Traffic prediction plays an important role in urban traffic management and signal control optimization. As research in this area advances, traffic prediction has become increasingly accurate. However, the complexity of the traffic system makes the quantification of uncertainty particularly important, as it is influenced by various factors such as weather changes, emergencies and road construction, which lead to the fluctuation and uncertainty of the traffic state. Although some progress has been made in traffic uncertainty quantification, most methods remain primarily focused on individual traffic observation points, with little exploration of the complex spatiotemporal dependencies at the road network level. In light of this situation, this paper proposes a spatiotemporal traffic prediction model based on Bayesian graph convolutional network, which can effectively capture the spatiotemporal dependence in traffic data, facilitating accurate predictions and comprehensive uncertainty

Li, Linfeng, Lin, Limeng

Risk Assessment Model for Highway Accidents Using Bayesian Random-Parameters Regression Model

2025-01-7121

02/21/2025

This paper presents a highway accident risk assessment model based on a Bayesian random-parameters logit model, aiming to evaluate the effects of real-time traffic conditions on crash risks on freeways. By incorporating random parameters to account for variations in the impacts of traffic variables across different freeway segments, the model offers greater flexibility and adaptability compared to traditional fixed-parameters logit models. The study utilizes traffic flow data collected from the Hangzhou-Shanghai-Ningbo expressway over a 14-month period, analyzing factors such as traffic density, average vehicle speed, and lane-changing frequency. The estimation process employs Markov Chain Monte Carlo (MCMC) methods, including Gibbs sampling and Metropolis-Hasting algorithms, to ensure model convergence and stability. Empirical results demonstrate significant impacts of these traffic variables on crash risks and successfully identify key variables with random effects, enhancing the

Feng, Shi, Wang, Zichen, Liu, Shaoweihua, Wang, Feng, Zhang, Yujie, Luo, Xi

Matching and Fusing Multi-Source Vehicle Information in Highway Gantry Scenarios Using Convolutional Neural Networks

2025-01-7136

02/21/2025

The performance differences of multiple sensors lead to inconsistencies, incompleteness, and distortion in the perception data of multi-source vehicle information in highway scenarios. Optimizing data fusion methods is important for intelligent toll collection systems on highways. First, this paper constructs a dataset for matching and fusing multi-source vehicle information in highway gantry scenarios. Second, it develops convolutional neural network models, Match-Pyramid-MVIMF-EGS and CDSSM-MVIMF-EGS, for this purpose. Finally, comparative experiments are conducted based on the constructed dataset to assess the performance of the Match-Pyramid-MVIMF-EGS and CDSSM-MVIMF-EGS models. The experimental results indicate that the Match-Pyramid-MVIMF-EGS model performs better than the CDSSM-MVIMF-EGS model, achieving matching and fusion accuracy of 93.07%, precision of 95.71%, recall of 89.17%, F1 scores of 92.32%, and 186 of training throughput respectively.

Wang, Junjun, Zhao, Chihang

Classification of Pavement Distress Images Using Fusion Convolutional Neural Network of Dual Branch

2025-01-7131

02/21/2025

In recent years, the issue of highway maintenance has become increasingly prominent. How to precisely detect and classify fine cracks and various types of pavement defects on highways through technical means is an essential foundation for achieving intelligent road maintenance. This paper first constructs the DenseNet201-PDC and MobileNetV2-PDC sub-classification networks that incorporate the three-channel attention judgment mechanism MCA. Secondly, based on the principle of parallel connection, a brand-new dual-branch fusion convolutional neural network DBF-PDC capable of classifying pavement defects in highway scenarios is proposed. Finally, this paper builds the Pavement Distress Datasets of Southeast University and conducts relevant ablation experiments. The experimental results demonstrate that both the attention mechanism module and the feature fusion strategy can significantly enhance the network's ability to classify pavement defects in highway scenarios. The average

