IEEE Transactions on Intelligent Transportation Systems最新文献_第10页

Exploring the Influence of Driving Context on Lateral Driving Style Preferences: A Simulator-Based Study

IF 7.9 1区工程技术

IEEE Transactions on Intelligent Transportation Systems Pub Date : 2025-02-04 DOI: 10.1109/TITS.2025.3534879

Johann Haselberger;Maximilian Böhle;Bernhard Schick;Steffen Müller

{"title":"Exploring the Influence of Driving Context on Lateral Driving Style Preferences: A Simulator-Based Study","authors":"Johann Haselberger;Maximilian Böhle;Bernhard Schick;Steffen Müller","doi":"10.1109/TITS.2025.3534879","DOIUrl":"https://doi.org/10.1109/TITS.2025.3534879","url":null,"abstract":"Technological advancements focus on developing comfortable and acceptable driving characteristics in autonomous vehicles. Present driving functions predominantly possess predefined parameters, and there is no universally accepted driving style for autonomous vehicles. While driving may be technically safe and the likelihood of road accidents is reduced, passengers may still feel insecure due to a mismatch in driving styles between the human and the autonomous system. Incorporating driving style preferences into automated vehicles enhances acceptance, reduces uncertainty, and poses the opportunity to expedite their adoption. Despite the increased research focus on driving styles, there remains a need for comprehensive studies investigating how variations in the driving context impact the assessment of automated driving functions. Therefore, this work evaluates lateral driving style preferences for autonomous vehicles on rural roads, considering different weather and traffic situations. A controlled study was conducted with a variety of German participants utilizing a high-fidelity driving simulator. The participants experienced four different driving styles, including mimicking of their own driving behavior under two weather conditions. A notable preference for a more passive driving style became evident based on statistical analyses of participants’ responses during and after the drives. This study could not confirm the hypothesis that people prefer to be driven by mimicking their own driving behavior. Furthermore, the study illustrated that weather conditions and oncoming traffic substantially influence the perceived comfort during autonomous rides. The gathered dataset is openly accessible at <uri>https://www.kaggle.com/datasets/jhaselberger/idcld-subject-study-on-driving-style-preferences</uri>.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"26 4","pages":"5448-5466"},"PeriodicalIF":7.9,"publicationDate":"2025-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10870573","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143726399","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Risk Assessment of Cyclists in the Mixed Traffic Based on Multilevel Graph Representation

IF 7.9 1区工程技术

IEEE Transactions on Intelligent Transportation Systems Pub Date : 2025-01-30 DOI: 10.1109/TITS.2025.3532679

Gege Cui;Chao Lu;Yupei Liu;Junbin Wang;Xianghao Meng;Jianwei Gong

{"title":"Risk Assessment of Cyclists in the Mixed Traffic Based on Multilevel Graph Representation","authors":"Gege Cui;Chao Lu;Yupei Liu;Junbin Wang;Xianghao Meng;Jianwei Gong","doi":"10.1109/TITS.2025.3532679","DOIUrl":"https://doi.org/10.1109/TITS.2025.3532679","url":null,"abstract":"Accurate assessment of the cyclist risk is a crucial task for the safety system of autonomous vehicles (AVs). This paper proposes a framework for defining and evaluating cyclist risk levels, considering behavioral cues. The framework comprises three modules: the cyclist graph construction (CGC) module, the risk label generation (RLG) module, and the risk assessment (RA) module. The CGC module constructs a spatiotemporal graph model of the cyclist with both the behavioral and risk information. The RLG module leverages the graph representation method (GRM) to extract features and assigns risk labels using unsupervised learning. The RA module employs spatiotemporal graph convolutional networks (ST-GCN) to extract features from the cyclist graph. Additionally, it facilitates feature fusion through interactions between the human body and the two-wheeler and between hierarchical levels. The fused features, along with the risk labels, are used to train a classifier for the risk assessment of cyclists. The proposed framework is validated using real-world data, and the comparative results with state-of-the-art methods demonstrate the effectiveness and accuracy of the proposed approach in cyclist risk assessment in mixed traffic.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"26 4","pages":"5196-5210"},"PeriodicalIF":7.9,"publicationDate":"2025-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143740262","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

CAN-Trace Attack: Exploit CAN Messages to Uncover Driving Trajectories

IF 7.9 1区工程技术

IEEE Transactions on Intelligent Transportation Systems Pub Date : 2025-01-30 DOI: 10.1109/TITS.2025.3532455

