IEEE Transactions on Intelligent Transportation Systems最新文献_第6页

Performance-Prescribed Optimal Control for Target Enclosing of Vehicles via Control Barrier Function-Based Reinforcement Learning

IF 7.9 1区工程技术

IEEE Transactions on Intelligent Transportation Systems Pub Date : 2025-02-20 DOI: 10.1109/TITS.2025.3540652

Fei Zhang;Guang-Hong Yang;Georgi Marko Dimirovski

{"title":"Performance-Prescribed Optimal Control for Target Enclosing of Vehicles via Control Barrier Function-Based Reinforcement Learning","authors":"Fei Zhang;Guang-Hong Yang;Georgi Marko Dimirovski","doi":"10.1109/TITS.2025.3540652","DOIUrl":"https://doi.org/10.1109/TITS.2025.3540652","url":null,"abstract":"The target enclosing control problem for autonomous vehicles with uncertainties necessitates simultaneous consideration of control optimality, robustness, and safety-guided performance constraints. This paper presents a performance-prescribed optimal control algorithm using control barrier function (CBF)-based reinforcement learning (RL) to address the above problem, which contains two key contributions. First, a special CBF-based argument term is developed and embedded into the reward function to characterize environmental feedback regarding the risk of violating constraints, which enables the controller to confine enclosing errors within declared boundaries with minimal intervention. Second, a critic-only neural network is utilized to synthesize the optimal control policy, where a novel fixed-time updating law is presented to accelerate the weight convergence to ideal values within a fixed settling time, thereby enhancing the online learning ability and further improving control performance. Theoretical outcomes related to learning convergence, safety, stability, and robustness are rigorously verified. Simulations reveal that the proposed strategy outperforms the previously designed enclosing controllers based on the non-RL and RL ways in terms of complying with prescribed safety constraints and optimizing long-term performance.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"26 4","pages":"5552-5567"},"PeriodicalIF":7.9,"publicationDate":"2025-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143725117","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

A Real-Time Degeneracy Sensing and Compensation Method for Enhanced LiDAR SLAM

IF 7.9 1区工程技术

IEEE Transactions on Intelligent Transportation Systems Pub Date : 2025-02-19 DOI: 10.1109/TITS.2024.3524394

Zongbo Liao;Xuanxuan Zhang;Tianxiang Zhang;Zhi Li;Zhenqi Zheng;Zhichao Wen;You Li

{"title":"A Real-Time Degeneracy Sensing and Compensation Method for Enhanced LiDAR SLAM","authors":"Zongbo Liao;Xuanxuan Zhang;Tianxiang Zhang;Zhi Li;Zhenqi Zheng;Zhichao Wen;You Li","doi":"10.1109/TITS.2024.3524394","DOIUrl":"https://doi.org/10.1109/TITS.2024.3524394","url":null,"abstract":"LiDAR is widely used in Simultaneous Localization and Mapping (SLAM) and autonomous driving. The LiDAR odometry is of great importance in multi-sensor fusion. However, in some unstructured environments, the point cloud registration cannot constrain the poses of the LiDAR due to its sparse geometric features, which leads to the degeneracy of multi-sensor fusion accuracy. To address this problem, we propose a novel real-time approach to sense and compensate for the degeneracy of LiDAR. Firstly, this paper introduces the degeneracy factor with clear meaning, which can measure the degeneracy of LiDAR. Then, the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) clustering method adaptively perceives the degeneracy with better environmental generalization. Finally, the degeneracy perception results are utilized to fuse LiDAR and IMU, thus effectively resisting degeneracy effects. Experiments on our dataset show the method’s high accuracy and robustness and validate our algorithm’s adaptability to different environments and LiDAR scanning modalities.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"26 3","pages":"4202-4213"},"PeriodicalIF":7.9,"publicationDate":"2025-02-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143535507","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

Array Self-Position Determination Based on Orthogonal Grid Matching Under Multipath Environments

IF 7.9 1区工程技术

IEEE Transactions on Intelligent Transportation Systems Pub Date : 2025-02-18 DOI: 10.1109/TITS.2025.3539634