Zhang, Ziyi, Zhao, Chihang, Shao, Yongjun, Wang, Junjun

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

A Distributed Control Strategy for Highway Merge Zone Based on Virtual Rotation

2025-01-7200

02/21/2025

The merging problem in the highway merge zone has been a research focus in the field of transportation for a long time. The rise of Connected and Automated Vehicles (CAVs) provides the potential to improve traffic flow efficiency, alleviate congestion and handle safety issues. However, existing two-dimensional merging strategies are facing challenges such as high computational complexity and the inevitable traffic oscillations during merging, which hinder the stability of traffic flow and fail to meet the dynamic requirements of merging control. To address these issues, this study proposes a distributed control strategy for CAVs in highway merge scenarios. Firstly, a virtual rotation method is designed to transform the merging problem of two different lanes into a car-following problem of a virtual platoon, and a virtual leader vehicle is introduced, to reduce computational complexity and determine vehicle sequencing. Based on this method, a Spring Cooperative Merging System (SCMS) is

Liu, Yandan, Qu, Xu

A Traffic Conflict Risk Assessment Model for Highway Construction Zones Based on Trajectory Data

2025-01-7217

02/21/2025

Highway construction zones present substantial safety challenges due to their dynamic and unpredictable traffic conditions. With the rising number of highway projects, limited accident data during brief construction phases underscores the need for alternative safety evaluation methods, such as traffic conflict analysis. This study addresses vehicular safety issues within the Kunshan section of the Shanghai-Nanjing Expressway, focusing on conflict risk assessment through a spatio-temporal analysis of a construction zone. Using drone-captured video, vehicle trajectories were extracted to derive key operational indicators, including speed and acceleration, providing a spatio-temporal foundation for analyzing traffic flow and conflict dynamics. A novel **Comprehensive Collision Risk Index (CCRI)** was introduced, integrating Time-to-Distance-to-Collision (TDTC) and Enhanced Time-to-Collision (ETTC) metrics to enable a multidimensional assessment of conflict risk. The CCRI captures both

Zhang, Yuwen, Guo, Xiucheng, Ma, Yuheng

Vehicle Trajectory and Traffic Conflict Prediction at Urban Expressway Confluence

2025-01-7128

02/21/2025

The urban expressway ramp entrance has always been one of the traffic accident prone areas. At present, most of the traffic safety research focuses on the intersection traffic conflict prediction analysis, so it is necessary to conduct related research on the urban expressway ramp entrance traffic safety. In this paper, we focus on trajectory prediction and traffic conflict analysis between straight vehicles and ramp vehicles on main roads for small data sets. Firstly, the transformer model and LSTM model were used to predict the trajectories of the straight vehicle on the main road and the vehicle at the entrance of the ramp respectively. TTC is calculated from the predicted trajectories of straight vehicles on the main road and confluence vehicles on the ramp. Then, the historical features are used to predict the future TTC by using LSTM plus self-attention mechanism, and the two conflict prediction methods are compared. The results show that in the case of small data sets, the

Liu, Tiansheng, Xiang, QiaoJun

Study on the Minimum Diversion Decision Distance of Autonomous Vehicle on Freeway Based on Lane-Changing Trajectory Modelling

2025-01-7170

02/21/2025

Most autonomous vehicles employ a relatively conservative lane-changing strategy in freeway system. In the diversion areas, autonomous vehicles typically initiate lane-changing to curb lanes at lower speeds at a considerable distance from the diversion point, resulting in a decrease in the overall traffic efficiency within the diversion areas. However, lane-changing decision points excessively close to exit ramps can exacerbate the urgency of the lane-changing process, prompting irrationally forced lane-changing and increasing the collision risk. To provide decision-making references for the safe and rapid diverging of autonomous vehicles in freeway diversion areas, this study proposes a minimum diversion decision distance (MDDD) model for autonomous vehicles through microscopic lane-changing trajectory data. Specifically, the lane-changing process was divided into waiting for the acceptable gap stage and executing the lane-changing stage in this model. Subsequently, UAV aerial

Li, ZhenFa, Luo, BaoGuo, Yang, Qi, Chen, Xu, Pan, BingHong

Research on Map Matching Method Based on Road Geometric Features

2025-01-7159

02/21/2025

For the mismatching defects of vertical projection method, this paper proposes an improved map matching algorithm based on road geometric features. For GNSS data, static repeated data is eliminated, dynamic high frequency data is compressed by light bar method. For network map data, extract motorized road segment, break road segment curve at the turning point, and establish network topology relationship. During map matching, determine the candidate road segment through the circular error area, and determine the matching path through the heading angle, connectivity and projection distance, and determine the projection points through the historical trajectory and driving speed. The effectiveness of the proposed algorithm is verified by case study.

