Accurate driving risk prediction is essential for preventing traffic accidents, particularly in complex mountain tunnel environments where conventional assessment methods often fall short. This study presents a novel approach for quantifying and predicting driving risk under multi-vehicle interactions scenarios. A weighted comprehensive risk matrix is constructed by integrating Time-to-Collision (TTC) and Interaction Strength (IS), taking into account the behavior of surrounding vehicles. A risk representation framework centered on the ego vehicle and a multi-level risk classification scheme are proposed. To capture the spatial and temporal dynamics of driving risk, a hybrid deep learning model is proposed, combining Convolutional Neural Networks (CNN), Long Short-Term Memory networks (LSTM), and Attention mechanisms. The model was validated using real-world trajectory data from six mountain tunnels in Chongqing, China. The model achieved prediction accuracies of 83% in car-following and 76% in lane-changing scenarios, outperforming traditional methods. The proposed model significantly enhances the identification and prediction of abnormal driving behaviors under highly interaction conditions, offering a valuable tool to improve intelligent driving safety in mountain tunnels.
{"title":"A Risk Identification and Prediction Model for Intelligent Driving Under Multi-Vehicle Interactions in Mountain Tunnel Environments","authors":"Xiaoyu Cai;Minghua Zhang;Cailin Lei;Ling Jin;Bo Peng","doi":"10.1109/OJITS.2025.3647953","DOIUrl":"https://doi.org/10.1109/OJITS.2025.3647953","url":null,"abstract":"Accurate driving risk prediction is essential for preventing traffic accidents, particularly in complex mountain tunnel environments where conventional assessment methods often fall short. This study presents a novel approach for quantifying and predicting driving risk under multi-vehicle interactions scenarios. A weighted comprehensive risk matrix is constructed by integrating Time-to-Collision (TTC) and Interaction Strength (IS), taking into account the behavior of surrounding vehicles. A risk representation framework centered on the ego vehicle and a multi-level risk classification scheme are proposed. To capture the spatial and temporal dynamics of driving risk, a hybrid deep learning model is proposed, combining Convolutional Neural Networks (CNN), Long Short-Term Memory networks (LSTM), and Attention mechanisms. The model was validated using real-world trajectory data from six mountain tunnels in Chongqing, China. The model achieved prediction accuracies of 83% in car-following and 76% in lane-changing scenarios, outperforming traditional methods. The proposed model significantly enhances the identification and prediction of abnormal driving behaviors under highly interaction conditions, offering a valuable tool to improve intelligent driving safety in mountain tunnels.","PeriodicalId":100631,"journal":{"name":"IEEE Open Journal of Intelligent Transportation Systems","volume":"7 ","pages":"155-165"},"PeriodicalIF":5.3,"publicationDate":"2025-12-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11313849","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145929519","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-11DOI: 10.1109/OJITS.2025.3643797
Atharva Ghate;Olamide Akinyele;Qilun Zhu;Robert Prucka;Miriam A. Figueroa-Santos;Morgan J. Barron;Matthew P. Castanier
Autonomous off-road navigation requires coping with unstructured terrain, intermittent obstacles, and tight real-time computational constraints, challenges that often exceed the capabilities of conventional motion-planning and control pipelines. This paper proposes the Cascaded Reinforcement Learning and Model Predictive Path Integral (CRM) framework, which integrates a curriculum-trained Reinforcement Learning (RL) critic for global planning with a fallback-enabled Model Predictive Path Integral (MPPI) controller for local refinement. Unlike prior RL-MPPI methods, the proposed approach incrementally teaches the RL critic obstacle avoidance, rollover prevention, and traction constraints, thereby improving the accuracy of terminal cost estimates. To safeguard against unconverged RL outputs in new or out-of-distribution states, we embed a logic-based fallback that reverts MPPI to baseline costs whenever the RL-driven terminal value is judged unreliable. In simulations on representative of off-road environments, CRM achieves success rates higher by 70%, lowers sample requirements up to 90% compared to MPPI alone, and avoids collisions more effectively than standalone RL methods. These results underscore the necessity of curriculum-informed critics and robust fallback strategies for safe and efficient off-road autonomy.