Zhongkang Cao;Jianfeng Li;Rui Xu;Pan Li;Xiaofei Zhang;Qihui Wu

{"title":"Array Self-Position Determination Based on Orthogonal Grid Matching Under Multipath Environments","authors":"Zhongkang Cao;Jianfeng Li;Rui Xu;Pan Li;Xiaofei Zhang;Qihui Wu","doi":"10.1109/TITS.2025.3539634","DOIUrl":"https://doi.org/10.1109/TITS.2025.3539634","url":null,"abstract":"Array self-position determination methods based on multiple emitter data can avoid significant deviations of vehicle satellite navigation in harsh environments. However, existing array self-position determination methods show decrease in performance under multipath environments. To deal with this problem, we propose an array self-position determination method based on orthogonal grid matching with the spatial differencing method. Specifically, the direction of arrival (DOA) of direct path and multipath signals are respectively estimated by array spatial differencing method. The matching accuracy is enhanced by utilizing the prior information of direct path signal. After calculating correlation coefficients of different sources, estimated angles with high correlation are then classified into the same set. Then, the noise subspace of each angle set is reconstructed and the position is estimated by grid matching with the orthogonal property between the noise subspaces and the characteristic steering vectors. The matching results of redundant angle sets are removed as non-matching items, thus averting positioning deviations. The simulation results demonstrate that the computational complexity of the proposed method is comparable to that of the signal subspace fitting (SSF). Moreover, in terms of positioning precision, the proposed method outperforms multiple signal classification with enhanced spatial smoothing (ESSMUSIC), initial signal fitting (ISF), and SSF.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"26 4","pages":"5156-5166"},"PeriodicalIF":7.9,"publicationDate":"2025-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143740245","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

Vulnerability Mitigation of Urban Traffic Control Against Cyberattacks Using Secure Multi-Party Computation

IF 7.9 1区工程技术

IEEE Transactions on Intelligent Transportation Systems Pub Date : 2025-02-18 DOI: 10.1109/TITS.2025.3538095

Saeed Adelipour;Enayatollah Amiri Darreh Razgahi;Mohammad Haeri

{"title":"Vulnerability Mitigation of Urban Traffic Control Against Cyberattacks Using Secure Multi-Party Computation","authors":"Saeed Adelipour;Enayatollah Amiri Darreh Razgahi;Mohammad Haeri","doi":"10.1109/TITS.2025.3538095","DOIUrl":"https://doi.org/10.1109/TITS.2025.3538095","url":null,"abstract":"Traffic signal control systems are essential for safe and efficient urban traffic management. However, these systems increasingly rely on collecting and processing extensive data, posing confidentiality challenges that must be addressed to protect sensitive information and safeguard against cyber-threats. This article proposes a privacy-preserving traffic signal control strategy based on secure multi-party computation. The design uses multiple computing servers and a secret sharing scheme to encrypt traffic data into several shares, each corresponding to a server. The servers then securely compute the green time plans, working directly on the encrypted data. The proposed method protects traffic data from curious computing servers and external eavesdroppers, preventing the adversaries from gaining the required information to launch a complex attack. Additionally, a detection and recovery procedure from false data injection attacks on green time signals is presented using secret reconstruction from different server combinations. Simulation results for various scenarios demonstrate the proposed method’s effectiveness in maintaining data confidentiality while achieving the same performance as an unprotected control strategy, with a reasonable addition to computation time. It also depicts a significant reduction in vulnerability against falsified green time signal attacks in terms of total travel time in the network.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"26 4","pages":"4568-4578"},"PeriodicalIF":7.9,"publicationDate":"2025-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143726429","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

A Surrogate Approach for Real-Time Stress Assessment of Urban Drivers Using Driving Video Data

IF 7.9 1区工程技术

IEEE Transactions on Intelligent Transportation Systems Pub Date : 2025-02-18 DOI: 10.1109/TITS.2025.3538647

Siwei Wan;Jie He;Xiaoyu Wu;Yuntao Ye;Pengcheng Qin;Zhiming Fang

{"title":"A Surrogate Approach for Real-Time Stress Assessment of Urban Drivers Using Driving Video Data","authors":"Siwei Wan;Jie He;Xiaoyu Wu;Yuntao Ye;Pengcheng Qin;Zhiming Fang","doi":"10.1109/TITS.2025.3538647","DOIUrl":"https://doi.org/10.1109/TITS.2025.3538647","url":null,"abstract":"Assessing driver stress can enhance Autonomous Vehicles’ (AVs) ability to recognize potential hazards in human-machine cooperative driving. In this study, we use a scene graph technique from the driver’s perspective as an intermediate representation for stress assessment, effectively modeling the complexity of real driving scenarios. We develop a dynamic scene graph readout model based on Global Context-Aware Attention (GCAA) to capture the spatiotemporal variability of the environment. Our approach integrates a Multi-Relational Graph Convolution Network (MR-GCN) and a Long-Short Term Memory Network (LSTM) to evaluate driver stress using field data from Nanjing, China. Results show that our model significantly improves driver stress classification, particularly for critical stress levels, with a 65.3% increase in balanced accuracy and a 38% rise in F-score compared to the baseline. The addition of GCAA enhances accuracy further, with an average improvement of 51.7%. This demonstrates our model’s superior ability to understand the contextual relationships within driving scenarios, making it highly effective for AVs to assess driver status in complex situations and interact more efficiently with human drivers. Furthermore, our model achieves a balance between accuracy and inference speed, making it suitable for real-time assessment tasks.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"26 4","pages":"4705-4716"},"PeriodicalIF":7.9,"publicationDate":"2025-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143726458","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