Zhang, Hongbin, Zhang, Xu

Deep Learning-Based Road Spill Detection Technology

2025-01-7130

02/21/2025

Rapid identification and cleanup of road debris are essential for enhancing traffic safety and ensuring unobstructed road conditions. Traditional detection methods often face challenges in accurately identifying debris in complex environments with varying light and weather conditions. To address these limitations, this study proposes a deep learning-based road debris detection method designed for improved accuracy and robustness. First, road images are processed using a semantic segmentation approach to remove background information, isolating only the drivable areas. This segmented region is then subjected to further object detection to filter out typical non-debris objects, such as vehicles, pedestrians, and non-motorized vehicles, thereby retaining a focused image that only contains potential debris or spill objects. Lastly, the processed image is compared to a baseline image to detect differences and identify road debris with high precision. Through these steps, the proposed method

Gao, Xiaofei

Remaining Useful Life Prediction Method for Highway Electromechanical Equipment Based on Bayesian Algorithm-Optimized CNN-LSTM Model

2025-01-7149

02/21/2025

Efficient maintenance of highway electromechanical equipment is crucial for ensuring reliability within intelligent highway infrastructure and optimizing the allocation of limited maintenance resources. Traditional Remaining Useful Life (RUL) prediction models frequently face limitations due to the complex and dynamic operating conditions of such systems, which often hinder their predictive accuracy and adaptability. To overcome these persistent challenges, this study introduces an advanced RUL prediction model that integrates a Bayesian-optimized Convolutional Neural Network (CNN) with a Long Short-Term Memory (LSTM) network. Initially, the study identifies key health indicators that effectively represent the degradation of equipment performance over time. These indicators undergo Spearman correlation analysis to determine their relevance to equipment capacity, ensuring that only the most pertinent features are used for model input. The CNN-LSTM model leverages CNN’s spatial pattern

Wang, Leyan, Zhang, Jian, Yao, Xuejian, Ping, Hao

Semantic Segmentation of Lane Lines for Pix2Pix Network Introducing Ghost Module

2025-01-7208

02/21/2025

Technology for lane line semantic segmentation is crucial for ensuring the safe operation of intelligent cars. Intelligent cars can now comprehend the distribution and meaning of scenes in an image more precisely thanks to semantic segmentation, which calls for a certain degree of accuracy and real-time network performance. A lightweight module is selected, and two previous models are improved and fused to create the lane line detection model. Finally, experiments are conducted to confirm the model's efficacy. This paper proposes a lightweight replacement program with the aim of addressing the issue of large parameterization in the generative adversarial network (GAN) model and difficult training convergence. The overall network structure is selected from the Pix2Pix network in the conditional generative adversarial network, and the U-net network of the generator is cut and replaced by the Ghost Module, which consists of a modified downsampling module that enhances the global fusion

Yang, Kun, Wang, Jian

VMD-CNN-BiGRU Model for Vehicle Fuel Consumption Prediction Considering Spatial-Functional Road Characteristics

2025-01-7211

02/21/2025

To accurately predict the fuel consumption of vehicles, this study proposes a vehicle fuel consumption prediction model based on the VMD-CNN-BiGRU algorithm by considering six road spatial features such as road grades, one-way road attributes and intersection attributes. First, the VMD algorithm is employed to reduce the nonlinearity and nonsmoothness of the raw data by determining the optimal number of VMD decomposition modes. Then, the CNN-BiGRU algorithm is used to predict each modal component after decomposition, and the obtained prediction results are compared and analyzed with the prediction results of existing CNN-BiGRU, EMD-CNN-BiGRU and EEMD-CNN-BiGRU models. The results show that the VMD-CNN-BiGRU model significantly outperforms other models in terms of prediction performance and can accurately capture the trend of vehicle fuel consumption, thus effectively verifying the superiority and feasibility of the model. In addition, this study provides an in-depth analysis of the