{"title":"Cascaded RL-MPPI Framework for Off-Road Vehicles: Integrating Global Maps and SLAM","authors":"Atharva Ghate;Olamide Akinyele;Qilun Zhu;Robert Prucka;Miriam A. Figueroa-Santos;Morgan J. Barron;Matthew P. Castanier","doi":"10.1109/OJITS.2025.3643797","DOIUrl":"https://doi.org/10.1109/OJITS.2025.3643797","url":null,"abstract":"Autonomous off-road navigation requires coping with unstructured terrain, intermittent obstacles, and tight real-time computational constraints, challenges that often exceed the capabilities of conventional motion-planning and control pipelines. This paper proposes the Cascaded Reinforcement Learning and Model Predictive Path Integral (CRM) framework, which integrates a curriculum-trained Reinforcement Learning (RL) critic for global planning with a fallback-enabled Model Predictive Path Integral (MPPI) controller for local refinement. Unlike prior RL-MPPI methods, the proposed approach incrementally teaches the RL critic obstacle avoidance, rollover prevention, and traction constraints, thereby improving the accuracy of terminal cost estimates. To safeguard against unconverged RL outputs in new or out-of-distribution states, we embed a logic-based fallback that reverts MPPI to baseline costs whenever the RL-driven terminal value is judged unreliable. In simulations on representative of off-road environments, CRM achieves success rates higher by 70%, lowers sample requirements up to 90% compared to MPPI alone, and avoids collisions more effectively than standalone RL methods. These results underscore the necessity of curriculum-informed critics and robust fallback strategies for safe and efficient off-road autonomy.","PeriodicalId":100631,"journal":{"name":"IEEE Open Journal of Intelligent Transportation Systems","volume":"7 ","pages":"41-60"},"PeriodicalIF":5.3,"publicationDate":"2025-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11298481","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145830788","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-09DOI: 10.1109/OJITS.2025.3642136
Yongjia Nian;Hao Liu;Renwen Chen;Xintong Hou;Aocheng He
This paper investigates the challenge of topology optimization for UAV swarms in dynamic environments and proposes a reinforcement learning窶電riven distributed framework. Under the centralized training and decentralized execution (CTDE) paradigm, a MADDPG-based topology reconfiguration algorithm is developed that integrates partial observability with a bi-directional interest game, enabling nodes to achieve distributed Nash equilibrium decisions under local information constraints. At the communication layer, a channel model, topology maintenance scheme, and CSDMA-based distributed slot allocation process are introduced to ensure reliable connectivity in the presence of interference and dynamic node access. Simulation results show that the proposed method attains faster convergence, greater robustness, lower communication latency, and higher path efficiency than benchmark approaches such as MST and PSO, with reconfiguration completed within milliseconds. These results highlight both the effectiveness and scalability of the framework for large-scale swarm networking. Beyond its theoretical contributions, the approach holds practical promise for deployment in critical scenarios such as emergency communications, disaster relief, and mission-critical operations, offering a viable pathway toward intelligent UAV swarm networks.
{"title":"Distributed Real-Time Topology Reconfiguration for UAV Swarms via MADDPG","authors":"Yongjia Nian;Hao Liu;Renwen Chen;Xintong Hou;Aocheng He","doi":"10.1109/OJITS.2025.3642136","DOIUrl":"https://doi.org/10.1109/OJITS.2025.3642136","url":null,"abstract":"This paper investigates the challenge of topology optimization for UAV swarms in dynamic environments and proposes a reinforcement learning窶電riven distributed framework. Under the centralized training and decentralized execution (CTDE) paradigm, a MADDPG-based topology reconfiguration algorithm is developed that integrates partial observability with a bi-directional interest game, enabling nodes to achieve distributed Nash equilibrium decisions under local information constraints. At the communication layer, a channel model, topology maintenance scheme, and CSDMA-based distributed slot allocation process are introduced to ensure reliable connectivity in the presence of interference and dynamic node access. Simulation results show that the proposed method attains faster convergence, greater robustness, lower communication latency, and higher path efficiency than benchmark approaches such as MST and PSO, with reconfiguration completed within milliseconds. These results highlight both the effectiveness and scalability of the framework for large-scale swarm networking. Beyond its theoretical contributions, the approach holds practical promise for deployment in critical scenarios such as emergency communications, disaster relief, and mission-critical operations, offering a viable pathway toward intelligent UAV swarm networks.","PeriodicalId":100631,"journal":{"name":"IEEE Open Journal of Intelligent Transportation Systems","volume":"7 ","pages":"74-92"},"PeriodicalIF":5.3,"publicationDate":"2025-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11288063","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145886652","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Timely ambulance allocation is essential for Emergency Medical Services (EMS) to