Browse Topic: Intelligent transportation systems

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North American CAV Performance Data StandardWP-0015To be published on 07/22/2026
As the deployment of connected and automated vehicles (CAVs) expands, the need for a consistent, cross-industry approach to performancerelevant 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: -A shared data language is needed to support safe and interoperable CAV operations. -The current ecosystem lacks consistent formatting, labeling, and visibility regarding who produces and consumes data. -A “start small, iterate, and scale” approach is needed, beginning with well-defined use cases such as school zones or baseline work zones. -Progress depends on 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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
2025 8 th International Conference on Traffic Transportation and Civil Architecture (ICTTCA)2025-99-020012/23/2025
TOC
Tobolski, Sue
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
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
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
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
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
Lightweight Yolov5s Algorithm for On-Board Highway Asset Detection2025-99-032812/17/2025
Highway asset detection is a core technology in intelligent highway maintenance. However, traditional detection algorithms face issues such as high computational complexity and the misdetection or missed detection of small targets, making them unable to meet the demands for both accuracy and real-time performance. To ensure the optimal performance of highway infrastructure, developing efficient on-board highway asset detection algorithms is essential. In this study, we applied the k-means++ clustering algorithm to re-cluster the width and height of labeled target boxes in the training set, obtaining optimal prior box sizes and addressing the issue of target size diversity. For vehicle-mounted scenarios, we adopted a lightweight network architecture, replacing the CSPDarknet53 backbone of Yolov5 with MobileNetV3-large as the main feature extraction network. Additionally, to counteract the potential decline in detection performance due to the reduced complexity of the backbone network, we introduced an improved Local Normalization Attention Mechanism (L-NAM) module into the last convolutional layer of the neck network. This effectively mitigates false positives and false negatives for small targets.We propose a lightweight Yolov5s algorithm tailored for vehicle-mounted highway asset detection. Experimental results on a custom dataset show that the improved algorithm achieves an average precision of 98.2%, increases FPS to 91, and reduces the computational load in GFLOPs from 15.8 to 2.3. The proposed lightweight Yolov5s algorithm significantly reduces parameter count while maintaining high detection accuracy, providing an efficient and viable solution for vehicle-mounted highway asset detection.
Zhang, Dong, Sun, Yawen, Pan, Dingyao
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
2025 International Conference on Transportation Infrastructure and Engineering (ICTIE 2025)2025-99-030012/17/2025
TOC
Research on Intelligent Control of Entrance Ramps for Networked Vehicles Based on Deep Reinforcement Learning2025-99-041412/10/2025
With the acceleration of urbanization, freeway traffic congestion is becoming increasingly serious, especially at entrance ramps, where the concentrated inflow of traffic often leads to increased traffic pressure on the mainline, affecting the overall access efficiency. In order to alleviate the ramp congestion problem, this paper proposes a deep reinforcement learning-based intelligent control method for entrance ramps of network-connected vehicles, which adopts Proximal Policy Optimization (PPO) algorithm to optimize the ramp vehicle flow and speed control strategy in real time by constructing a reinforcement learning control framework. In this paper, simulation experiments are conducted in different traffic density scenarios and compared with the traditional reinforcement learning algorithms DQN and A2C. The experimental results show that the PPO algorithm is able to converge quickly in low, medium and high traffic densities, significantly improve the cumulative reward value, and exhibit higher stability and superiority compared with other algorithms. The research in this paper not only provides an intelligent solution for ramp flow optimization, but also provides theoretical support and technical reference for the development of intelligent transportation system.
Yang, Liu
Research on Control Strategy for Heterogeneous Traffic at Signalized Intersections2025-99-045312/10/2025
In order to reduce conflicts between vehicles at intersections and improve safety, an optimization model of traffic sequence allocation is studied and established for the heterogeneous traffic scenario of connected autonomous vehicles and manual vehicles. With the minimum safe traffic time as constraint, the right of way is allocated to vehicles according to the microscopic traffic characteristics of heterogeneous traffic flow fleet movement and the phase of signal lights, and the optimal trajectory planning control of each vehicle and evaluation indicators are established. A jointly simulation running environment is built using VISSIM and MATLAB. The simulation results indicate that at the micro level, collaborative control slows down the waiting time for manually driven vehicles and improves the utilization of green light travel time. At the macro level, as the penetration rate of connected autonomous vehicles increases, the sum of squares of vehicle acceleration gradually decreases, and the minimum following distance and minimum collision time of vehicles increase. It improves the overall comfort and safety level of traffic flow.
