Browse Topic: Intelligent transportation systems

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Taxonomy and Definitions for Terms Related to Driving Automation Systems for On-Road Motor VehiclesJ3016_202609 (Current)9/20/2026
This document describes [motor] vehicle driving automation systems that perform part or all of the dynamic driving task (DDT) on a sustained basis. It provides a taxonomy with detailed definitions for levels of driving automation, ranging from no driving automation (Level 0) to automated driving under all conditions in which humans can drive, with human driving not needed (Level 5), in the context of [motor] vehicles (hereafter also referred to as “vehicle” or “vehicles”) and their operation on roadways: Level 0: No driving automation Level 1: Driver support for steering OR speed, with continual driver supervision necessary and driver intervention when needed Level 2: Driver support for steering AND speed, with continual driver supervision necessary and driver intervention when needed Level 3: Automated driving under defined conditions, with human driving needed following an alert or evident vehicle malfunction Level 4: Automated driving under defined conditions, with human driving not needed to mitigate risk Level 5: Automated driving under all conditions in which humans can drive, with human driving not needed. The simple level descriptors have been changed to improve understanding of the differences among levels, but these are NOT the definitions of the levels of driving automation. See the definitions of each automation level in Sections 4 and 5 for explanation of these changes. These level definitions, along with additional supporting terms and definitions provided herein, can be used to describe the full range of driving automation features equipped on [motor] vehicles in a functionally consistent and coherent manner. “On-road” refers to publicly accessible roadways (including parking areas and private campuses that permit public access) that collectively serve all road users, including cyclists, pedestrians, and users of vehicles with and without driving automation features. The levels apply to the driving automation feature(s) that are engaged in any given instance of on-road operation of an equipped vehicle. As such, although a given vehicle may be equipped with a driving automation system that is capable of delivering multiple driving automation features that perform at different levels, the level of driving automation exhibited in any given instance is determined by the feature(s) that are engaged. This document also refers to three primary actors in driving: the (human) user, the driving automation system, and other vehicle systems and components. These other vehicle systems and components (or the vehicle in general terms) do not include the driving automation system in this model, even though as a practical matter a driving automation system may actually share hardware and software components with other vehicle systems, such as a processing module(s) or operating code. The levels of driving automation are defined by reference to the specific role played by each of the three primary actors in performance of the DDT and/or DDT fallback. “Role” in this context refers to the expected role of a given primary actor, based on the design of the driving automation system in question and not necessarily to the actual performance of a given primary actor. For example, a driver who fails to monitor the roadway during engagement of a Level 1 adaptive cruise control (ACC) system still has the role of driver, even while they are neglecting it. Active safety systems, such as electronic stability control (ESC) and automatic emergency braking (AEB), and certain types of driver assistance systems, such as lane keeping assistance (LKA), are excluded from the scope of this driving automation taxonomy because they do not perform part or all of the DDT on a sustained basis, but rather provide momentary intervention during potentially hazardous situations. Due to the momentary nature of the actions of active safety systems, their intervention does not change or eliminate the role of the driver in performing part or all of the DDT, and thus are not considered to be driving automation, even though they perform automated functions. In addition, systems that inform, alert, or warn the driver about hazards in the driving environment are also outside the scope of this driving automation taxonomy, as they neither automate part or all of the DDT, nor change the driver’s role in performance of the DDT (see 8.13). It should be noted, however, that crash avoidance features, including intervention-type active safety systems, may be included in vehicles equipped with driving automation systems at any level. For automated driving system (ADS) features (i.e., Levels 3 to 5) that perform the entire DDT, crash mitigation and avoidance capability is part of ADS functionality (see also 8.13). Note that this document provides a taxonomy and definitions and is not a safety standard. The document is not intended to provide guidance for safe vehicle operation by the driving automation system.
On-Road Automated Driving (ORAD) Committee
Research on the Stability of Three-Axis Vehicles with Coordinated Steering under High-Speed Low-Adhesion Conditions2026-99-11369/11/2026
Three-axle vehicles are widely used in engineering, transportation, and other heavy-duty applications, but they are prone to lateral instability at high speeds or on low-adhesion road conditions, which severely degrades handling stability. To enhance their dynamic performance under extreme operating conditions, this paper proposes a direct yaw-moment control (DYC) strategy based on an incremental linear quadratic regulator (ILQR) for a distributed-drive three-axle vehicle equipped with active front-wheel steering (AFS) and differential drive assist steering (DDAS), thereby improving the accuracy and responsiveness of lateral stability control. Furthermore, to mitigate the mutual coupling and interference among multiple control subsystems, a coordinated steering strategy based on phase-plane analysis is proposed to achieve effective integration and dynamic coordination of AFS, DDAS, and DYC. Co-simulation studies conducted in Matlab/Simulink and TruckSim reveal that the proposed coordinated steering strategy substantially diminishes the peak yaw rate and vehicle sideslip angle across diverse driving conditions, thereby considerably enhancing the lateral stability of the three-axle vehicle during extreme maneuvers.
Hu, Jiadong, Wang, Tie
ASTDGCN: An Adaptive Spatial-Temporal Diffusion Graph Convolutional Network for Traffic Forecasting2026-99-15839/11/2026
Precise traffic flow prediction functions as the fundamental cornerstone for the efficient, safe, and reliable operation of intelligent transportation systems (ITS). It not only provides data-driven support for key applications, for instance, real-time traffic signal regulation, proactive congestion mitigation, and personalized route optimization, but also exerts a critical effect on reducing traffic accidents and improving overall urban travel efficiency. However, the traffic system belongs to a complex system, with spatio-temporal dynamics that are both intricate and variable, ranging from predictable fluctuations during morning and evening peak hours to localized propagation effects caused by accidents, as well as seasonal variations and significant nonlinear characteristics. These factors collectively pose substantial challenges to building accurate and reliable prediction models, creating a long-standing technical bottleneck in this field. With the aim of solving the dilemma that existing methods are hardly able to capture traffic flow’s spatio-temporal dependence effectively, we advance an adaptive spatial–temporal diffusion graph convolutional network (ASTD-GCN) for a traffic flow prediction model that integrates adaptive graph learning, diffusion convolution, and bi-directional long short-term memory network (Bi-LSTM) with attention mechanism. The model dynamically constructs the correlation between the nodes of the transportation network through the adaptive graph learning module and accurately describes the spatial topology. The diffusion convolution module realizes multi-order spatial information diffusion based on graph structure, which realizes the effective extraction of the traffic flow’s spatial dependence features. The Bi-LSTM module incorporating the attention mechanism captures the historical and future context information of traffic flow simultaneously through the bidirectional loop structure and the temporal attention mechanism, and strengthens the key time step features. Experimental results on -world traffic datasets PEMS03, PEMS04, PEMS07, and PEMS08 indicate that our proposed model exhibits better predictive precision in traffic flow forecasting tasks than baseline counterparts.
Li, Sumin, Gao, Yina, Zhu, Hongnian
Vehicle Multi-Target Tracking Algorithm Based on Improved CenterTrack2026-99-11029/11/2026
With the rapid increase in the number of vehicles worldwide in recent years, multi-vehicle tracking has become a critical and challenging research topic in smart transportation. Although many researchers have constructed classical multi-object tracking (MOT) models, these models often lose trajectories of objects in challenging traffic environments with frequent occlusions and high object density. This significantly hinders the practical deployment of such tracking systems. In order to improve tracking robustness and accuracy when faced with these challenges, we propose a new multi-vehicle tracking algorithm built upon the CenterTrack framework. The core of our work lies in three key improvements. Each overcomes a distinct weakness in existing approaches, and together they work to improve performance. First, we use the Wise Intersection over Union (WIoU) loss to guide the model optimization and reduce the impact of label noise during training, which results in better convergence. Second, we apply an attention mechanism to reduce feature interference caused by multiple inputs (current frame, previous frame, and heatmap) of the model. Third, we employ the Sigmoid Linear Unit (SiLU) in the backbone network to further improve nonlinear feature representation. We evaluate our algorithm on the standard KITTI multi-object tracking benchmark. Experimental results show that our method achieves a Multi-Object Tracking Accuracy (MOTA) of 65.94% and an Identification F1 (IDF1) of 84.01%. This represents an improvement of 2.93% in MOTA and 2.36% in IDF1 over the baseline method. The results demonstrate not only the effectiveness of our method but also its practical usefulness and robustness in challenging road environments.