deliver life-saving care effectively. Conventional methods often struggle to adapt to the unpredictable nature and locations of emergencies. Within EMS, efficient resource management is crucial for ensuring rapid and effective responses. While much emphasis has been placed on optimizing the deployment of ambulances from fixed stations, managing specialized critical care response units—known as Charlie vehicles in Qatar EMS—presents a distinct challenge. These rapid response cars are vital for providing advanced care in challenging situations, and their dynamic deployment requires a more flexible management strategy. Effectively relocating Charlie vehicles to areas with high anticipated demand after they have responded to an emergency introduces unique challenges that differ from traditional ambulance redeployment approaches. This paper proposes a novel dynamic redeployment system specifically for optimizing the allocation of critical care response vehicles, including those involved in patient transfers. Utilizing a Deep Reinforcement Learning (DRL) framework, we create a deep scoring network that prioritizes and navigates various dynamic factors at each station. Experiments using real-world data from Qatar EMS demonstrate that our system significantly outperforms existing methods. For instance, our approach achieves faster average response times and improved critical response rates compared to the leading baseline method. Notably, we observe a substantial 21.55% reduction in average response time (AveRT) and an 18.34% increase in relative response time (RelaRT) in comparison to actual operational metrics. Our approach effectively shortens the time needed to reach patients, thereby increasing the likelihood of timely treatment and improving overall patient care outcomes.
{"title":"A Dynamic Redeployment System for Critical Care Paramedic Units in Qatar Utilizing Deep Reinforcement Learning","authors":"Reem Tluli;Ahmed Badawy;Saeed Salem;Muhammad Hardan;Sailesh Chauhan;Guillaume Alinier","doi":"10.1109/OJITS.2025.3642001","DOIUrl":"https://doi.org/10.1109/OJITS.2025.3642001","url":null,"abstract":"Timely ambulance allocation is essential for Emergency Medical Services (EMS) to deliver life-saving care effectively. Conventional methods often struggle to adapt to the unpredictable nature and locations of emergencies. Within EMS, efficient resource management is crucial for ensuring rapid and effective responses. While much emphasis has been placed on optimizing the deployment of ambulances from fixed stations, managing specialized critical care response units—known as Charlie vehicles in Qatar EMS—presents a distinct challenge. These rapid response cars are vital for providing advanced care in challenging situations, and their dynamic deployment requires a more flexible management strategy. Effectively relocating Charlie vehicles to areas with high anticipated demand after they have responded to an emergency introduces unique challenges that differ from traditional ambulance redeployment approaches. This paper proposes a novel dynamic redeployment system specifically for optimizing the allocation of critical care response vehicles, including those involved in patient transfers. Utilizing a Deep Reinforcement Learning (DRL) framework, we create a deep scoring network that prioritizes and navigates various dynamic factors at each station. Experiments using real-world data from Qatar EMS demonstrate that our system significantly outperforms existing methods. For instance, our approach achieves faster average response times and improved critical response rates compared to the leading baseline method. Notably, we observe a substantial 21.55% reduction in average response time (AveRT) and an 18.34% increase in relative response time (RelaRT) in comparison to actual operational metrics. Our approach effectively shortens the time needed to reach patients, thereby increasing the likelihood of timely treatment and improving overall patient care outcomes.","PeriodicalId":100631,"journal":{"name":"IEEE Open Journal of Intelligent Transportation Systems","volume":"7 ","pages":"93-109"},"PeriodicalIF":5.3,"publicationDate":"2025-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11288020","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145886653","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Ensuring that longitudinal control in autonomous driving is accurate, robust, and smooth is key to enhance vehicle autonomy and reduce driver intervention, improving user acceptance of autonomous vehicles. Vehicles have complex dynamics that make accurately following the speed reference in various driving situations a challenging task. Model-Free Control (MFC) has shown its performance and robustness in systems which are difficult to model or with time-varying dynamics, making it relevant for this application. In this paper, a cascade control architecture based on MFC is proposed. This strategy keeps the MFC principle of simplicity in control while, due to the cascade structure, using all the information generated by the motion planner and the measured speed and acceleration, which are easy to obtain. Regulators with this structure have been systematically designed to keep the tracking quality, safety and passenger comfort in a wide variety of driving situations.These regulators have been evaluated both in simulation and real-world scenarios, showing improvements in robustness and performance when compared with the baseline.