Yuan, Shoutong, Li, Zhiqiang, Liu, Tianyu, Yu, Zhengyang
Impact of Maximum Platoon Size of Connected and Automated Vehicles on Safety of On-Ramp Mixed Traffic2025-99-045412/10/2025
This study investigates how the maximum platoon size (MaxPS) of Connected and Automated Vehicles (CAVs) influences traffic safety within mixed traffic environment on freeway on-ramps. Built upon the SUMO simulation framework, a mixed traffic flow model involving CAV platoons is developed for on-ramp scenarios. This paper examines traffic conditions under varying on-ramp inflow volumes and evaluates upstream speed fluctuations in the merging area. Safety indicators such as Time Exposed Time-to-Collision (TET) and Time-Integrated time-to-Collision (TIT) are employed to assess overall traffic safety. Additionally, collision types are analyzed. Results indicate that under low on-ramp inflow conditions, a moderate MaxPS with low CAV penetration rates significantly enhances safety, whereas a larger MaxPS is preferable with high penetration rates. Under moderate on-ramp inflow, limiting the CAV MaxPS to 2 reduces conflicts. As on-ramp inflow increases further, a MaxPS of 1 or 2 leads to a lower overall collision risk across different CAV penetration rates. These findings provide insights into optimizing CAV platoon control strategies to enhance safety in mixed traffic environments.
Pan, Gongyu, Huang, Yujie, Xie, Junping
A Cooperative Conflict-Free Algorithm for Autonomous Vehicles at Unsignalized Intersections Based on Incremental Learning Monte Carlo Tree Search2025-99-043412/10/2025
The paper examines how connected automated vehicles (CAVs) can navigate unsignalized intersections—especially those where major roads differ significantly from minor roads. The proposed method uses an improved incremental learning Monte Carlo Tree Search to quickly determine an optimal passing order for vehicles, adjusting in real time based on road conditions and vehicle states. Numerical experiments demonstrate that this approach achieves conflict-free, real-time cooperative, reducing average delays significantly compared to traditional traffic signal control. Compared to fully-actuated signal control, the proposed method achieves average delay reductions of 19.92s, 16.46s, and 15.47s for CAVs across varying demand patterns. The practical application of this research lies in its potential to enhance traffic efficiency in urban areas by replacing traditional signal-based control with intelligent, autonomous intersection management. This could lead to reduced congestion, lower fuel consumption, and improved traffic safety, making it particularly valuable for smart city initiatives and future CAV-dominated transportation systems.
Xue, Yongjie, Gao, Feng, Feng, Qiang, Cui, Shaohua
Vehicle Abnormal Detection Based on Multi-Source Data Fusion and Graph Neural Networks2025-99-043012/10/2025
Traffic abnormal detection is crucial in intelligent transportation systems, while the heterogeneity and weak spatio-temporal correlation of multi-source data make it difficult for traditional methods to effectively fuse and utilize multimodal information. Most of the existing studies use data-level or decision-level fusion, which fails to fully exploit the feature complementarity of multi-source data, resulting in limited detection accuracy. To this end, we propose a multi-source data fusion anomaly detection method based on graph autoencoder (GAE) and diffusion graph neural network (DiffGNN). First, a unified data preprocessing and fusion strategy is designed to perform feature-level fusion of data from on-board sensors, infrastructures, and external environments to eliminate inconsistencies in data format, temporal alignment, and spatial distribution. Then, GAE is employed for potential graph structure feature extraction to enhance the global representation of the data on the basis of dimensionality reduction. Then, DiffGNN propagates anomalous features through the dynamic diffusion mechanism to strengthen the detection ability of local anomalous behaviors. The experimental results show that GAE-DG outperforms the traditional method for anomaly detection in multi-source data environment, with an accuracy rate of 95.24%, demonstrating stronger generalization ability and detection stability.