Zhang, Hao, Huai, Chongfei, Wang, Ziming, Zhao, Zexuan, Ye, Shuai, Du, Xiaobing, Sun, Shenghai
An Improved Multi-Object Vehicle Detection Algorithm Based on YOLOv11s2026-99-11299/11/2026
For object detection in complex road situations, such as inadequate detection performance and difficulties caused by vehicle occlusion and cluttered environments, this paper pursues a YOLOv11s-based object detection framework. The algorithm successfully designed a novel PEConv module. This module integrates a partial convolutional network with an efficient multi-head attention mechanism. Through a Split operation, the input image is divided into locally enhanced channels and original channels. The locally enhanced channels undergo partial convolution and feature weight allocation via the efficient multi- head attention mechanism for feature extraction. Finally, these channels are fused with the original channels before undergoing convolution. This approach preserves the original features while minimising feature loss caused by the series of operations. Therefore, the PEConv module is based on a partially convolutional network and efficient multi-head attention. It improves the detection ability by precisely giving more weight to small objects and occluded parts with augmented partial channel attention and original channel fusion. This study further enhances the model’s detection precision and improves its performance in addressing small target vehicles and severe occlusion issues by refining and upgrading the original C3K2 architecture. The LSBlock is integrated into the original model’s bottleneck structure, replacing the traditional 3x3 convolution to create the C3K2 - LSBlock module. Experimental results show that on the UA - DETRAC dataset, compared with the original YOLOv11s, the optimized YOLOv11s has improved the original mAP @ 50 by 3.4%, reaching 61.3%, and improved the original mAP @ 50: 95 by 2%, which verifies the correctness of it.
Chen, Yulin, Wang, Yini, Wang, Jianwei, Zhang, Xin
Intelligent Connected Road Test Brake Throttle Robot Calibration Method2026-99-11209/11/2026
With the development of intelligent connected vehicle (ICV) technology, road testing has become a key guarantee for verifying the safety and reliability of automobiles. The brake pedal robot basically eliminates human differences in complex scenes by simulating human operation. Therefore, the accuracy of the actions performed by these robots directly determines the validity of the test results. However, the current study lacks a uniform calibration standard, resulting in reduced execution accuracy. In order to meet the requirements of precision and a unified standard for the test, this research analyzes the metrological characteristics of brake pedal robot. Based on this, a systematic calibration framework was established to verify key performance parameters. Specifically, the pedal speed is dynamically calibrated using high-precision accelerometers, and pedal force is verified through a dedicated calibration device that integrates standard force sensors. And the pedal space travel is measured using a portable three coordinate articulated arm system. Experimental verification shows that the proposed method can strictly control the pedal speed error within ± 5%, pedal force error within ± 3%, and pedal stroke error within ± 2 mm, fully meeting the requirements of ICV road testing. This study provides a standardized framework and scientific basis for calibration, improving the accuracy and credibility of road test data, thereby supporting safer deployment of intelligent driving systems.
Chen, Xi, Ma, Siyao, Feng, Zhu
Research on High-Reliability Testing Systems for Intelligent and Connected Vehicle2026-99-11119/11/2026
With the large-scale application of intelligent connected vehicles, the verification of their functional safety and reliability has become a core bottleneck in the industrial development. The traditional real- vehicle road test method can no longer meet the current demand for large-scale test verification due to problems such as high cost, low efficiency, and difficulty in reproducing dangerous scenarios. This paper studies the vehicle-in-the-loop simulation test system based on a digital twin. By constructing a virtual scenario highly consistent with the real world, physical-level multi-source perception signals are simulated and mapped to the system under test to enable high- reliability verification of real vehicles. In terms of lateral and longitudinal control functions, multiple sets of test cases are selected respectively for comparison between road tests and virtual simulation tests. The results show that the accuracy of key indicators is above 90%, which provides practical reference for the subsequent test and verification system of high-level autonomous driving.
Hou, Quanshan, Tang, Ke, Gao, Tian, Chen, Tao, Zhou, Si
2025 5th International Conference on Logistics System, Traffic and Transportation (LSTT 2025)2026-99-15009/11/2026
TOC
Tobolski, Sue
Emotional Impacts of Manual and Assisted Driving Modes under Varying Time Pressures: A Scenario-based Simulation Study2026-99-15089/11/2026
Emotion as a critical psychological variable in driving behavior has been extensively confirmed to exert a profound influence on traffic safety. With the advancement of autonomous driving technologies, the application value of emotion regulation mechanisms in intelligent transportation systems is gaining importance, given their proven impact on reducing unsafe driving tendencies and improving human-machine interaction quality. However, systematic research on the mechanisms underlying emotional variation across different driving modes remains relatively scarce. This study developed a questionnaire based on the Chinese version of the Driving Anger Scale (DAS), incorporating 19 traffic scenarios and corresponding video stimuli, to examine the impact of manual and assisted driving on driving anger under varying levels of time pressure. In addition, the moderating effects of individual factors such as age and education were analyzed. Descriptive statistics, independent samples t-tests, and one-way ANOVA were applied to 113 valid responses to construct a three-dimensional analytical framework involving driving mode, time pressure, and demographic variables. The results indicate that under high time pressure, assisted driving systems significantly reduce drivers' anger levels in typical delay-related scenarios compared with manual driving. Age is identified as a key moderating factor influencing emotional responses. This research provides theoretical support for the development of emotion-aware driving intervention systems and offers empirical evidence for the formulation of future strategies for driving emotion regulation.
Zhang, Tingjia, Li, Ruiheng, Zhu, Tong
Real-time Vehicle Speed Detection Based on YOLOv11 and DeepSORT2026-99-15959/11/2026
The technology of real-time and effective vehicle speed detection is considered a key technology to improve traffic monitoring efficiency and traffic safety management grade. To address the limitations of traditional speed detection schemes—including reliance on dedicated hardware, poor environmental adaptability, and high construction and maintenance costs—this paper proposes a r10eal-time vehicle speed detection system based on YOLOv11 and the DeepSORT algorithm. The proposed system uses the YOLOv11 target detection algorithm as its primary model. DeepSORT multi-target tracking technology is integrated to enhance tracking performance. Speed measurement is implemented using a virtual detection line. This approach enables accurate vehicle detection, continuous tracking, and real-time speed measurement within video frames. Through the experiments, the result shows that the improved YOLOv11n model reaches mAP@0.5 of 0.982 and a recall rate of 0.956 in the test set, higher than the YOLOv8n and YOLOv5s models. The speed detection error can be restricted to 3 km/h, satisfying the real-time detection need. There is no need for road surface modification, and the detection system has a flexible layout, providing a dynamic basis for traffic law enforcement and traffic data support for road construction and traffic control optimization.
Jin, Guowei, Ma, Wenlong, Jiang, Dali, Li, Nan
2025 International Conference on Intelligent Equipment, Vehicle Engineering and Automation Control2026-99-11009/11/2026
TOC
Tobolski, Sue
DisReID: Single Domain Generalizable Person Re-ID Method for Highway Construction & Operation2026-99-15309/11/2026
With the advancement of computer vision technologies and the widespread deployment of video surveillance systems, traffic safety and the development of intelligent highways have been significantly enhanced. As a key component of the intelligent video analysis module in smart highways, person re-identification (re-ID) addresses critical challenges, including cross-segment tracking of pedestrians illegally using emergency lanes, multi-camera joint searches for lost persons in service areas, and trajectory tracing of individuals involved in traffic accidents. These functions directly support the core goals of "safety assurance and efficient service" for smart highways. However, due to the complexity of the application scene, its generalization to unseen environments remains a core challenge. This problem is formally studied under the setting of Single-Domain Generalizable Person Re-identification (SDG re-ID), which aims to train a model on a single source domain that can perform well on arbitrary unseen target domains. To handle this issue, this paper proposes a novel Disentangled Augmentation re-ID Framework (DisReID) that disentangles and augments both structure and style. Specifically, DisReID consists of two modules: Structure-aware Viewpoint Simulation (SVS), a novel pre-processing technique that simulates cross-camera perspective changes by perspective transformation, diversifying geometric structure without harming identity semantics; and Style-Dominant Frequency Perturbation (SFP), which selectively focuses on the style-dominant frequencies and applies perturbation to enable controllable style augmentation while preserving structure cues. Furthermore, to alleviate the BN-induced domain bias, we introduce a simple yet effective test-time adaptation strategy, termed Cluster Fine-tuning (CF), that performs unsupervised clustering on target-domain features to assign pseudo-labels and subsequently fine-tunes the model, enhancing adaptability to unseen domains. Extensive experimental results on four public datasets demonstrate that our DisReID achieves superior generalization performance compared to the state-of-the-art methods. This work provides key technical support for the large-scale application of re-ID in smart highways, advancing the goal of "full-domain perception and intelligent collaboration".