Xiaojie Lin;Baihe Ma;Xu Wang;Guangsheng Yu;Ying He;Wei Ni;Ren Ping Liu

{"title":"CAN-Trace Attack: Exploit CAN Messages to Uncover Driving Trajectories","authors":"Xiaojie Lin;Baihe Ma;Xu Wang;Guangsheng Yu;Ying He;Wei Ni;Ren Ping Liu","doi":"10.1109/TITS.2025.3532455","DOIUrl":"https://doi.org/10.1109/TITS.2025.3532455","url":null,"abstract":"Driving trajectory data remains vulnerable to privacy breaches despite existing mitigation measures. Traditional methods for detecting driving trajectories typically rely on map-matching the path using Global Positioning System (GPS) data, which is susceptible to GPS data outage. This paper introduces CAN-Trace, a novel privacy attack mechanism that leverages Controller Area Network (CAN) messages to uncover driving trajectories, posing a significant risk to drivers’ long-term privacy. A new trajectory reconstruction algorithm is proposed to transform the CAN messages, specifically vehicle speed and accelerator pedal position, into weighted graphs accommodating various driving statuses. CAN-Trace identifies driving trajectories using graph-matching algorithms applied to the created graphs in comparison to road networks. We also design a new metric to evaluate matched candidates, which allows for potential data gaps and matching inaccuracies. Empirical validation under various real-world conditions, encompassing different vehicles and driving regions, demonstrates the efficacy of CAN-Trace: it achieves an attack success rate of up to 90.59% in the urban region, and 99.41% in the suburban region.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"26 3","pages":"3223-3236"},"PeriodicalIF":7.9,"publicationDate":"2025-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143535551","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

An Automated Airborne Support Tool for Aircraft Emergencies: Selection of Landing Sites and 4D Diversion Trajectories

IF 7.9 1区工程技术

IEEE Transactions on Intelligent Transportation Systems Pub Date : 2025-01-30 DOI: 10.1109/TITS.2025.3531952

Raúl Sáez;Andréas Guitart;Benoît Viry;Ioan Octavian Rad;Xavier Prats;Daniel Delahaye;Patrice Gonzalez

{"title":"An Automated Airborne Support Tool for Aircraft Emergencies: Selection of Landing Sites and 4D Diversion Trajectories","authors":"Raúl Sáez;Andréas Guitart;Benoît Viry;Ioan Octavian Rad;Xavier Prats;Daniel Delahaye;Patrice Gonzalez","doi":"10.1109/TITS.2025.3531952","DOIUrl":"https://doi.org/10.1109/TITS.2025.3531952","url":null,"abstract":"We present a software prototype (SafeNcy) capable of automatically choosing and ranking landing sites for emergency situations and of generating four-dimensional (4D) trajectories towards these sites. We describe the modules composing this framework, together with their main capabilities, interactions and the workflow of the full integrated system. Different types of emergencies are firstly categorized. Then, for each type of emergency, landing sites—including off-airport locations—are ranked, and speed and vertical trajectory descent profiles are tailored accordingly. These algorithms take into account several data from different sources, such as terrain databases, weather forecasts and aircraft performance models. We outline a new concept of operations aiming to integrate SafeNcy into the current aircraft operations and air traffic management paradigms. Several scenarios, focusing on total engine flame-out situations, are described and used to validate the framework, as well as to show its main features. The scenarios were designed in cooperation with a group of expert pilots and engineers. SafeNcy is expected to be an additional function for advanced and extended flight management systems, alleviating flight crew’s workload and contributing to a more digital cockpit. It could also be a technical enabler for future unmanned or highly-automated aviation.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"26 4","pages":"5030-5048"},"PeriodicalIF":7.9,"publicationDate":"2025-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10858596","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143740244","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

The Road to Safe Automated Driving Systems: A Review of Methods Providing Safety Evidence

IF 7.9 1区工程技术

IEEE Transactions on Intelligent Transportation Systems Pub Date : 2025-01-30 DOI: 10.1109/TITS.2025.3532684

Magnus Gyllenhammar;Gabriel Rodrigues de Campos;Martin Törngren

{"title":"The Road to Safe Automated Driving Systems: A Review of Methods Providing Safety Evidence","authors":"Magnus Gyllenhammar;Gabriel Rodrigues de Campos;Martin Törngren","doi":"10.1109/TITS.2025.3532684","DOIUrl":"https://doi.org/10.1109/TITS.2025.3532684","url":null,"abstract":"In recent years, enormous investments in Automated Driving Systems (ADSs) have distinctly advanced ADS technologies. Despite promises made by several high profile auto-makers, it has however become clear that the challenges involved for deploying ADS have been drastically underestimated. Contrary to previous generations of automotive systems, common design, development, verification and validation methods for safety critical systems do not suffice to cope with the increased complexity and operational uncertainties of an ADS. Therefore, the aim of this paper is to provide an understanding of existing methods for providing safety evidence and, most importantly, identifying the associated challenges and gaps pertaining to the use of each method. To this end, we have performed a literature review, articulated around four categories of methods: design techniques, verification and validation methods, run-time risk assessment, and run-time (self-)adaptation. We have identified and present eight challenges, collectively distinguishing ADSs from safety critical systems in general, and discuss the reviewed methods in the light of these eight challenges. For all reviewed methods, the uncertainties of the operational environment and the allocation of responsibility for the driving task on the ADS stand-out as the most difficult challenges to address. Finally, a set of research gaps is identified, and grouped into five major themes: 1) completeness of provided safety evidence, 2) improvements and analysis needs, 3) safe collection of closed loop data and accounting for tactical responsibility on the part of the ADS, 4) integration of AI/ML-based components, and 5) scalability of the approaches with respect to the complexity of the ADS.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"26 4","pages":"4315-4345"},"PeriodicalIF":7.9,"publicationDate":"2025-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10858599","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143735296","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Strategic XFC Charging Station Placement in Equilibrium Traffic Networks