Wang, Yaguang, Xiao, Yujie, Ma, Ying
CAV Optimal Scheduling Method for Unsignalized Intersections Based on Rolling Ant Colony Algorithm2025-99-042812/10/2025
With the development of intelligent networking technology and autonomous driving technology, how to efficiently and safely schedule intelligent networked autonomous vehicles at signalless intersections has become a research hotspot in traffic management. Based on this, this article first designs an objective function that considers both intersection traffic efficiency and intersection traffic safety, taking into account constraints such as safe distance, speed, acceleration, etc., and constructs a signal free intersection CAV traffic scheduling model. On this basis, a model solving algorithm based on rolling ant colony algorithm is proposed. Simulation experiments show that compared with typical signal control methods, this method can significantly improve intersection traffic efficiency and reduce the number of conflicts.
Zhao, Yingjie, Liu, Xiaoming, Ma, Zechao, Wang, Yuanrong
A Study of Electro-Hydraulic Brake of Electric Automobiles and Intelligent Connected Vehicles2025-99-040212/10/2025
The traditional hydraulic braking system with vacuum booster technology is very mature, but it is not suitable for use in electric vehicles due to the lack of a vacuum source. The brake system by wire is an innovative electronic controlled braking technology, and the Electro-Hydraulic Brake is currently the most widely used brake system by wire in electric vehicles. The classification, structure, working principle, and advantages of Electro-Hydraulic Brake as a braking system for electric automobiles and intelligent connected vehicles are studied. The structure, working principle, advantages and disadvantages of Pump-Electro - Hydraulic Brake and Integrated Electro-Hydraulic Brake are compared and analyzed.
Song, Jiantong, Zhu, Chunhong, Ren, Xiaolong
Minimum Requirements to Support Traffic Signal Priority and Preemption™ SET FileJ2945/BS_202511 (Current)11/20/2025
Included in this set are the SAE J2945/B Standard which specifies the over-the-air (OTA) interface between connected vehicles (CVs) and connected intersections (CIs) to support traffic signal priority and preemption (TSPP) applications. It specifies the use of updated revisions of the SAE J2735 Signal Request Message (SRM) and Signal Status Message (SSM) and the use of a Wireless Access in Vehicular Environments (WAVE) Service Advertisement (WSA) to advertise support for TSPP at a CI. Included are a concept of operations, requirements, design, and the Abstract Syntax Notation One (ASN.1) message format, data frame, and data element definitions. Also included is the Abstract Syntax Notation One (ASN.1) file precisely defines the structure of the data used to implement applications conformant to the SAE J2945/B Standard. Using this ASN.1 specification, a compiler tool can be used to produce encodings to enable applications to easily encode and decode the Signal Request Message (SRM) and Signal Status Message (SSM) messages, along with the Wireless Access in Vehicular Environments (WAVE) Service Advertisement (WSA) application data, defined in SAE J2945/B defined in SAE J2945/B. Included in the ASN.1 file is the complete SAE J2735 ASN.1 along with the updates to the ASN.1 for the SRM and SSM defined in SAE J2945/B.
Connected Transportation Interoperability Committee
Minimum Requirements to Support Traffic Signal Priority and PreemptionJ2945/B_202511 (Current)11/20/2025
When identifying the content of this report, one of the goals was that it supports a nationally interoperable method for connected vehicles (CVs) to make traffic signal priority and/or preemption (TSPP) requests of connected intersections (CIs) that support priority and/or preemption services. Given that, this report specifies the over-the-air (OTA) interface between CVs and CIs to support TSPP applications using updated revisions of the SAE J2735 Signal Request Message (SRM) and Signal Status Message (SSM) and the use of a Wireless Access in Vehicular Environments (WAVE) Service Advertisement (WSA) to advertise support for TSPP at a CI. Included are a concept of operations, requirements, design, and message structure definitions developed using a detailed systems engineering process.