Gao, Yating, Yan, Lixin, Deng, Guangyang, Chen, Siyuan

Time-Varying Gain-Scheduled Path-Tracking Controller with Delay Compensation (TGDC) for Autonomous Vehicles

10-09-02-0012

02/19/2025

Path-tracking control occupies a critical role within autonomous driving systems, directly reflecting vehicle motion and impacting both safety and user experience. However, the ever-changing vehicle states, road conditions, and delay characteristics of control systems present new challenges to the path tracking of autonomous vehicles, thereby limiting further enhancements in performance. This article introduces a path-tracking controller, time-varying gain-scheduled path-tracking controller with delay compensation (TGDC), which utilizes a linear parameter-varying system and optimal control theory to account for time-varying vehicle states, road conditions, and steering control system delays. Subsequently, a polytopic-based path-tracking model is applied to design the control law, reducing the computational complexity of TGDC. To evaluate the effectiveness and real-time capability of TGDC, it was tested under a series of complex conditions using a hardware-in-the-loop platform. The

Hu, XuePeng, Zhang, Yu, Hu, Yuxuan, Wang, Zhenfeng, Qin, Yechen

Formulation of Power Spectral Density of Fuel Cell Stacks in Different Vehicle Models Based on Road Driving Conditions

2025-01-7083

01/31/2025

The durability of fuel cell vehicle (FCV) has always been one of the key factors affecting its large-scale application. However, the durability test methods of FCV and its key components, fuel cell stack (FCS), are incomplete all over the world, especially the lack of vibration test method on FCV. Focused on the FCS, this paper collects the road load spectrum of different vehicle models in their typical working conditions, so as to obtain the power spectral density of FCS of different vehicle models, which is used as the input signal of durability test. Through the FCS testing and analysis of fuel cell passenger car, bus, tractor and cargo van, the results show that the vibration intensity in three directions of FCS of different models is basically less than that of power battery, and only the FCS of fuel cell bus is greater than that of power battery in the direction of vehicle travel.

Wang, Guozhuo, Wu, Zhen, Guo, Ting, Wu, Shiyu, Liang, Rongliang, Nie, Zhenyu

A Novel Approach for Integrating Virtual Reality in the Ergonomic Testing of Automotive Interior Designs

2024-01-5243

01/28/2025

This research aimed to explore the integration of Virtual reality technology in ergonomically testing automotive interior designs. This objective was aimed at ensuring that such technology could be used to ameliorate user comfort through controlled simulations. Existing ergonomic testing methods are often limited when it comes to recreating actual driving situations and quickly repeating design improvements. VR could be used as a solution because its ergonomically tested simulation can be used to provide users with the real experience of driving. The users can be observed while they experience it and asked for their feedback. For this research, an interactive VR environment imitating a 10-minute-long trip through traffic and changing road conditions was created. It was populated by ten users, concatenated equally in men and women, both aged 20-35, representing approximate demographics of workers in the automotive production industry. Participants of the research were asked to use

Natrayan, L., Kaliappan, Seeniappan, Swamy Nadh, V., Maranan, Ramya, Balaji, V.

Campus Bus Station Layout Planning in Mountainous Universities Based on User Needs

13-06-01-0006

12/13/2024

The planning of mountain campus bus routes needs to take into account user demand, convenience, and other factors. This study adopts a comprehensive research method that combines quantitative and qualitative viewpoints. From the perspective of university students, this article studies the demand of campus public transportation and proposes the layout of campus bus routes in mountainous universities to meet the needs of users. The psychological needs questionnaire was used to investigate college students’ expectation of bus station service function. Taking three mountain universities as examples, the integration and selectivity of campus road networks are evaluated by using space syntax analysis, which provides valuable insights into the quality of bus stop areas. This article discusses the correlation between psychological needs assessment of college students and objective conditions of campus road network. The study concludes with the following findings: (1) The pedestrian environment