IF 7.9 1区工程技术

IEEE Transactions on Intelligent Transportation Systems Pub Date : 2025-01-30 DOI: 10.1109/TITS.2025.3529342

Cheng Fang;Xi Chen;Xiang Li;Yi Fang;Sihang Li;Shiqi Shao;Michele Samorani;Haibing Lu

{"title":"Strategic XFC Charging Station Placement in Equilibrium Traffic Networks","authors":"Cheng Fang;Xi Chen;Xiang Li;Yi Fang;Sihang Li;Shiqi Shao;Michele Samorani;Haibing Lu","doi":"10.1109/TITS.2025.3529342","DOIUrl":"https://doi.org/10.1109/TITS.2025.3529342","url":null,"abstract":"Electric vehicles have become a trend as a replacement to gasoline-powered vehicles, and been promoted by worldwide policy makers as a solution to combat environmental problems and stimulate economy, whereas the lack of extreme fast charging infrastructure has become one main obstacle to broad adoption of electric vehicles. To promote the commercial success of electric vehicles, effective placement of electric vehicle (EV) charging stations is pivotal. While numerous studies address EV charging station placement, the integration of transportation network traffic, specifically equilibrium traffic assignment, where flows stabilize as drivers seek routes to minimize travel time, has been relatively limited. This research investigates equilibrium traffic assignment with the inclusion of extreme fast charging (XFC) stations and introduces an algorithmic solution. We assess diverse charging station placement strategies, including node-based and network-based approaches, weighing their respective advantages and drawbacks. Extensive experiments on real transportation networks of varying scales validate our algorithm and evaluate different charging station placement strategies. Many interesting findings are drawn from the study. For instance, increasing the number of XFC charging stations may not always result in reduced traffic time; the added value of extra stations beyond a certain threshold can be quite limited. The findings offer valuable insights for strategically deploying EV charging infrastructure, thus promoting electric vehicle adoption.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"26 4","pages":"4865-4877"},"PeriodicalIF":7.9,"publicationDate":"2025-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143726400","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Applying Quantum Computing to Solve Multicommodity Network Flow Problem

IF 7.9 1区工程技术

IEEE Transactions on Intelligent Transportation Systems Pub Date : 2025-01-30 DOI: 10.1109/TITS.2025.3532640

Chence Niu;Purvi Rastogi;Jaikishan Soman;Kausik Tamuli;Vinayak Dixit

引用次数: 0

Curricular Subgoals for Inverse Reinforcement Learning

IF 7.9 1区工程技术

IEEE Transactions on Intelligent Transportation Systems Pub Date : 2025-01-30 DOI: 10.1109/TITS.2025.3532519

Shunyu Liu;Yunpeng Qing;Shuqi Xu;Hongyan Wu;Jiangtao Zhang;Jingyuan Cong;Tianhao Chen;Yun-Fu Liu;Mingli Song

{"title":"Curricular Subgoals for Inverse Reinforcement Learning","authors":"Shunyu Liu;Yunpeng Qing;Shuqi Xu;Hongyan Wu;Jiangtao Zhang;Jingyuan Cong;Tianhao Chen;Yun-Fu Liu;Mingli Song","doi":"10.1109/TITS.2025.3532519","DOIUrl":"https://doi.org/10.1109/TITS.2025.3532519","url":null,"abstract":"Inverse Reinforcement Learning (IRL) aims to reconstruct the reward function from expert demonstrations to facilitate policy learning, and has demonstrated its remarkable success in imitation learning. To promote expert-like behavior, existing IRL methods mainly focus on learning global reward functions to minimize the trajectory difference between the imitator and the expert. However, these global designs are still limited by the redundant noise and error propagation problems, leading to the unsuitable reward assignment and thus downgrading the agent capability in complex multi-stage tasks. In this paper, we propose a novel Curricular Subgoal-based Inverse Reinforcement Learning (CSIRL) framework, that explicitly disentangles one task with several local subgoals to guide agent imitation. Specifically, CSIRL firstly introduces decision uncertainty of the trained agent over expert trajectories to dynamically select specific states as subgoals, which directly determines the exploration boundary of different task stages. To further acquire local reward functions for each stage, we customize a meta-imitation objective based on these curricular subgoals to train an intrinsic reward generator. Experiments on the D4RL and autonomous driving benchmarks demonstrate that the proposed methods yields results superior to the state-of-the-art counterparts, as well as better interpretability. Our code is publicly available at <uri>https://github.com/Plankson/CSIRL</uri>.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"26 3","pages":"3016-3027"},"PeriodicalIF":7.9,"publicationDate":"2025-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143535448","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