Connected Transportation Interoperability Committee
Multimodal Intelligent Transportation Data Fusion and Application Methods in Heavy-Duty Vehicles2025-99-013911/11/2025
Heavy-duty commercial vehicles (HDCVs) are the key mobile nodes in intelligent transportation systems (ITS). However, their complex operating conditions and the diversity of data sources (such as road conditions, driver behavior, traffic signals, and on-board sensors) present considerable difficulties for accurately estimating the state and perceiving the environment using a single modality of data. This requires effective multi-modal data fusion to enhance the control and decision-making capabilities of HDCVs. This paper addresses this need by proposing a customized multi-modal intelligent transportation data fusion framework for intelligent HDCVs. This paper presents a solution for establishing a multi-modal intelligent transportation data collection platform, including real-scene collection methods and simulation scene collection methods based on the SUMO-MATLAB joint simulation platform. Through three representative case studies, the application methods of multi-modal traffic data are demonstrated: vehicle speed prediction, vehicle power demand prediction, and trajectory planning. The hyperparameter optimization using an enhanced LSTM neural network with the Sparrow Search Algorithm (SSA) is achieved, resulting in more adaptable, safer, and more efficient multi-modal intelligent transportation data applications.
Chen, Zhengxian, Wang, Shaoqi, Jiang, Huiming, Zhou, Fojin, Wang, Mingqiang, Li, Jun
A Hybrid Framework for Kinematic Vehicle Prediction: You Only Look Once version 11 with Unscented Kalman Filter12-09-02-001310/22/2025
In the context of intelligent transportation systems and applications such as autonomous driving, it is essential to predict a vehicle’s immediate future states to enable precise and timely prediction of vehicles’ movements. This article proposes a hybrid short-term kinematic vehicle prediction framework that integrates a novel object detection model, You Only Look Once version 11 (YOLOv11), with an unscented Kalman filter (UKF), a reliable state estimation technique. This study provides a unique method for real-time detection of vehicles in traffic scenes, tracking and predicting their short-term kinematics. Locating the vehicle accurately and classifying it in a range of dynamic scenarios is achievable by the enhanced detection capabilities of YOLOv11. These detections are used as inputs by the UKF to estimate and predict the future positions of the vehicles while considering measurement noise and dynamic model errors. The focus of this work is on individual vehicle motion prediction using short-horizon kinematic cues. The publicly employable Lyft Level 5 dataset has been used to validate the proposed method, indicating its efficacy in attaining high prediction accuracy with low latency. The experimental results illustrate that the accuracy, precision, root mean square error (RMSE), and mean absolute error (MAE) are improved by 4.1%, 2.66%, 11.9%, and 13.3%, respectively, when the performance of the enhanced algorithm is compared to that of the YOLOv11 combined with extended Kalman filter (EKF) algorithm. Integrating YOLOv11 with the UKF leads to enhanced responsiveness and reliability of vehicle trajectory predictions, which is profitable for autonomous vehicles and advanced driver-assistance systems.
Pahal, Sudesh, Nandal, Priyanka
Key Influential Factors for Smart Logistics in Guangzhou Based on DEMATEL -AHP2025-99-002310/17/2025
Use Decision Making Trial and Evaluation Laborator (DEMATEL) and Analytic Hierarchy Process (AHP) to jointly analysis and determine the key factors of Guangzhou intelligent logistics. Through the questionnaire survey of 92 logistics enterprises in Guangzhou, it is concluded that Information infrastructure, big data, Internet of Things, artificial intelligence, Logistics dynamic updates, and Smart warehousing have a great impact on intelligent logistics. Combining practical engineering with theory to make the implementation of Guangzhou’s smart logistics project more scientific, It is characterized by a higher degree of scientificity. Moreover, it is of great warning value, which can alert relevant parties to potential issues. Meanwhile, it provides essential guidance for the implementation of the smart city project in Guangzhou, facilitating a more efficient and well - directed execution process. This study is limited to logistics business respondents in Guangzhou and may limit the generalizability of the survey results.
Zhang, Shuangshuang, Chen, Ning, Khaw, Khai Wah, Liu, Chenxi, Jin, Lili
A Distributed V2X Hardware-in-the-loop Test System Based on the PanoSim for Intelligent Connected Vehicles2025-99-004110/17/2025
Based on the similarity analysis of Intelligent Connected Vehicles (ICVs), a distributed V2X hardware-in-the-loop test system for ICVs is designed, including the PanoSim autonomous driving simulation engine, GNSS simulator, V2X simulator, and management and cooperative control software. The system integrates the major technologies of distributed interaction, including operation management, time synchronization, coordinate conversion, and data preprocessing, and realizes the spatial and temporal consistency of each simulation node. 89 V2X first-stage application scenarios (e.g., FCW, RLVW) and 5 V2X second-stage application scenarios (e.g., CLC) use case experimental results have proved the reliability of the system. The FCW use case experiment results show that its simulation results pass with high confidence. The study emphasizes the value of the system in reducing development costs, improving safety, and accelerating the deployment of V2X applications, while identifying future research directions such as lateral distance impact analysis.
