This standard will apply primarily to the vehicle classes identified in SAE J3194. It provides a schema for utilizing alphanumeric values to represent identifying information such as the manufacturer or vehicle provider, year of manufacture, model, vehicle type, weight, width, speed, and power source. Although conceptually similar to a Vehicle Identification Number (VIN), this standard does not classify or intend to suggest classification of these vehicles as motor vehicles for regulatory or safety data purposes. The location for placement of these identifiers on the vehicle, type of label, permanence, and visibility are out of scope for this document.

Powered Micromobility Vehicles Committee

Taxonomy and Classification of Powered Micromobility Vehicles

J3194_202502 (Current)

02/25/2025

This technical report provides a taxonomy and classification of powered micromobility vehicles. These vehicles may be privately owned or be available via shared- or rental-fleet operations. This technical report does not provide specifications or otherwise impose minimum safety design requirements for powered micromobility vehicles.

Powered Micromobility Vehicles Committee

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

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

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

Identifying High-Risk Situations in Right-Turn Phase of Heavy-Duty Vehicles Using Naturalistic Driving Data

2025-01-7209

02/21/2025

The interaction between heavy-duty vehicles turning right and non-motor vehicles going straight has led to severe traffic crashes. It is essential to evaluate the driving risk of heavy-duty vehicles in the right-turn phase. Increasingly, studies have explored some indicators associated with driving risk. Based on naturalistic driving data of 121 heavy-duty vehicles in Nanjing, this research combined factor analysis and K-means cluster algorithm to assess the driving risk of two scenarios, one without a blind spot warning and another with a blind spot warning during the right-turn phase. The results have concluded the driving characteristics of heavy-duty vehicles under different risk levels. It formed a set of driving risk level assessment methods for heavy-duty vehicles in the right-turn phase. This evaluation method is expected to identify high-risk right-turn behaviors of heavy-duty vehicles and provide some insights to practitioners for traffic management.

Zhang, Hedi, Fu, Yuanhang, Ma, Yongfeng, Chen, Shuyan

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

Research on Taxiing Path Planning of Hub Airport Based on Dijkstra Algorithm

2025-01-7198

02/21/2025

Aiming at the problem of insufficient capacity of taxiways in hub airports, which combine the safety interval, conflict resolution and fair principles, a taxiway planning model is established by taking the shortest taxiway as the optimisation goal, considering fuel consumption and exhaust emissions. Dijkstra algorithm is used to transform the taxiing path into an adjacency matrix, and conflict resolution is carried out in a weighted way. Under the premise of ensuring zero conflict of taxiways, the total taxiing distance is reduced. Based on actual operational data from a hub airport in China, the results show that the proposed taxiing path planning method is feasible, shortening the aircraft taxiing distance and improving the surface taxiing efficiency.

Feng, Bocheng, Qi, Xinyue, Zhang, Hongbin

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

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

Spatio-Temporal Traffic Forecasting with Uncertainty Quantification via Bayesian Graph Convolution

2025-01-7142

02/21/2025

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

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

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

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

Traffic Flow Simulation Analysis of Mixed Traffic Flow Involving CAVs and Human-Driven Vehicles in Urban Work Zones

2025-01-7187

02/21/2025

With the acceleration of urbanization, developing public transportation is an important means to alleviate travel pressure and traffic congestion in cities. Work zones that occupy urban road resources affect normal vehicle operations, leading to reduced vehicle efficiency. Based on this, the paper conducts research on traffic flow modeling and simulation analysis for work zones in a vehicle-road coordination environment. Based on the Gipps model and the SCAT model, optimizations and improvements were made to the following and lane-changing rules for three types of vehicles: human-driven vehicles (HVs), autonomous and connected vehicles (CAVs), and buses. Using cellular automata theory, it constructs a running model suitable for mixed traffic flow vehicles in work zones. MATLAB software is utilized to simulate the operation process of vehicles under work zone scenarios, analyzing changes in traffic flow from two directions: road geometric conditions (speed limits) and traffic flow

Xie, Rongkai, Sun, Bo, Zheng, Yunchao, Liao, Meixian, Zhang, Wendi, Li, Rui

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

Evaluation Method for the Impacts of Truck Platoons Lateral Control on Pavement Performance

2025-01-7152

02/21/2025

The introduction of autonomous truck platoons is expected to result in drastic changes in operational characteristics of freight shipments, which may in turn have significant impacts on efficiency, energy consumption, and infrastructure durability. Since the lateral positions of autonomous trucks traveling consecutively within a lane are fixed and similar (channelized traffic), such platooning operations are likely to accelerate damage accumulation within pavement structures. To further advance the application of truck platooning technology in various pavement environments, this study develops a flexible evaluation method to evaluate the impact of lateral arrangement within autonomous truck platoons on asphalt pavement performance. This method simplifies the impact of intermittent axle load applications along the driving direction within a platoon, supporting platoon controllers in directly evaluating pavement damage for different platoon configurations. Specifically, a truck platoon

Wenlu, Yu, Ye, Qin, Chen, Daoxie, Min, Yitong, Chen, Leilei

Items per page:

1 – 50 of 4423