{"title":"Model-Free Speed Tracking Control for Automated Cars","authors":"Marcos Moreno-Gonzalez;Antonio Artuñedo;Jorge Villagra","doi":"10.1109/OJITS.2025.3640943","DOIUrl":"https://doi.org/10.1109/OJITS.2025.3640943","url":null,"abstract":"Ensuring that longitudinal control in autonomous driving is accurate, robust, and smooth is key to enhance vehicle autonomy and reduce driver intervention, improving user acceptance of autonomous vehicles. Vehicles have complex dynamics that make accurately following the speed reference in various driving situations a challenging task. Model-Free Control (MFC) has shown its performance and robustness in systems which are difficult to model or with time-varying dynamics, making it relevant for this application. In this paper, a cascade control architecture based on MFC is proposed. This strategy keeps the MFC principle of simplicity in control while, due to the cascade structure, using all the information generated by the motion planner and the measured speed and acceleration, which are easy to obtain. Regulators with this structure have been systematically designed to keep the tracking quality, safety and passenger comfort in a wide variety of driving situations.These regulators have been evaluated both in simulation and real-world scenarios, showing improvements in robustness and performance when compared with the baseline.","PeriodicalId":100631,"journal":{"name":"IEEE Open Journal of Intelligent Transportation Systems","volume":"7 ","pages":"1-15"},"PeriodicalIF":5.3,"publicationDate":"2025-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11278736","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145886651","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
With the rapid advancement of autonomous driving technology, the achievement of complex trajectory prediction for human-like driving behaviors has become a critical research focus. Traditional data-driven models exhibit substantial limitations in replicating human driving logic and cognitive processes, constraining their adaptability and robustness across diverse driving scenarios. This study proposes and validates a novel Data-knowledge-driven Human-like logic Trajectory Prediction model (DHTP) using a bidirectional hybrid modeling approach. It incorporates an attention mechanism, memory reasoning, and autonomous evolution modules. The performance is assessed using multiple quantitative metrics and experimentally validated in real-world driving scenarios, including the urban expressway and highway weaving areas. The experimental results show that the DHTP model significantly outperforms the baseline model, showcasing enhanced accuracy and robustness across diverse driving conditions. Additionally, it rapidly converges to the global optimal solution, particularly in highly dynamic environments. The results indicate that optimizing the attention mechanism and autonomous evolution module allows the DHTP model to successfully simulate human driving logic and behavioral patterns. This study can help to facilitate AV-HV interaction and supports cognitive module advancement toward autonomy.
{"title":"Incomprehensible But Intelligible Human Logics: Toward a Data-Knowledge-Driven Trajectory Prediction Model","authors":"Jiming Xie;Jianhua Li;Yaqin Qin;Jiachen Ren;Hongjian Liang;Liang Chen;Yulan Xia","doi":"10.1109/OJITS.2025.3640704","DOIUrl":"https://doi.org/10.1109/OJITS.2025.3640704","url":null,"abstract":"With the rapid advancement of autonomous driving technology, the achievement of complex trajectory prediction for human-like driving behaviors has become a critical research focus. Traditional data-driven models exhibit substantial limitations in replicating human driving logic and cognitive processes, constraining their adaptability and robustness across diverse driving scenarios. This study proposes and validates a novel Data-knowledge-driven Human-like logic Trajectory Prediction model (DHTP) using a bidirectional hybrid modeling approach. It incorporates an attention mechanism, memory reasoning, and autonomous evolution modules. The performance is assessed using multiple quantitative metrics and experimentally validated in real-world driving scenarios, including the urban expressway and highway weaving areas. The experimental results show that the DHTP model significantly outperforms the baseline model, showcasing enhanced accuracy and robustness across diverse driving conditions. Additionally, it rapidly converges to the global optimal solution, particularly in highly dynamic environments. The results indicate that optimizing the attention mechanism and autonomous evolution module allows the DHTP model to successfully simulate human driving logic and behavioral patterns. This study can help to facilitate AV-HV interaction and supports cognitive module advancement toward autonomy.","PeriodicalId":100631,"journal":{"name":"IEEE Open Journal of Intelligent Transportation Systems","volume":"7 ","pages":"412-433"},"PeriodicalIF":5.3,"publicationDate":"2025-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11278737","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146175851","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-03DOI: 10.1109/OJITS.2025.3639557