Gao, Tianfang, Zhang, XingHui, Chen, Liang, Huang, Zhichen, Ni, Dong
Decision-Making Research for Intelligent Connected Vehicles Considering Adhesion Condition2025-99-002810/17/2025
The adhesion condition of the road surface is an important factor in the driving decision-making, and the lower the adhesion coefficient of the road, the greater the risk of safety. In order to study the development and progress in the research of the substances, a comparative analysis of Chinese and foreign references was carried out. The sensitive factors to the adhesion coefficient and influence of adhesion condition on driving were summarized. Then two main strategies to avoid a collision were presented, including longitudinal braking and lateral lane change. A detailed description of three methods used in automotive decision-making processes was offered, including rule-based method, supervised learning method, and reinforcement learning method, each characterized with certain attributes. Topics in the field of driving decision-making considering adhesion condition for intelligent connected vehicles were pointed out and future-oriented research formulations were provided. These results indicate that (1) Factors about roads are lacking in driving behaviour decision-making research. Thus, the studies on driving decision-making under different road conditions need to be carried out to improve the adaptability of driving decision-making systems in complex road environment. (2) Studies on cooperative driving decision-making in intelligent connected vehicles are important to enhance the efficiency in dealing with various complicated and dangerous traffic environments. There is a need for extended work on cooperative driving strategies. (3) There are also some issues in reinforcing learning with the driving decision-making, such as difficulty of realization. Accordingly, the introduction of human feedback-driven reinforcement learning is favored in the study about driving decision-making. This method is believed to improve the decision-making performance of systems and speed up the transition of research to the real-world interception.
Wang, Hong, Hou, De-Zao
The Traffic Light Control Strategy at Intersections in Intelligent Transportation Systems2025-99-000910/17/2025
This thesis explores strategies for controlling traffic signals at intersections within the context of ITS., emphasizing the role of DRL in optimizing traffic flow. In recent years, urbanization and the rapid increase in vehicle numbers in China have exacerbated traffic congestion, significantly hindering urban development. This study explores innovative approaches to alleviating traffic congestion, focusing on smart traffic signal systems that adjust according to real-time traffic conditions. The research reviews fundamental concepts in traffic signal control, including traffic flow, signal phases, and signal cycles, and investigates how DRL can dynamically adjust traffic light cycles to optimize intersection performance. The findings suggest that DRL provides an effective method for managing complex and unpredictable traffic environments, as it enables systems to self-learn and continuously refine their strategies based on environmental changes. The adoption of this technology holds the potential to greatly optimize traffic flow, alleviate congestion, and boost the performance of urban transportation systems. The study concludes that signal control strategies based on DRL present a viable approach to tackling the issues associated with growing traffic and urban congestion.
Liu, Junao, Zuo, Tingyou
2025 5th International Conference on Smart City Engineering and Public Transportation (SCEPT)2025-99-000110/17/2025
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Adaptive Path Planning and Control of Autonomous Sweeping Vehicle Considering the Error of Perception10-09-04-00358/19/2025
This article presents a path planning and control method for a cost-effective autonomous sweeping vehicle operating in enclosed campus. First, to address the challenges from perception, an effective obstacle filtering algorithm is proposed, considering the elimination of false detection and correction of object position. Based on it, the adaptive sampling–based path planner and pure pursuit controller are developed. Not only an adaptive cost-weighting mechanism is introduced by TOPSIS algorithm to determine the desired trajectory as a multi-objective optimization problem, but also the adaptive preview distance is designed according to the trajectory curvature and vehicle state. The real-vehicle tests are implemented in typical scenario. The results show that the 87.8% effective edge-following rate is achieved in curved paths, and 22.93% cleaning coverage is improved for cleaning coverage. Therefore, the proposed method is effective and reliable for cost-effective autonomous sweeping vehicle.
Lei, WuKun, Yang, Bo, Pei, Xiaofei, Zhang, Yang, Zhou, HongLong
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