Pan, Hong, Yu, Fangying
Analysis of Latency-Influencing Factors in Vehicle-Road-Cloud Integrated Systems Based on Fault Tree Analysis and Analytic Hierarchy Process2026-99-15439/11/2026
Vehicle–road–cloud integrated systems have great potential in terms of improving traffic efficiency and achieving intelligent automatic driving through the integration of on–board terminals, roadside facilities, and cloud computing. However, their operational capabilities are heavily reliant on ultra-low-latency collaborative communication. This paper constructs a latency fault tree model to comprehensively analyze multi-source triggering paths of computation delay and reveals the formation mechanism of the delay path from “germination–induction–evolution”. On this basis, the Analytic Hierarchy Process is used to construct a three-level evaluation framework, and the influencing factors are quantitatively evaluated using NS-3 simulation data of the 004-V2X Communication Performance Testing Dataset. The result shows that the weight value of the network communication layer is the largest, 0.498, which shows that the bottleneck of performance in network communication is the wireless link quality. The cloud processing layer is second 0.327, which is dominated by the computational complexity and resource allocation policy. The impact of the onboard terminal layer is the smallest, 0.175. The FTA–AHP framework supported by empirical data can find the key factors affecting delay, which can help engineering optimization. It is noted that the AHP consistency check (CR) just checks the inner transitivity of expert judgment (i.e., the matrix consistency), while it cannot assure the objectivity and the bias elimination. We reduce the subjectivity by combining multiple experts, anchoring judgment with the simulation data, and performing a sensitivity check on the perturbation of the weights.
Xu, Yunchuan, Wang, Xiaomeng, Wang, Yan
Research on the Performance Evaluation Method of Intelligent Chassis Vehicles Based on AHP2026-99-08617/30/2026
The comprehensive performance evaluation system for intelligent chassis vehicles comprises multi-level indicators and exhibits certain complexity. In this study, the Analytic Hierarchy Process (AHP) is employed to calculate the weights of indicators across different performance levels. Comprehensive performance is evaluated through the integration of objective indicator assessment and subjective scoring, and the evaluation results of the vehicle’s comprehensive performance are ultimately derived. This work provides a scientific scoring method for the product testing and evaluation of intelligent chassis vehicles.
Wu, Shiyu, Wang, Jingxian, Guo, Ruiling, Liang, Dong, Li, Saisai, Yu, Xuetian
Simulation and Experimental Evaluation of a Vehicle–Road Cooperative Positioning Method in Global Navigation Satellite Systems (GNSS)-Denied Environments12-09-04-00297/29/2026
In complex urban environments, vehicle positioning based on Global Navigation Satellite Systems (GNSS) is prone to failure or accuracy degradation due to signal blockage and multipath effects. To address this issue, this article proposes a vehicle–road cooperative positioning method based on factor graph optimization for GNSS-denied environments and evaluates its performance through both simulation and real-vehicle experiments. In the proposed approach, road codes deployed on the road surface serve as absolute position references on the road surface, and high-precision vehicle position estimates are obtained in real time by fusing roadside positioning information with onboard sensor measurements using factor graph optimization. Furthermore, to reduce the experimental cost and development cycle of the vehicle–road cooperative positioning method, a performance simulation platform is developed based on the CARLA simulator, RoadRunner, and the CARLA-ROS (Robot Operating System) bridge for real-time communication between simulation and positioning modules. The platform supports road code generation and deployment, customized scenario construction, and positioning performance simulation, and a multi-objective optimization approach is employed to obtain an optimal deployment scheme for road code spacing. Finally, the effectiveness of the proposed vehicle–road cooperative positioning method is validated through both simulation and real-vehicle experiments. Real-vehicle experiments show that the proposed method reduces the average RMSE (Root Mean Square Error) by 26.6% compared with the ESKF (Error State Kalman Filter) baseline, achieving an RMSE of 0.25 m and a maximum error of 0.95 m at a vehicle speed of 60 km/h and a 10 m code spacing, thereby confirming decimeter-level continuous accuracy in GNSS-denied environments.
Shen, Chuanfu, Zhao, Zhiguo, Yan, Danshu, Ling, Yubin
SAE TOMORROW TODAY - SDVs, AI, and the Next Era of Automotive Innovation135787/28/2026
What does it really mean to build a software-defined vehicle? As AI reshapes the automotive industry, SDVs may become the foundation for the future rather than the destination. Listen in as we sit down with Jeffrey Chou, Founder and CEO of Sonatus, a leading provider of intelligence-driven SDV solutions, to explore why SDVs are best understood as a platform for innovation -- one that is scalable, upgradable, and proven at scale. This conversation dives into the evolution of SDVs, from over-the-air updates and AI-powered diagnostics to intelligent infrastructure services that could one day allow vehicles to share computing power, storage, and data with the world around them. You'll also get insight on how Sonatus scaled its software, the cultural shift required to bring Silicon Valley and automotive engineering together, and why collaboration -- not competition -- will define the future of mobility. If you're interested in automotive OS, AI, SDVs, or the future of vehicle architecture, this episode offers an insider's perspective on where the industry is headed next. We'd love to hear from you! Share your comments, questions and ideas for future topics and guests podcast@sae.org. Don't forget to take a moment to follow SAE Tomorrow Today-a podcast where we discuss emerging technology and trends in mobility with the leaders, innovators and strategists making it all happen--and give us a review on your preferred podcasting platform. Follow SAE on LinkedIn, Instagram, Facebook, X, and YouTube. Follow host Grayson Brulte on LinkedIn, X, and Instagram.
Patterson, Lori
A Review of Physical and Digital Infrastructure Standards and Guidelines to Support Deployment of Automated Vehicles in Rural Environments12-09-04-00307/23/2026
Connected and Automated Vehicles (CAVs) represent a transformative innovation poised to revolutionize roadway transportation by leveraging automated driving systems equipped with advanced sensors, high-performance computing, and communication technologies. While urban areas are the primary focus of current CAV developments, rural transportation systems risk being left behind despite the significant benefits that CAVs can bring to these regions. This article, therefore, explores the physical and digital infrastructure requirements for the safe deployment of CAVs in rural areas, drawing insights from standards, recommendations, and guidelines developed by leading standard organizations. The study highlights the specific design of physical infrastructure, including traffic signs, traffic signals, and pavement markings, and digital infrastructure, including communication, sensing, and mapping, to ensure rural communities are effectively prepared to benefit from the potential of CAVs. As its primary contribution, this article provides a comprehensive review of existing standards and guidelines relevant to rural CAV deployment. By synthesizing guidance across multiple standard-setting organizations, this review delivers a structured analytical assessment of existing standards, revealing their limitations and misalignment with rural transportation contexts while highlighting emerging good practices. The article clarifies the applicability of current guidance to rural infrastructure, identifies systemic infrastructure-related failure modes, and informs context-aware planning considerations for efficient and scalable CAV deployment in rural areas.
Zakaria, Mohammed, Getahun, Tesfamichael, Tavasoli, Mahsa, Pandey, Venktesh, Sarrafzadeh, Abdolhossein, Karimoddini, Ali
North American CAV Performance Data StandardWP-00157/22/2026
As the deployment of connected and automated vehicles (CAVs) expands, the need for a consistent, cross-industry approach to performance relevant CAV data exchange is becoming more pressing. Vehicle developers, infrastructure owners and operators (IOOs), and technology providers generate and consume data that support safety, mobility, and operational efficiency, yet much of the data remains fragmented, inconsistently formatted, and difficult to reuse across systems. To address these gaps, the Society of Automotive Engineers (SAE) and the Canadian Standards Association (CSA) convened a multi-stakeholder workshop on November 3, 2025, with participants representing original equipment manufacturers (OEMs), automated driving system (ADS) developers, state and local agencies, standards bodies, and technology partners. The workshop focused on identifying challenges, clarifying needs, and outlining a path toward a North American CAV Performance Data Standard. Key themes from the workshop included: -The need for a shared data language to support safe and interoperable CAV operations. -The lack of consistent formatting, labeling and visibility regarding who produces and consumes data. -A “start small, iterate and scale” approach beginning with well-defined use cases. -The need for technical harmonization and governance structures that build trust and support sustained coordination. This white paper summarizes the key findings and outlines a practical approach to developing a Version 0.1 base-layer data standard that can support measurable progress in 2026 and beyond.