Aditya Haryanto;Ondřej Vaculín
Recent advances in sensor and computing technologies have enabled road side units (RSUs) to not only monitor traffic flow but also process data in real time to improve road safety. However, leveraging RSUs for proactive accident detection remains a challenging and underexplored task, partly due to the lack of diverse accident data. To address this, this study proposes two key contributions: (i) a scenario-based synthetic data generation framework, and (ii) YoFlow, a novel system for vehicle-to-vehicle accident detection from a simulated RSU camera perspective. The proposed framework leverages the PEGASUS method for scenario generation strategy and BeamNG.tech for generating synthetic traffic videos. This approach led to the development of the SB-SIF dataset, which includes five representative intersection crash scenarios derived from German accident data. The SB-SIF dataset contains 914 crash videos, 123 near-miss events, and 924 normal traffic instances and is publicly available at: https://doi.org/10.5281/zenodo.15267252. The proposed YoFlow system identifies accidents by analyzing temporal variations in vehicle speed vectors, using YOLO for vehicle classification and CUDA-accelerated dense optical flow to capture abrupt motion changes. The extracted features are processed and classified using an XGBoost model, achieving 94% recall and 90% precision in accident detection.
{"title":"YoFlow Method for Scenario-Based Automatic Accident Detection","authors":"Aditya Haryanto;Ondřej Vaculín","doi":"10.1109/OJITS.2025.3639557","DOIUrl":"https://doi.org/10.1109/OJITS.2025.3639557","url":null,"abstract":"Recent advances in sensor and computing technologies have enabled road side units (RSUs) to not only monitor traffic flow but also process data in real time to improve road safety. However, leveraging RSUs for proactive accident detection remains a challenging and underexplored task, partly due to the lack of diverse accident data. To address this, this study proposes two key contributions: (i) a scenario-based synthetic data generation framework, and (ii) YoFlow, a novel system for vehicle-to-vehicle accident detection from a simulated RSU camera perspective. The proposed framework leverages the PEGASUS method for scenario generation strategy and BeamNG.tech for generating synthetic traffic videos. This approach led to the development of the SB-SIF dataset, which includes five representative intersection crash scenarios derived from German accident data. The SB-SIF dataset contains 914 crash videos, 123 near-miss events, and 924 normal traffic instances and is publicly available at: <uri>https://doi.org/10.5281/zenodo.15267252</uri>. The proposed YoFlow system identifies accidents by analyzing temporal variations in vehicle speed vectors, using YOLO for vehicle classification and CUDA-accelerated dense optical flow to capture abrupt motion changes. The extracted features are processed and classified using an XGBoost model, achieving 94% recall and 90% precision in accident detection.","PeriodicalId":100631,"journal":{"name":"IEEE Open Journal of Intelligent Transportation Systems","volume":"7 ","pages":"61-73"},"PeriodicalIF":5.3,"publicationDate":"2025-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11277281","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145886650","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-03DOI: 10.1109/OJITS.2025.3640002
Mojtaba Jafarian Abyaneh;Jinwoo Jang
With the rise of intelligent systems in urban transportation, the ability to predict agent behavior in real time has gained increasing research attention. Accurate trajectory prediction plays an important role in improving safety and decision-making in self-driving vehicles and smart city infrastructure. This study focuses on LiDAR-sensor-based trajectory prediction of agents at a hyperlocal level using a Transformer architecture. A large-scale dataset was collected using an Ouster OS1 LiDAR sensor at a busy urban intersection in West Palm Beach, Florida. This experiment captured more than 12,390 real-world trajectories which include vehicles, pedestrians, and bicycles. After obtaining experimental results from the sensor, the proposed framework first performs object detection to extract agent trajectories from LiDAR point-cloud data. Afterwards, data curation was performed to filter out the reflections of pedestrians and vehicles on the glass storefronts, or they were almost stationary. In the next stage, a Transformer model is developed to learn and predict spatial-temporal patterns of agent trajectories. By performing a hyperparameter tuning, the Transformer model was able to achieve a 15.24% improvement in the average displacement error in comparison with the traditional LSTM method. Results are visualized to display predicted and ground-truth paths on a geo-referenced map. With a higher convergence rate compared to the LSTM approach, the proposed results showed the effectiveness of attention-based models in complex multi-agent urban environments.