Duan, Ran, Tang, Rui, Wang, Zhigang, Zhao, Yixue, Wang, Qida, Yang, Jiyi, Su, Jiafu

Design and Development of Steering based Adaptive Headlight System

2024-28-0272

12/05/2024

Driving at night presents a myriad of challenges, with one of the most significant being visibility, especially on curved roads. Despite the fact that only a quarter of driving occurs at night, research indicates that over half of driving accidents happen during this period. This alarming statistic underscores the urgent need for improved illumination solutions, particularly on curved roads, to enhance driver visibility and consequently, safety. Conventional headlamp systems, while effective in many scenarios, often fall short in adequately illuminating curved roads, thereby exacerbating the risk of accidents during nighttime driving. In response to this critical issue, considerable efforts have been directed towards the development of alternative technologies, chief among them being Adaptive Front Lighting Systems (AFS). The primary objective of this endeavor is to design and construct a prototype AFS that can seamlessly integrate into existing fixed headlamp systems. Throughout the

T, Karthi, G, Manikandan, P C, Murugan, S, Sakthivel, N, Vinu, P, Dineshkumar

Evaluation of Dynamic Load Coming on E-Driveline Due to Bump and Study its Effect on Motor Mountings and Battery Position

2024-28-0171

12/05/2024

During the development of E-Driveline, it observed that loading failure encountered with On-highway vehicle’s E-Drivelines has increased in comparison with traditional driveline. The major cause of these failures is motor and battery reaction loads acting on driveline in electric vehicles. The main source of load generation is acute dynamic reaction coming from road conditions i.e., bumps, potholes, ditch, uneven surfaces, and it transferred to motor or batteries through the E-Driveline. The uneven distribution of motor and battery loads in vehicle will amplify the dynamic reactions which may lead to severe failures. It will be useful if we predict the dynamic loads in early design and simulation stage for accurate solution. This work is based on development of multi body dynamic modeling and simulation approach to predict loads coming on to E-Driveline due to road conditions and correlate it with test setup of actual vehicle running on these road conditions. After the correlation

Deshmukh, Shardul, Nikam, Vinod

Crash Simulation of Steel-Wood Hybrid Road Safety Barrier with Perforated Post Design: TB 11 Evaluation

2024-28-0162

12/05/2024

Research areas in Road furniture have become critical due to the rising incidence of road accidents and fatalities. Enhancing road attributes such as crash barriers, crash cushions, crash poles, and emergency communication systems can significantly reduce these fatalities. Among these, crash barriers promise particular attention as they serve as immediate safety mechanisms. When a vehicle loses control, crash barriers can effectively mitigate the severity of accidents by restraining the vehicle and preventing more severe outcomes. This paper focuses on the performance of a novel steel-wood hybrid crash barrier with perforated post parallel to vehicles direction, designed to enhance road safety in hilly areas. Utilizing finite element analysis (FEA) in LS-DYNA software, renowned for simulating structural deformation under loading, we evaluated the structural response and crashworthiness of the hybrid barrier under various impact scenarios. Our simulations assessed the barrier's

Bendre, Sagar, Das, Alakendu, Jaiswal, Manish

Reliability Assessment of Audio Generating Devices- Horn

2024-28-0178

12/05/2024

The increase in vehicular traffic on Indian roads has led to a significant rise in the frequency of horn usage, particularly in city driving conditions and during peak traffic hours. Existing electro-mechanical horns are designed to have a mission life of 100,000 cycles according to Indian standards IS 1884 [1]. However, the intensified usage patterns have prompted a re-evaluation of the efficacy of these requirements. Studies reveal that the average horn blow frequency for normal usage vehicles is approximately three times per kilometer. When extrapolated to various usage categories, such as public transport and privately owned vehicles, observed increase in average horn blowing frequency per kilometer. When extrapolated, this corresponds to more than 4 lakhs cycles for a vehicle mission life of 2.5 lakhs kilometers. This insight drives the need to review and update validation test specifications to better align with customer usage patterns, thereby enhancing component reliability. By