Nesheli, Mahmood
Research on the Design of Low-altitude Intelligent Logistics Air Membrane Station2026-99-18447/17/2026
Since the concept of low-altitude economy was included in the national plan, many application scenarios have continuously promoted the innovation of low-altitude technology. This paper presents a design scheme for low-altitude intelligent logistics air-supported membrane service stations, analyzes their technical advantages over traditional logistics service stations, conducts investment estimates for trial operation projects, and demonstrates their scientificity and economy, providing a new solution for intelligent logistics.
Li, Xing, Chen, Panpan, Qing, Qiang, Shang, Ming, Zhu, Lili, Wang, Shuai
Development of a Hybrid-Driven Computational Model for Cylinder Pressure in Marine Low-Speed Engines2026-99-05257/10/2026
To address the high operating cost of online cylinder pressure monitoring systems for low-speed engines in ships and the limitations of existing alternatives - i.e., the lack of flexibility of the mechanical model under different operating conditions and the lack of physical interpretability of the data-driven model - this study proposes a hybrid-driven based in-cylinder pressure calculation model. Taking the 6EX340EF marine low-speed engine as the object of study, the method first constructs a mechanical model and optimizes the Wiebe function parameters using the Dung Beetle Optimizer (DBO). Subsequently, the mapping relationships between operating parameters, Wiebe parameters, initial compression stage temperature and charge mass are learned by constructing a combined neural network of Convolutional Neural Network (CNN) and Bi-directional Long and Short-Term Memory Network (Bi-LSTM). Finally, the overall calculation of in-cylinder pressure was realized by integrating a multidimensional parametric framework of engine configuration parameters, real-time running inputs and dynamic MAP maps. The results show IMEP R2 = 0.9864 and peak pressure error ≤ 2%, confirming that the model can provide technical support for long-term real-time pressure measurement and closed-loop optimization control based on in-cylinder pressure for marine low-speed engines.
Huang, Jialong
Advances in Research on Emergency Lane Activation Model for Highways2026-99-05427/10/2026
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Qin, Fengcai, Chen, Jianqiu, Che, Guoyan, Lou, Benxiao, Wang, Xiang, Ning, Longtang, Zhou, Shixuan, Zhang, Xiyuan, Bao, Chun, Gu, Guobin
Research on Low-Altitude Economic Air Traffic Control Model Based on Spatiotemporal Constraint Reinforcement Learning2026-99-05807/10/2026
With the rapid development of the low-altitude economy—represented by drone logistics, aerial inspections, and air taxis—air traffic has exhibited new characteristics including diverse forms, high density, and significant speed differences. To address these changes, the traditional air traffic control system requires upgrades, particularly in dynamic aircraft scheduling. This study proposes an air traffic control model (DS-ATM) tailored to this domain, built on the Deepseek large model. By integrating spatiotemporal graph neural networks with multi-objective reinforcement learning algorithms, the model achieves real-time path planning and conflict resolution in complex airspace environments. Validated using public datasets such as OpenSky Network, NASA UTM Dataset, and METAR meteorological data, experimental results demonstrate its significant advantages in reducing conflict rates and scheduling delays.
Li, Rui, Zhao, Fangyu, She, Yue, Li, Wujie
Dynamic Routing Optimization of Electric Garbage Collection and Transportation Vehicles Based on Smart Dumpster2026-99-05507/10/2026
The comprehensive deployment of smart garbage bins realizes the real-time monitoring of garbage generation and recycling demand, and the use of intelligent network connected collection and transportation vehicles can sense dynamic data such as vehicle location and load in real time. In this context, how to efficiently integrate these dynamic information to build a responsive scheduling system has become a key requirement of smart city management. Aiming at this requirement, this paper proposes a dynamic routing optimization model of electric garbage collection and transportation vehicles considering charging constraints, and designs a hybrid PSODE combining improved particle swarm optimization(PSO) and differential evolution(DE) to solve the model. By introducing a nonlinear decreasing strategy of inertia factor and a dynamic learning factor adjustment mechanism, an adaptive optimization framework of algorithm parameters is established to enhance the adaptability of the algorithm. Numerical example analysis shows that the PSO-DE can effectively deal with the change of garbage collection and transportation demand in dynamic environment. It provides an intelligent solution for the urban garbage collection and transportation scheduling system, and significantly improves the response ability and operation efficiency of the traditional collection and transportation system.
Shen, Xiaolong, Ma, Huimin
Impact of Network Latency on the Lateral Control in Automated Vehicle Marshalling2026-01-07817/1/2026
Automated Vehicle Marshalling (AVM) is the first functionally safe Level 4 automated driving system. It consists of the wireless control of unoccupied vehicles at low speed in well-defined environments, such as parking facilities or manufacturing plants. The driverless operation in an AVM system is achieved by transmitting control messages between connected vehicles and intelligent infrastructure. Similar to other wireless applications, network reliability poses a major challenge to ensuring safe automated driving. An AVM system must provide uninterrupted communication between the vehicle and the infrastructure at a stable frequency. However, wireless systems usually suffer from varying latencies and network disturbances. In this context, international organizations and automotive industry contributors have defined requirements specifying network performance, communication interfaces, and message formats for different AVM use cases. These requirements cover communication aspects without involving core automated driving functions, such as vehicle motion control, which are also decisive in ensuring the safety of the overall system. Therefore, studying communication factors in combination with vehicle motion control offers better interpretability of system capabilities. In this work, we investigate the trade-off between communication specifications and vehicle lateral control within an AVM framework implemented on a real vehicle. We aim to address the limitations that may arise under real-world AVM driving conditions. First, we revisit the current technical specifications to highlight the specific AVM messages relevant to vehicle lateral control. Then, we propose a testing framework by establishing communication with the test vehicle over a Wi-Fi network using multiple access points deployed across an indoor parking facility and an outdoor test track. Thus, we obtain a quantitative analysis of network factors, such as latency, in different driving environments. In the next step, we present a Model Predictive Control (MPC) approach that uses the AVM control messages to achieve robust vehicle lateral control. By evaluating the control performance under communication conditions, we assess the impact of network latency on vehicle lateral control. This work provides a baseline for exploring the limitations of AVM and deriving potential optimizations.
Mejri, Mohamed Amine, Münchhausen, Henrik, Flormann, Maximilian, Sturm, Axel, Henze, Roman
Accelerating Physical AI System Validation with Electronics Digital TwinsTBMG-554406/29/2026
Physical AI refers to applications in which AI technologies are connected to hardware that sense and execute actions in the physical world, allowing systems to autonomously act and adapt in real time. It spans automotive, robotics, industrial automation, smart infrastructure, aerospace, healthcare devices, software-defined machines, and more. Regardless of application, they have one thing in common: they must operate safely, reliably, and predictably in real world environments. Unlike purely digital AI, these systems are constrained by embedded electronics, timing, power, safety, and system-level interactions that are difficult to validate early.
Research on Performance Prediction Methods for Centrifugal Refrigeration Compressors in Data Centers2026-99-07255/15/2026
As the “digital brain” and core foundational support for the development of intelligent transportation and connected vehicles, the performance of data centers directly determines the operational capability of intelligent transportation systems. In the process of advancing the vehicle-road-cloud collaborative architecture, the demand for high-performance computing power in data centers has experienced explosive growth. The substantial increase in computing tasks has posed severe challenges to thermal management, making efficient and reliable cooling systems an indispensable core component. Centrifugal compressor water-cooling units are the mainstream cooling solution for large-capacity scenarios, and their design optimization is crucial for improving the energy efficiency and performance of the entire cooling system. This paper proposes a one-dimensional performance prediction method for centrifugal compressors based on an empirical loss model, and realizes the iterative calculation of parameters in the entire flow path from the impeller inlet to the diffuser outlet through Python programming. A systematic impact assessment was carried out for major loss mechanisms such as surface friction, tip clearance, and wake mixing under standard operating conditions and critical operating conditions. The results show that the original model has high prediction accuracy under standard operating conditions, with isentropic efficiency error not exceeding 5%; however, under critical operating conditions, the efficiency prediction deviation reaches 7.54% due to the neglect of coupling effects between various losses. To address this issue, this paper introduces deviation correction factors related to flow rate, rotational speed, and density, which significantly improve the model’s prediction capability under extreme operating conditions: the efficiency error under critical operating conditions is reduced to 1.54%, and only 0.3% under rated operating conditions. This model provides a reliable tool for compressor performance prediction and extreme operating boundary identification, and has high application value in engineering practice.