{"title":"Transformer-Based Trajectory Prediction Using LiDAR Data for Situational Awareness in Complex Urban Environments","authors":"Mojtaba Jafarian Abyaneh;Jinwoo Jang","doi":"10.1109/OJITS.2025.3640002","DOIUrl":"https://doi.org/10.1109/OJITS.2025.3640002","url":null,"abstract":"With the rise of intelligent systems in urban transportation, the ability to predict agent behavior in real time has gained increasing research attention. Accurate trajectory prediction plays an important role in improving safety and decision-making in self-driving vehicles and smart city infrastructure. This study focuses on LiDAR-sensor-based trajectory prediction of agents at a hyperlocal level using a Transformer architecture. A large-scale dataset was collected using an Ouster OS1 LiDAR sensor at a busy urban intersection in West Palm Beach, Florida. This experiment captured more than 12,390 real-world trajectories which include vehicles, pedestrians, and bicycles. After obtaining experimental results from the sensor, the proposed framework first performs object detection to extract agent trajectories from LiDAR point-cloud data. Afterwards, data curation was performed to filter out the reflections of pedestrians and vehicles on the glass storefronts, or they were almost stationary. In the next stage, a Transformer model is developed to learn and predict spatial-temporal patterns of agent trajectories. By performing a hyperparameter tuning, the Transformer model was able to achieve a 15.24% improvement in the average displacement error in comparison with the traditional LSTM method. Results are visualized to display predicted and ground-truth paths on a geo-referenced map. With a higher convergence rate compared to the LSTM approach, the proposed results showed the effectiveness of attention-based models in complex multi-agent urban environments.","PeriodicalId":100631,"journal":{"name":"IEEE Open Journal of Intelligent Transportation Systems","volume":"7 ","pages":"16-28"},"PeriodicalIF":5.3,"publicationDate":"2025-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11277286","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145754196","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
As automated vehicles continue to evolve, teleoperation is emerging as a fallback solution in edge-case scenarios where human intervention is required. To ensure effective and safe remote support, the design of human-machine interfaces (HMIs) must be centered around the needs and capabilities of the operator. In recent years, various graphical user interfaces (GUIs) for teleoperation have been developed and predominantly evaluated in simulation environments. An integrated investigation of display and interaction concepts in combination with real vehicle teleoperation remains lacking. This work addresses this gap by investigating a GUI in three different layout options for two teleoperation concepts: Direct Control using Steering Wheel and Pedals, and Trajectory Guidance through separate path and velocity input via Mouse and Keyboard or Touchscreen. The conducted user study (N $ = 45$ ) evaluates these approaches using a 1:10 scaled vehicle in a controlled environment to enable the collection of metrics, such as collisions, in challenging scenarios without the intervention of a safety driver or incurring high consequential costs. The evaluation shows that different interaction concepts favor different GUI layouts. For Steering Wheel and Pedals, a Picture-in-Picture layout is preferred, whereas for sequential input via Touchscreen or Mouse and Keyboard, a Horizontal split layout proves more suitable. Additionally, it emphasizes the advantage of Direct Control via Steering Wheel and Pedals as being significantly faster than Trajectory Guidance using a Touchscreen or Mouse and Keyboard. Overall, participants consider the user interface acceptable in terms of usability and workload. The participants’ feedback provides valuable insights and design suggestions for further improvements, serving as a foundation for future research.