Joshi, Vivek S., Jape, Akshay

Shell's Starship 3.0 NG spotlights efficient powertrain, aero tech

24TOFHP12_07

12/01/2024

Innovation often comes a piece at a time, but what happens when you put all those pieces together at once? That is precisely the question Shell is attempting to answer with its Starship initiative. Now in its third iteration, Starship 3.0 Natural Gas continues pushing the boundaries of efficiency and emissions reduction by employing all currently available technologies and engineering advancements. The Shell Starship initiative was first launched in 2018 with the aim of setting new benchmarks for the commercial road transport sector. The Starship 2.0 managed 254 ton-miles per gallon for freight ton efficiency (FTE), which is 3.5 times the North American average. Additionally, Starship 2.0 recorded fuel consumption of 10.8 mpg on a cross-country run, which according to Shell is nearly double the current fleet average in North America.

Wolfe, Matt

Research on Multi-Mode Switching Strategy for Electromagnetic Suspension

2024-01-7005

11/15/2024

This study proposes a multi-mode switching control strategy based on electromagnetic damper suspension (EMDS) to address the different performance requirements of suspension systems on variable road surfaces. The working modes of EMDS are divided into semi-active damping mode and energy harvest mode, and the proposed mode switching threshold is the weighted root mean square value of acceleration. For the semi-active damping mode, a controller based on LQR(Linear Quadratic Regulator) was designed, and a variable resistance circuit was also designed to meet the requirements of the semi-active mode, which optimized the damping effect relative to passive suspension. For the energy harvest mode, an energy harvest circuit was designed to recover vibration energy. In order to reduce the deterioration of suspension performance caused by frequent mode switching in the mode switching strategy, as frequent system switching can lead to system disorder, deterioration of damping effect, and

Zeng, Sheng, Zhang, Bangji, Tan, Bohuan, Qin, An, Lai, Jiewen, Wang, Shichen

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

Minimum Requirements for Road Geometry and Attributes Definition™ ASN File

J2945/AA_202410 (Current)10/25/2024

This Abstract Syntax Notation (ASN.1) file precisely define the structure of the data used to implement applications conformant to the SAE J2945/A Recommended Practice. Using the ASN.1 specification, a compiler tool can be used to produce encodings using encoding rules identified in J2945/A. Both this ASN.1 file and the SAE J2735 ASN.1 files are necessary to collectively implement the data structures described in the J2945/A. The combined library can be used by any receiving application (along with the additional logic of the application).

V2X Core Technical Committee

Minimum Requirements for Road Geometry and Attributes Definition™ SET File

J2945/AS_202410 (Current)10/25/2024

Included in this set are the SAE J2945/A Recommended Practice which specifies the minimum requirements for defining various road geometries and select attributes associated with them via the Road Geometry and Attributes (RGA) data set (DS) to support road geometry related motor vehicle safety applications. Contained in this report are a concept of operations, requirements, and design, developed using a detailed systems engineering process. Utilizing the requirements, the RGA DS is defined, which includes the DS Abstract Syntax Notation One (ASN.1) format, data frames, and data element definitions. Also included is the Abstract Syntax Notation (ASN.1) file which precisely defines the structure of the data used to implement applications conformant to the SAE J2945/A Recommended Practice. Using the ASN.1 specification, a compiler tool can be used to produce encodings using encoding rules identified in J2945/A. Both this ASN.1 file and the SAE J2735 ASN.1 files are necessary to

V2X Communications Steering Committee

Minimum Requirements for Road Geometry and Attributes Definition

J2945/A_202410 (Current)10/25/2024

This report specifies the minimum requirements for the Road Geometry and Attributes (RGA) data set (DS) to support road geometry related motor vehicle safety applications. Contained in this report are a concept of operations, requirements, and design, developed using a detailed systems engineering process. Utilizing the requirements, the RGA DS is defined, which includes the DS Abstract Syntax Notation One (ASN.1) format, data frames, and data element definitions. The requirements are intended to enable the exchange of the messages and their DS information to provide the desired interoperability and data integrity to support the applications considered within this report, as well as other applications which may be able to utilize the DS information. System requirements beyond this are outside the scope of this report. NOTE: Unless otherwise marked as Informative, all material in this report is to be considered normative.

V2X Core Technical Committee

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