Zhu, Minhao, Jiang, Bin, Li, Min, Zeng, Zihui, Gu, Yunhui
A Real-Time Monitoring and Rapid Warning Method for Guardrail Collisions in Intelligent Transportation Systems2026-99-07235/15/2026
This article proposes a method for real-time monitoring and rapid alert for guardrail collisions based on Distributed Acoustic Sensing (DAS). The aim is to enhance traffic safety through continuous analysis of vibration signals. To achieve this, a system architecture that combines both hardware and software design has been developed, enabling the handling of the entire process from signal acquisition and decoding to intelligent event recognition and visualization. To improve signal reliability, an adaptive noise reduction algorithm and a multi-level feature extraction method are introduced, enabling accurate differentiation between collision events and environmental disturbances. Tests at various vehicle speeds show that the DAS-based system detects collisions with over 98% accuracy and cuts false alarms by more than 60% compared to traditional video and point-sensor monitoring. It can locate accidents with an average error of 4.2 meters and respond in under 1 second, demonstrating both its accuracy and speed. These results confirm the method’s effectiveness and reliability for enhancing transportation safety.
Sun, Lang
Evaluating Cloud Platforms for Real-Time Cooperative Perception Systems Using RSU Cameras: A Comparative Study of Google Colab, Azure, and AWS2026-01-00184/7/2026
This paper presents a comparative study of three widely used cloud platforms, Google Colab, Microsoft Azure, and Amazon Web Services (AWS), for running a real-time cooperative perception system based on roadside unit (RSU) cameras. The goal is to evaluate the performance, scalability, and cost-efficiency of each platform when handling high-volume video data for object detection, a key task in autonomous driving. A unified perception pipeline using the YOLOv8 Small model was deployed on all platforms, with the same dataset and settings to ensure fair comparison. The evaluation focused on key metrics such as latency, frame processing rate, detection accuracy, cost, scalability, and reliability. The results show that Google Colab is a cost-effective starting point but has limitations in uptime and scalability. Azure offers stable performance and balanced cost, making it suitable for medium-scale applications. AWS delivers the best scalability and speed but at a higher cost. This study provides practical guidance for choosing the right cloud platform for deploying cooperative perception and intelligent transportation systems.
Alkharabsheh, Ekhlass, Alawneh, Shadi, Rawashdeh, Osamah
MEDAS: A Multi-Dimensional Framework for Vehicle Detection Model Selection2026-01-00254/7/2026
Recent years have seen a rapid rise in edge-oriented object detection models, including new YOLO variants and transformer-based RT-DETR. Choosing an appropriate model for vehicle detection, however, remains challenged because common metrics such as precision, recall, and mAP capture only part of the trade-off between accuracy and computational cost. To better support model selection, we introduce the Multi-dimensional Equilibrium Detection Assessment Score (MEDAS), which evaluates detectors across four practical dimensions: performance, balance, efficiency, and adaptability. The framework includes a normalization strategy and adjustable weighting so that evaluations can reflect specific deployment needs, especially in resource-limited settings. Experiments on the MS-COCO vehicle dataset show that while RT-DETR models offer competitive accuracy, they require substantially more computation. In contrast, lightweight YOLO variants provide a stronger balance between accuracy and efficiency. Among all evaluated models, YOLOv11s achieves the highest MEDAS score, suggesting it is well suited for applications such as ADAS and embedded autonomous systems. MEDAS offers a practical way to compare modern detectors and helps connect offline accuracy metrics with real deployment constraints in intelligent transportation systems.
Guo, Bin
High Order Control Lyapunov Function - Control Barrier Function - Quadratic Programming Based Autonomous Driving Controller for Bicyclist Safety2026-01-00344/7/2026
Ensuring the safety of Vulnerable Road Users (VRUs) is a critical challenge in the development of advanced autonomous driving systems in smart cities. Among vulnerable road users, bicyclists present unique characteristics that make their safety both critical and also manageable. Vehicles often travel at significantly higher relative speeds when interacting with bicyclists as compared to their interactions with pedestrians which makes collision avoidance system design for bicyclist safety more challenging. Yet, bicyclist movements are generally more predictable and governed by clear traffic rules as compared to the sudden and sometimes erratic pedestrian motion, offering opportunities for model-based control strategies. To address bicyclist safety in complex traffic environments, this study proposes and develops a High-Order Control Lyapunov Function–High-Order Control Barrier Function–Quadratic Programming (HOCLF-HOCBF-QP) control framework. Through this framework, CLFs constraints guarantee system stability so that the vehicle can track its reference trajectory, whereas CBFs constraints ensure system safety by letting vehicle avoiding potential collisions region with surrounding obstacles. Then by solving a QP problem, an optimal control command that simultaneously satisfies stability and safety requirements can be calculated. Three key bicyclist crash scenarios recorded in the Fatality Analysis Reporting System (FARS) are recreated and used to comprehensively evaluate the proposed autonomous driving bicyclist safety control strategy in a simulation study. Simulation results demonstrate that the HOCLF-HOCBF-QP controller can help the vehicle perform robust, and collision-free maneuvers, highlighting its potential for improving bicyclist safety in complex traffic environments.
Chen, Haochong, Cao, Xincheng, Guvenc, Levent, Aksun Guvenc, Bilin
Collaborative Development Framework for Electric-Based Software-Defined Vehicles – The European Research Project CODE4EV2026-01-01134/7/2026
The automotive industry is subject to major transformation initiated by societal and economical pull (reducing emissions, zero fatalities, European competitiveness) and accelerated by technology push (electrification, Cooperative, Connected and Automated Mobility (CCAM), and Cooperative Intelligent Transport Systems (C-ITS)). Following this trend, the Software-Defined Vehicle (SDV) targets the integration of software (SW) development methodologies for vehicle development as well as the value delivery shift toward customers along the entire lifecycle. It promises to create benefits for the car manufacturers in terms of faster time to market, easier update – as well as for the car users (private persons, fleet operators) in terms of personalized user experience, upgradability. At the same time, SDV requires a much more integrated and continuous development framework to enable different experts to efficiently develop and validate concurrently the different parts of the vehicles, to gather information about real operation, and to support update in the field. This paper introduces the collaborative development framework introduced in the European research program Collaborative Development Framework for electric-based Software-Defined Vehicles (CODE4EV).
Armengaud, Eric, Permann, Robert, Joergler, Sabrina, Barcelona, Miguel Angel, García, Laura, Rodriguez, José Manuel, Ivanov, Valentin, Li, Zhenqian, Nguyen Quoc, Trieu, Rodrigues, Sandy, Kowalczyk, Bogdan, Avdić Čaušević, Amra
ProGuard: A Real-Time Preemptive Artificial Intelligence-Based Anti-Pinch System for Bus Entryways on Low-Power Edge Devices2026-01-01004/7/2026
This paper proposes ProGuard, a novel approach to preemptive pinch detection systems for buses. ProGuard utilizes state-of-the-art AI object detection algorithms to identify potential pinching events in bus entryways before pinching occurs. Modern conventional anti-pinch systems, such as pressure sensors or hall effect sensors, often rely on mechanical contact before triggering. While these systems are established safety mechanisms, they are reactive and therefore require some level of pinching before triggering. This reactive approach presents numerous safety concerns for passengers, especially when considering children on school buses. Existing preemptive detection methods, such as infrared or ultrasonic sensors, solve the problems presented by these reactive detection systems. However, these systems either lack the range or environmental resilience needed for reliable operation in buses. The critical nature of anti-pinch systems requires a robust and reliable solution that can adapt to various applications and environments. Our study investigates an AI-based approach that leverages the YOLOv11 nano object detection model to detect people and backpacks in real-time. We performed a comparative study on various model formats to find the best-performing format on the chosen edge compute hardware. Our experimental results revealed that when using the IMX model format on an AI-accelerated camera, ProGuard can achieve 24 frames per second and an inference time of 125ms while running on a Raspberry Pi computer. Performance tests on this model showed a mAP@0.5-0.95 of 0.522, putting ProGuard on par with baseline YOLOv11 nano performance. These results demonstrate that ProGuard offers an efficient and real-time alternative to current pinch detection approaches while operating on low-cost consumer hardware.