{"title":"A User-Centered Teleoperation GUI for Automated Vehicles: Application and Comparison of Teleoperation HMIs","authors":"Maria-Magdalena Wolf;Niklas Krauss;Michael Christl;Kai-Fabian Treder;Frank Diermeyer","doi":"10.1109/OJITS.2025.3639765","DOIUrl":"https://doi.org/10.1109/OJITS.2025.3639765","url":null,"abstract":"As automated vehicles continue to evolve, teleoperation is emerging as a fallback solution in edge-case scenarios where human intervention is required. To ensure effective and safe remote support, the design of human-machine interfaces (HMIs) must be centered around the needs and capabilities of the operator. In recent years, various graphical user interfaces (GUIs) for teleoperation have been developed and predominantly evaluated in simulation environments. An integrated investigation of display and interaction concepts in combination with real vehicle teleoperation remains lacking. This work addresses this gap by investigating a GUI in three different layout options for two teleoperation concepts: Direct Control using Steering Wheel and Pedals, and Trajectory Guidance through separate path and velocity input via Mouse and Keyboard or Touchscreen. The conducted user study (N <inline-formula> <tex-math>$ = 45$ </tex-math></inline-formula>) evaluates these approaches using a 1:10 scaled vehicle in a controlled environment to enable the collection of metrics, such as collisions, in challenging scenarios without the intervention of a safety driver or incurring high consequential costs. The evaluation shows that different interaction concepts favor different GUI layouts. For Steering Wheel and Pedals, a Picture-in-Picture layout is preferred, whereas for sequential input via Touchscreen or Mouse and Keyboard, a Horizontal split layout proves more suitable. Additionally, it emphasizes the advantage of Direct Control via Steering Wheel and Pedals as being significantly faster than Trajectory Guidance using a Touchscreen or Mouse and Keyboard. Overall, participants consider the user interface acceptable in terms of usability and workload. The participants’ feedback provides valuable insights and design suggestions for further improvements, serving as a foundation for future research.","PeriodicalId":100631,"journal":{"name":"IEEE Open Journal of Intelligent Transportation Systems","volume":"6 ","pages":"1667-1684"},"PeriodicalIF":5.3,"publicationDate":"2025-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11277269","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145729456","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Failures of safety-critical systems such as highly automated cars may result in loss of life, significant property damage, or environmental harm. Their trustworthiness and acceptance by society relies on safe operation, i.e., they have to be safer than their human-controlled counterparts, which is called the positive risk balance and which is a prerequisite for the operation in the EU. Hence, guaranteeing sufficient safety is a crucial task that requires rigorous examination. However, critical events such as severe accidents are assumed to occur with probabilities of order $10^{-6}$ or less. For this, automated simulation-based approaches for the purpose of statistical model checking contribute significantly to quantitative safety assessment. Common methods such as pure Monte Carlo simulation are inadequate to estimate the probability of these rare critical events due to excessively high simulation budget required. To overcome this, we provide a mathematical framework for combining an optimization algorithm, here from the family of optimistic optimization algorithms, with importance sampling in order to assess the safety of these systems quantitatively. Our methodology relies on a given criticality function that assesses each state of the underlying deterministic system regarding prescribed safety requirements. Applying the approach to a common test function and a simulated braking scenario using the software SILAB showcases that our method significantly reduces the required effort to quantify acceptable risk levels, compared to pure Monte Carlo simulation.
{"title":"Verifying Safety of Safety-Critical Systems With Rare Events via Optimistic Optimization","authors":"Tabea Henning-Günther;Daniel Grujic;Tino Werner;Lars Weber;Birte Neurohr;Eike Möhlmann","doi":"10.1109/OJITS.2025.3638166","DOIUrl":"https://doi.org/10.1109/OJITS.2025.3638166","url":null,"abstract":"Failures of safety-critical systems such as highly automated cars may result in loss of life, significant property damage, or environmental harm. Their trustworthiness and acceptance by society relies on safe operation, i.e., they have to be safer than their human-controlled counterparts, which is called the positive risk balance and which is a prerequisite for the operation in the EU. Hence, guaranteeing sufficient safety is a crucial task that requires rigorous examination. However, critical events such as severe accidents are assumed to occur with probabilities of order <inline-formula> <tex-math>$10^{-6}$ </tex-math></inline-formula> or less. For this, automated simulation-based approaches for the purpose of statistical model checking contribute significantly to quantitative safety assessment. Common methods such as pure Monte Carlo simulation are inadequate to estimate the probability of these rare critical events due to excessively high simulation budget required. To overcome this, we provide a mathematical framework for combining an optimization algorithm, here from the family of optimistic optimization algorithms, with importance sampling in order to assess the safety of these systems quantitatively. Our methodology relies on a given criticality function that assesses each state of the underlying deterministic system regarding prescribed safety requirements. Applying the approach to a common test function and a simulated braking scenario using the software SILAB showcases that our method significantly reduces the required effort to quantify acceptable risk levels, compared to pure Monte Carlo simulation.","PeriodicalId":100631,"journal":{"name":"IEEE Open Journal of Intelligent Transportation Systems","volume":"6 ","pages":"1569-1579"},"PeriodicalIF":5.3,"publicationDate":"2025-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11271315","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145674670","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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