An Empirical Study of Ground Segmentation for 3-D Object Detection

IF 7.9 1区工程技术

IEEE Transactions on Intelligent Transportation Systems Pub Date : 2025-01-30 DOI: 10.1109/TITS.2025.3532436

Hongcheng Yang;Dingkang Liang;Zhe Liu;Jingyu Li;Zhikang Zou;Xiaoqing Ye;Xiang Bai

{"title":"An Empirical Study of Ground Segmentation for 3-D Object Detection","authors":"Hongcheng Yang;Dingkang Liang;Zhe Liu;Jingyu Li;Zhikang Zou;Xiaoqing Ye;Xiang Bai","doi":"10.1109/TITS.2025.3532436","DOIUrl":"https://doi.org/10.1109/TITS.2025.3532436","url":null,"abstract":"The ratio of foreground and background points directly impacts the accuracy and speed of the lidar-based 3D object detection methods. However, existing methods generally ignore the impact of ground points. Although some traditional ground segmentation algorithms are available to remove ground point clouds, they usually suffer from over-segmentation, which leads to a sub-optimal and even worse performance for the downstream 3D detection task. We conduct an in-depth analysis and attribute this phenomenon to the reason that some crucial foreground points attached to the ground (e.g., the wheels of Cars, or the feet of Pedestrians) are directly removed due to over-segmentation. To this end, we propose a new Attached Point Restoring (APR) module to recover these discarded foreground points. Experimental results demonstrate the effectiveness and generalization of APR by integrating it into various ground segmentation algorithms to boost the performance or the running time of 3D detection on KITTI and Waymo datasets. Finally, we hope this paper can serve as a new guide to inspire future research in this field. Code is available at <uri>https://github.com/yhc2021/GPR</uri>.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"26 3","pages":"3071-3083"},"PeriodicalIF":7.9,"publicationDate":"2025-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143535576","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Multimodal Transport Scheme Optimization and Capacity Allocation Considering Customer Classification

IF 7.9 1区工程技术

IEEE Transactions on Intelligent Transportation Systems Pub Date : 2025-01-29 DOI: 10.1109/TITS.2025.3528406

Bing Han;Yingxia Chi;Yuan Xu;Yongshin Park

{"title":"Multimodal Transport Scheme Optimization and Capacity Allocation Considering Customer Classification","authors":"Bing Han;Yingxia Chi;Yuan Xu;Yongshin Park","doi":"10.1109/TITS.2025.3528406","DOIUrl":"https://doi.org/10.1109/TITS.2025.3528406","url":null,"abstract":"Although multimodal transport has been widely developed, the multimodal transport operation system is still not as mature as other single modes of transport. Currently, the optimization of multimodal transport solutions and the allocation of capacity are planned as two separate parts, and the research on customer classification of multimodal transport is insufficient. The current bottleneck in multimodal transportation systems is optimizing multimodal customer classification and transportation solutions. Additionally, such an optimization can significantly improve the system’s overall revenue. Based on the customer classification theory of revenue management, we classified and managed multimodal transport customers and allocated and priced the transportation capacity according to the demand characteristics and price sensitivities of different customers. Furthermore, we considered the impact of transportation scheme planning on the profit of multimodal transport. Accordingly, we developed a joint optimization model for multimodal transport schemes, capacity allocation, and pricing by considering the customer classification. We solved the model using a hybrid particle swarm algorithm and validated it using arithmetic examples. The results indicate that a customer classification strategy can significantly improve the profit of multimodal transport operators when customer demand is unstable, and the total profit of the transportation system is increased by 7%. Moreover, these results suggest combining transportation solution optimization with customer classification can lead to more profitable multimodal revenue management solutions. Thus, our results can guide multimodal operators for systematically dealing with the customer classification problem and optimizing transportation schemes while improving their profits. Moreover, this study offers a reference for decision-making in multimodal operation management.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"26 3","pages":"3161-3174"},"PeriodicalIF":7.9,"publicationDate":"2025-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143535580","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0