Bradley, Hudson, Zadeh, Mehrdad, Tan, Teik-Khoon
A Dataset for Visual Classification of Flat Tires2026-01-01524/7/2026
Flat tires represent a common yet serious issue in vehicle safety, leading to compromised control, increased braking distance, and potential rim or structural damage when undetected. Conventional tire pressure monitoring systems (TPMS) rely on embedded sensors that can fail, incur high replacement costs, and are not always equipped in older or low-cost vehicles. To address these limitations, this study presents a comprehensive visual dataset for flat-tire classification using computer vision and machine learning techniques. The dataset comprises 600 labeled images—300 flat-tire and 300 non-flat-tire samples—collected from diverse vehicle types, lighting conditions, and viewpoints. This dataset is designed to support the training and benchmarking of lightweight edge-AI models suitable for real-time deployment on embedded platforms. A set of supervised learning models were evaluated. Results demonstrate that visual-based classification provides a cost-effective and scalable pathway toward automated tire health monitoring and contributes to safer and more sustainable intelligent transportation systems.
Gunasekaran, Aswin, Govilesh, Vidarshana, Challa, Karthikeya, Maxim, Bruce, Shen, Jie
SAE TOMORROW TODAY - AI, Government, and the Future of Urban Mobility135593/13/2026
What happens when government, technology, and sustainability collide? Listen in as we down with Gabe Klein, Co-Founder of Adapt/Impact and CityFi, for a candid conversation about the future of urban mobility, the evolving role of government, and the impact of AI on our streets. From smart curb management to electrification, you'll hear real-world examples of cities embracing innovation, rethinking transportation as a utility, and navigating the economic shifts that come with change. Tune in for an inside look at how smarter infrastructure can create more connected, efficient, and greener cities. We'd love to hear from you. Share your comments, questions and ideas for future topics and guests to podcast@sae.org. Don't forget to take a moment to follow SAE Tomorrow Today--a podcast where we discuss emerging technology and trends in mobility with the leaders, innovators and strategists making it all happen--and give us a review on your preferred podcasting platform. Follow SAE on LinkedIn, Instagram, Facebook, X, and YouTube. Follow host Grayson Brulte on LinkedIn, X, and Instagram.
Patterson, Lori
IoT-Enabled Cloud-Integrated Smart Parking System with Real-Time Monitoring and AI-Based Space Optimization for Next-Gen Mobility2026-28-01132/12/2026
This study presents the design and implementation of an advanced IoT-enabled, cloud-integrated smart parking system, engineered to address the critical challenges of urban parking management and next-generation mobility. The proposed architecture utilizes a distributed network of ultrasonic and infrared occupancy sensors, each interfaced with a NodeMCU ESP8266 microcontroller, to enable precise, real-time monitoring of individual parking spaces. Sensor data is transmitted via secure MQTT protocol to a centralized cloud platform (AWS IoT Core), where it is aggregated, timestamped, and stored in a NoSQL database for scalable, low-latency access. A key innovation of this system is the integration of artificial intelligence (AI)-based space optimization algorithms, leveraging historical occupancy patterns and predictive analytics (using LSTM neural networks) to dynamically allocate parking spaces and forecast demand. The cloud platform exposes RESTful APIs, facilitating seamless interoperability with user-facing mobile and web applications. These interfaces provide end-users with real-time visualization of parking availability, intelligent navigation to optimal spaces, and digital payment integration, thereby minimizing search time and enhancing user convenience. From an administrative perspective, the system delivers comprehensive analytics dashboards, including heatmaps of space utilization, anomaly detection for unauthorized parking, and predictive maintenance alerts for sensor nodes. Field trials conducted across a multi-level parking facility demonstrated a 32% reduction in average vehicle search time and a 21% improvement in space utilization efficiency compared to conventional systems. The end-to-end solution adheres to robust cybersecurity standards (TLS 1.2 encryption, role-based access control) and is designed for modular scalability, supporting integration with smart city infrastructure and electric vehicle charging stations. This research establishes a scalable, intelligent framework for urban parking management, contributing significantly to reduced congestion, optimized resource allocation, and enhanced urban mobility.
Deepan Kumar, Sadhasivam, S, Balakrishnan, Dhayaneethi, Sivaji, Boobalan, Saravanan, Abdul Rahim, Mohamed Arshad, S, Manikandan, R, Jamuna, L, Rishi Kannan
GTSRB Shield: A Novel Machine Learning Approach to Anomaly Detection in German Traffic Signs Recognition Benchmark (GTSRB) Dataset2026-26-06111/16/2026
The escalating dependence of Autonomous Vehicles on Intelligent Transportation Systems (ITS) has highlighted the imperative for comprehensive security protocols to safeguard such vehicles against cyber threats. Intrusion Detection Systems (IDS’s) are pivotal in ensuring the protection of these systems by detecting and alleviating unauthorized access and nefarious activities. The German Traffic Sign Recognition Benchmark (GTSRB) database, which encompasses an extensive compilation of traffic sign imagery, functions as a vital asset for the advancement of machine learning-based IDS. This research elucidates an intrusion detection system (IDS) that employs machine learning algorithms to scrutinize the GTSRB database. The proposed IDS emphasize the preprocessing of the GTSRB dataset to extricate pertinent features that can be employed for the training of machine learning models. Research also focuses on model development with machine learning algorithms to classify traffic signs and discern anomalies suggestive of potential intrusions. The efficacy of the models is evaluated utilizing accuracy thereby ensuring that the IDS can consistently differentiate between benign and malicious activities. This inquiry contributes to the domain of intelligent transportation systems by establishing a resilient framework in autonomous vehicles for intrusion detection, thus bolstering the security of automated traffic management systems against prospective cyber threats. The results underscore the criticality of incorporating machine learning methodologies in real-time systems to proactively mitigate security vulnerabilities and preserve the integrity of traffic data.
Patil, Kamalesh, Akbar Badusha, A., Jadhav, Savitri, Gunale, Kishanprasad
AI-Powered Intelligent Cognitive Lens for Driver Monitoring for Safe Transportation and Increased Fleet Efficiency Abstract2026-26-06341/16/2026
The rapid evolution of intelligent transportation systems has made drivers’ attentiveness and adherence to safety protocols more critical than ever. Traditional monitoring solutions often lack the adaptability to detect subtle behavioral changes in real time. This paper presents an advanced AI-powered Driver Monitoring System designed to continuously assess driver behavior, fatigue, distractions, and emotional state across various driving conditions. By providing real-time alerts and insights to vehicle owners, fleet operators, and safety personnel, the system significantly enhances road safety. The system integrates lightweight AI/ML algorithms, image processing techniques, perception models, and rule-based engines to deliver a comprehensive monitoring solution for multiple transportation modes, including automotive, rail, aerospace, and off-highway vehicles. Optimized for edge devices, the models ensure real-time processing with minimal computational overhead. Alerts are communicated through web and mobile platforms, supplemented by audio-visual cues for prompt user responses. Data from multi-camera setups, auditory sensors, and vehicle CAN bus inputs are processed by a real-time analytics engine that detects abnormal behaviors and safety violations, improving situational awareness and enabling timely interventions. For both individual drivers and fleet managers, the platform serves as an intelligence hub that boosts situational awareness, operational efficiency, and safety compliance. Drivers receive real-time feedback on their behavior, allowing them to make proactive adjustments and reduce risks. Fleet managers can leverage cloud-based connectivity to access predictive analytics, real-time monitoring, and detailed historical behavior data. This enables the identification of unsafe driving patterns, enforcement of safety protocols, and optimization of fleet performance. The system also simplifies regulatory reporting and auditing processes, ensuring compliance with safety standards. By continuously monitoring driver behavior, managers can foster a culture of safety and performance while improving overall fleet operations.
