation. The complexity of the unmanned vehicle system is considered and a dynamic error model of the system is established . A full-order sliding mode control method is adopted to realize the cooperative control of unmanned vehicle systems. The unmanned vehicle system can force each vehicle accurately track the specified trajectory. The simulation results show that the designed full-order sliding mode control method has excellent performance compared with the traditional linear sliding mode control in terms of accuracy and robustness. In the case of large changes in different types of road surface and vehicle dynamics, the movement of unmanned vehicles is effectively controlled, and the trajectory tracking control of unmanned vehicle formation system is realized.

Zhou, Minghao, Chen, Jiaxin, Cai, Wei, Fei, Xueran

Modelling, Simulation and Testing of Adaptive Sliding Mode Control for Semi-Active Suspension System Based on Road Information

2024-01-2765

04/09/2024

The accuracy of chassis control for intelligent electric vehicles (IEVs), especially in road-based IEVs control for improving road holding and ride comfort, is a challenging task for the intelligent transport system. Due to the high fatality rate caused by inaccurate road-based control algorithms, how to precisely and effectively choose a reasonable road-based control algorithm become a hot topic in both academia and industry. To address and improve the performance of road holding and ride comfort of IEVs by using a semi-active suspension system, an adaptive sliding mode control (ASMC) algorithm-based road information is proposed to realize the overall performance of the intelligent vehicle chassis system in the paper. Firstly, the models of road excitation and equivalent hybrid control of a quarter semi-active suspension system are established. Secondly, connecting with the minimum redundancy maximum relevance (MRMR) approach and probability neural network (PNN) theory, the method of

Wang, Zhenfeng, Liao, Yinsheng, Zhang, Zhijie, Hu, Zhiming, Zhao, Gaoming, Huang, Taishuo, Zhang, Lei

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