Chikhale, Shraddha, Sing, Sandip, Hivarkar, Umesh, Mardhekar, Amogh
Revolutionizing Mass Mobility: Sustainable, Safe, and Smart Transport Ecosystems2026-26-03601/16/2026
Mass Mobility Systems are critical for a sustainable and progressive society. As the world confronts the serious challenges of global warming and urban traffic congestion, efficient mass mobility solutions become critical in reducing carbon footprints and enabling equitable access. Advancement in mass mobility is not limited to electric buses alone but also includes innovations across conventional ICE vehicles, autonomous vehicles, trains, and other integrated transport networks. Safety and accessibility for users remain critical to the sustainability of future mass mobility concepts. The COVID-19 pandemic exposed vulnerabilities in public transportation, highlighting the urgent need for safer and more resilient systems. Road safety, passenger well-being, and hygienic standards must be deeply embedded into future mobility solutions. Furthermore, strong last-mile connectivity will be essential to ensure that mass mobility truly meets the needs of all citizens. An effective Mass Mobility System integrates various modes - buses, trains, and feeder services into a seamless travel experience. Cities like Singapore provide excellent examples of how integrated planning, smart scheduling, and multimodal connectivity can achieve this goal. The future of mass mobility will see an increasing adoption of EV technology, offering significant advantages in reducing emissions, noise, and vibration. However, ICE and hybrid solutions will continue to play a supporting role, especially in specific geographies and use cases. Additionally, the emergence of autonomous vehicles promises to reshape the landscape dramatically, creating an altogether new world of transport possibilities. Artificial Intelligence (AI) will become a key differentiator, enabling smarter route planning, personalized user experiences, and real-time adaptability. This paper explores how Mass Mobility Systems can evolve to balance sustainability, safety, technology, and inclusiveness, offering insights for city planners, mobility operators, and policymakers to create future-ready transport ecosystems.
Vasudevan, M, Kumar S, Ashok, Sridevi, M, Kumar, Rajiv, Kumar, Om
Software-Defined Vehicles: Architecting the Future of Intelligent and Connected Mobility2026-26-06911/16/2026
This paper presents a comprehensive technical review of the Software-Defined Vehicle (SDV), a paradigm that is fundamentally reshaping the automotive industry. We analyze the architectural evolution from distributed Electronic Control Units (ECUs) to centralized zonal compute platforms, examining the critical role of Service-Oriented Architectures (SOA), the AUTOSAR standard, and virtualization technologies in enabling this shift. A comparative analysis of leading High-Performance Computing (HPC) platforms, including NVIDIA DRIVE, Tesla FSD, and Qualcomm Snapdragon Ride, is conducted to evaluate the silicon foundation of the SDV. The paper further investigates key enabling technologies such as Over- the-Air (OTA) updates, Digital Twins, and the integration of Artificial Intelligence (AI) for applications ranging from predictive maintenance to software-defined battery management. We scrutinize the competing V2X communication standards (DSRC vs. C-V2X) and address the paramount challenges of functional safety (ISO 26262) and cybersecurity (ISO/SAE 21434) in this new landscape. Finally, we identify open research challenges, ethical considerations, and the future trajectory of SDVs toward fully AI-defined, intelligent, and connected mobility.
Ahmad, Aqueel, Hemanth, Khimavath, Kumar, Om, Kumar, Rajiv, Haregaonkar, Rushikesh Sambhaji
Automotive Cybersecurity: Safeguarding Connected and Autonomous Mobility2026-26-06291/16/2026
As vehicles transform into complex cyber-physical systems within Intelligent Transportation Systems (ITS), automotive cybersecurity has become a foundational pillar in securing safe, reliable, and trustworthy transportation. This paper examines cybersecurity challenges in connected and autonomous vehicles (CAVs), focusing on Vehicle-to-Everything (V2X) communications technologies, including Vehicle-to-Vehicle (V2V), Vehicle-to-Infrastructure (V2I), and Vehicle-to-Pedestrian (V2P), and critical systems like electronic control units (ECUs), battery management units (BMUs), and sensor fusion modules. Key vulnerabilities, such as remote hacking, denial-of-service (DoS) attacks, malware injection, and data breaches, threaten vehicle functionality, passenger safety, and privacy. Key protection mechanisms, including encryption, intrusion detection systems (IDS), cryptographic protocols, secure over-the-air (OTA) updates, and Advanced Artificial Intelligence (AI) and Machine Learning (ML) algorithms, enhance threat detection, anomaly monitoring, and adaptive security responses. Additionally, emerging blockchain-based security frameworks offer decentralized solutions for data integrity and secure transactions. For electric vehicles (EVs), lightweight and energy-efficient cybersecurity solutions are critical to securing EV-specific architectures. Global standardization efforts, including ISO/SAE 21434 and UN Regulation No. 155, are shaping industry best practices, ensuring interoperability and scalable security frameworks for next-generation vehicles. This review synthesizes research advancements from 2001 to 2024, identifying key challenges such as real-time threat mitigation, scalability, and adaptive security architectures. The paper aims to provide valuable insights for researchers, engineers, and policymakers, fostering the development of secure, resilient, and sustainable automotive ecosystems in an increasingly digitized transportation network.
Kumar, Om, Kumar, Rajiv, Sankar M, Gopi, Haregaonkar, Rushikesh Sambhaji
Distributed Robust Control of Heterogeneous Vehicular Platoons Based on IDM Following Model under Communication Delay2025-01-735012/31/2025
With the rapid development of automobile industrialization, the traffic environment is becoming increasingly complex, traffic congestion and road accidents are becoming critical, and the importance of Intelligent Transportation System (ITS) is increasingly prominent. In our research, for the problem of cooperative control of heterogeneous intelligent connected vehicle platoons under ITS considering communication delay. The proposed method integrates the nonlinear Intelligent Driver Model (IDM) and a spacing compensation mechanism, aiming to ensure that the platoon maintains structural stability in the presence of communication disturbances, while also enhancing the comfort and safety of following vehicles. Firstly, construct heterogeneous vehicle platoon system based on the third-order vehicle dynamics model, Predecessor-Leader-Following (PLF) communication topology, and the fixed time-distance strategy, while a nonlinear distributed controller integrating the IDM following behavior and the front-vehicle spacing compensation mechanism is designed to enhance the robustness of the system to delay disturbance. Secondly, leveraging the Lyapunov-Krasovskii functional framework in conjunction with the Moon inequality, an LMI-based stability condition is derived to ensure the uniform asymptotic stability of the system. The corresponding maximum admissible communication delay is then determined, followed by a detailed analysis of the system's string stability. Finally, comparative simulations are conducted on the MATLAB/Simulink platform. Simulation results verify that the proposed controller offers enhanced convergence speed, reduced acceleration variability, and improved suppression of spacing errors under communication delay disturbances. Compared to conventional linear controllers, it demonstrates markedly superior control performance and greater practical applicability. This method provides a valuable reference for the robust design and performance optimization of cooperative control systems for heterogeneous vehicle platoons under communication delay conditions.
Ye, Xin, Kang, Zhongping
Knowledge Modeling and Reuse of Concrete Structure Strengthening Solutions for Existing Buildings2025-99-020212/23/2025
Implementing knowledge modelling tools of concrete structure strengthening solutions for existing buildings addresses the urgent needs of urban renewal efforts. This paper thoroughly investigates the application of Natural Language Processing (NLP), and knowledge graphs for organizing and managing complex information related to building strengthening strategies. By developing an ontology model for solutions and supplementing it with methods for generating word vectors and annotating data, this study constructed a comprehensive framework for the management of strengthening solution knowledge. A case study on the partial structural strengthening validated the applicability of the proposed model in facilitating recommendations for similar cases and supporting solution design. This research under-scores the transformative impact of digital technologies and knowledge modelling on the efficiency and quality of urban renewal projects, contributing to the advancement of smart cities. The findings promoted the integration of informatized and intelligent methods in the strategic planning and execution of concrete structure strengthening projects.
Zhang, Zhuohao, Luo, Hanbin, Wu, Haozheng, Chen, Weiya
Trajectory Reconstruction at Signalized Intersections in a Vehicle-to-Infrastructure Environment2025-99-021712/23/2025
Vehicle trajectories encapsulate critical spatial-temporal information essential for traffic state estimation, congestion analysis, and operational parameter optimization. In a Vehicle-to-Infrastructure (V2I) environment, connected automated vehicles (CAVs) not only continuously transmit their own real-time trajectory data but also utilize onboard sensors to perceive and estimate the motion states of surrounding regular vehicles (RVs) within a defined communication range. These multi-source data streams, when integrated with fixed infrastructure-based detectors such as speed cameras at intersections, create a robust foundation for reconstructing full-sample vehicle trajectories, thereby addressing data sparsity issues caused by incomplete CAV penetration. Building upon classical car-following (CF) theory, this study introduces a novel trajectory reconstruction framework that fuses CAV-generated trajectories and infrastructure-based speed detection data. The proposed method specifically aims to reconstruct the unobserved trajectories of RVs located between successive CAVs within the same lane, ensuring continuity and accuracy in trajectory estimation. To validate the framework’s effectiveness, extensive SUMO simulations were conducted under different CAV penetration rates (PRs: 5%, 10%, 15%, and 20%) with a controlled traffic flow rate of 1000 veh/h. Key findings indicate that the proposed method maintains stable reconstruction accuracy across all tested penetration rates, with errors remaining within acceptable thresholds. Furthermore, comparative analysis against state-of-the-art CF-based reconstruction approaches reveals substantial improvements in accuracy, achieving reductions of 84.51% (LE), 97.07% (QLE) and 95.55% (TE), respectively. The result highlights the proposed method potential for enhancing real-time traffic state estimation, optimizing signal control strategies, and improving overall traffic management in V2I-enabled urban networks.
Bai, Wei, Fu, Chengxin, Yao, Zhihong
Study on the Evaluation Indicator System of Intelligent Capacity Optimization on Highways2025-99-025312/23/2025
Intelligent capacity optimization of highways could realize intelligent enhancement of traffic capacity by optimizing traffic management, improving traffic efficiency and enhancing system synergy without significantly increasing physical lanes. However, there was a lack of a unified and perfect index system to scientifically evaluate the effectiveness of such projects. This paper analyzed the basic theory, evaluation indicator structure and system, and puts forward seven key evaluation dimensions, which including traffic efficiency enhancement, traffic safety improvement, economic and cost-benefit, environmental impacts, technology application and innovation, system reliability and resilience, and service experience. This paper screened the specific evaluation indexes of the seven dimensions and proposes the hierarchical structure of the index system and the weight determination method. This paper constructed a comprehensive, multi-dimensional evaluation index system for highway smart expansion projects, aiming to provide scientific basis and standardized tools for the planning, decision-making, implementation effect assessment and continuous optimization of highway smart expansion projects.
Che, Xiaolin, Li, Weichen, Zhu, Lili, Li, Xin, Wang, Lin
Construction and Empirical Study of an Intercity Rail Station Connection Performance Evaluation Model Based on Multi-Source Data Fusion: A Case Study of the Guangzhou-Dongguan-Shenzhen Intercity Railway2025-99-024312/23/2025
With the rapid expansion of China’s intercity rail transit network, station connection systems play a crucial role in enhancing rail transit efficiency. The efficiency of their supply-demand matching has become a significant factor influencing regional transportation integration. This paper focuses on the Guangzhou-Dongguan-Shenzhen Intercity Railway as the research subject. It constructs a connection performance evaluation model that integrates multisource data from both supply and demand perspectives, revealing spatial differentiation patterns of station connection pressure and facility needs, and classifies the stations accordingly. Based on these findings, the paper proposes optimization strategies to inform intercity transportation planning and the development of intelligent transportation systems. Intercity railway, connection performance, data envelopment analysis, Guangdong-Hong Kong-Macao Greater Bay Area, evaluation model
Hu, Qiyue, Gao, Yifei
Design of Intelligent Lighting System for Expressway Tunnel2025-99-023212/23/2025
A smart highway tunnels lighting system based on the technology of cloud platform and Internet of Things(IoTs) has been designed to address the common problems of high energy consumption and low level of intelligence in China's highway tunnel lighting system. The highway tunnel lighting system consists of four layers of architecture: platform management layer, local management layer, middle layer and terminal layer. The system collects real-time brightness, lamp brightness, traffic volume and other data outside the tunnel through various sensors deployed on site, and then uploads the collected data to the main controller through LoRa IoTs. The main controller combines the brightness calculation method of the lighting design rules to control the brightness of the tunnel lighting in real time, achieving real-time adjustment of the brightness of the tunnel LED lights and the brightness outside the tunnel, and realizing a safe and energy-saving lighting effect of "lights on when the car comes, lights on when the car goes, and lights follow the car". The experimental results show that the energy-saving rate of the system has reached about 70%, which has achieved good energy-saving and emission reduction effects, and has significant economic, social, and ecological benefits.
Wang, Juntao, Liu, Jingyang, Liu, Yong, Feng, Xunwei
Research on the Decision-making Model and Algorithm of Following and Lane-Changing Behavior of Connected Autonomous Vehicles on Freeway2025-99-023612/23/2025
In order to achieve the widespread application of autonomous driving technology in basic freeway segments, especially in the automated decision-making of following and lane changing behaviors, Connected Autonomous Vehicles (CAVs) must be able to reliably complete driving tasks in complex traffic environments. Our study introduces a novel behavior decision-making architecture for connected autonomous vehicles, which employs the Dueling Double Deep Q-Network (D3QN) algorithm as its core methodology. The model optimizes the decision-making ability in complex traffic scenarios by separating action selection and value assessment and implementing them by different neural networks. The multi-dimensional reward function, which comprehensively considers safety, comfort and efficiency, is introduced into the reinforcement learning training of the model. The simulation scenario of the basic freeway segment is established and the model is trained in the mixed traffic flow environment, compared with the traditional DQN and DDQN, the D3QN model can not only ensure traffic safety in the task of following and changing lanes on the expressway, but also ensure traffic safety. It also improves the smoothness of the ride.
Hou, Zhiyun, Yang, Xiaoguang
2025 8 th International Conference on Traffic Transportation and Civil Architecture (ICTTCA)2025-99-020012/23/2025
TOC
Tobolski, Sue
Basic Diagram Model of Mixed Traffic Flow Considering Buses2025-99-030912/17/2025
The emergence of connected and autonomous vehicle (CAV) technologies has ushered in a new era of mixed traffic flow, where CAVs will coexist with human-driven vehicles (HDVs) for the foreseeable future. To investigate the fundamental relationships among flow, density, and speed in this heterogeneous traffic environment, this study develops a comprehensive analytical framework that explicitly accounts for the impact of bus integration in mixed traffic streams. The study initially identifies vehicle classifications and their respective distribution ratios within heterogeneous traffic streams. A fundamental graphical representation of mixed traffic patterns is established, followed by a comprehensive sensitivity evaluation focusing on free-flow velocity parameters within the proposed framework. Subsequently, a micro-level simulation platform is developed utilizing SUMO software. Research outcomes reveal a favorable link between the percentage of integrated self-driving cars and improvements in traffic flow and congestion measures.
Xiao, Yujie, Chen, Xiufeng, Wang, Meng, Xu, Ying
Optimization of Real-Time Traffic Flow Detection Methods Based on Sensor Networks2025-99-033712/17/2025
As modern society develops rapidly, people’s requests for traffic convenience and traffic safety become greater and greater, and it is essential to eliminate traffic congestion and traffic accident to sustainable development of urban areas. Therefore, this paper brings forward novel solution based on hybrid sensor networks to observe the status of traffic in road networks in order to alleviate traffic jam and prevent traffic accident. With the collection of precise traffic flow information at the time, it realizes traffic flow control at crossroads, gives warning in advance with the congestion or accident. We carried out a bunch of simulation experiments in succession, the main discoveries are as follows. a. The energy consumption is great reduced under the sensor deployment rate between 1:50–1:60 (sensor : vehicles). b.The sampling rates can keep a very high level of precise and efficiency under the appropriate range between 1:50–1:60 (sensor : vehicles).The critical segments of roadways are fitted with the radar sensors to accomplish not only reliable surveillance of traffic congestions but also timeearlies warning of traffic accidents as opposed to relying on the single-sensor network. As reflected in Fig. 17, the heterogeneous sensor network is more robust against sensor errors because of the complementarity effects without relying on individual sensors. The experimental results highlight the potential for hybrid sensing architecture for intelligent transportation systems(ITS) and provides a well-technical basis to alleviate urban traffic jam and improve transportation efficiency.
Wang, Xinhai
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