High-Resolution LiDAR Depth Completion Algorithm Guided by Image Topography Maps

IF 7.9 1区工程技术

IEEE Transactions on Intelligent Transportation Systems Pub Date : 2025-02-18 DOI: 10.1109/TITS.2025.3528017

Genyuan Xing;Jun Lin;Kunyang Wu;Yang Liu;Guanyu Zhang

引用次数: 0

Cooperative Merging in Mixed Traffic: A Mobile-Edge Hybrid Control Framework

IF 7.9 1区工程技术

IEEE Transactions on Intelligent Transportation Systems Pub Date : 2025-02-17 DOI: 10.1109/TITS.2025.3540136

Zhanbo Sun;Ziyan Gao;Xiangyu He;Zheyi Li;Tianyu Huang

{"title":"Cooperative Merging in Mixed Traffic: A Mobile-Edge Hybrid Control Framework","authors":"Zhanbo Sun;Ziyan Gao;Xiangyu He;Zheyi Li;Tianyu Huang","doi":"10.1109/TITS.2025.3540136","DOIUrl":"https://doi.org/10.1109/TITS.2025.3540136","url":null,"abstract":"This paper addresses decision-making challenges in mixed traffic environments comprising both conventional human-operated vehicles (HVs) and connected automated vehicles (CAVs). Our proposed framework is exemplified using a ramp merging scenario and is structured as an optimization problem, in which a merge sequencing problem and a trajectory planning problem are embedded and solved by a bi-level hybrid centralized-decentralized model predictive control (HMPC) approach. The HMPC framework we introduce leverages centralized edge computing for efficient merge decision optimization through a dynamic-programming approach and decentralized mobile computing for distributed trajectory planning through three different optimization algorithms. Simulation results show that compared to open-loop control, the proposed framework can ensure system efficient ramp-merging control, and exhibits robustness in the presence of uncertainty caused by the stochastic driving behaviors of HVs. In addition, it is found that mobile-edge hybrid framework can reduce the computational time to the millisecond-level, potentially meeting real-time computational requirements.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"26 4","pages":"4837-4850"},"PeriodicalIF":7.9,"publicationDate":"2025-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143726348","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

Enhancing Perception for Autonomous Vehicles: A Multi-Scale Feature Modulation Network for Image Restoration

IF 7.9 1区工程技术

IEEE Transactions on Intelligent Transportation Systems Pub Date : 2025-02-17 DOI: 10.1109/TITS.2025.3538485

Yuning Cui;Jianyong Zhu;Alois Knoll

{"title":"Enhancing Perception for Autonomous Vehicles: A Multi-Scale Feature Modulation Network for Image Restoration","authors":"Yuning Cui;Jianyong Zhu;Alois Knoll","doi":"10.1109/TITS.2025.3538485","DOIUrl":"https://doi.org/10.1109/TITS.2025.3538485","url":null,"abstract":"Accurate environmental perception is essential for the effective operation of autonomous vehicles. However, visual images captured in dynamic environments or adverse weather conditions often suffer from various degradations. Image restoration focuses on reconstructing clear and sharp images by eliminating undesired degradations from corrupted inputs. These degradations typically vary in size and severity, making it crucial to employ robust multi-scale representation learning techniques. In this paper, we propose Multi-Scale Feature Modulation (MSFM), a novel deep convolutional architecture for image restoration. MSFM modulates multi-scale features in both frequency and spatial domains to make features sharper and closer to that of clean images. Specifically, our multi-scale frequency attention module transforms features into multiple scales and then modulates each scale in the implicit frequency domain using pooling and attention. Moreover, we develop a multi-scale spatial modulation module to refine pixels with the guidance of local features. The proposed frequency and spatial modules enable MSFM to better handle degradations of different sizes. Experimental results demonstrate that MSFM achieves state-of-the-art performance on 12 datasets for a range of image restoration tasks, i.e., image dehazing, image defocus/motion deblurring, and image desnowing. Furthermore, the restored images significantly improve the environmental perception of autonomous vehicles.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"26 4","pages":"4621-4632"},"PeriodicalIF":7.9,"publicationDate":"2025-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143726350","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

Data-Based Estimator Design for Sideslip Angles of Autonomous Ground Vehicles

IF 7.9 1区工程技术

IEEE Transactions on Intelligent Transportation Systems Pub Date : 2025-02-17 DOI: 10.1109/TITS.2025.3535828

Chenchao Wang;Deyuan Meng;Honggui Han;Kaiquan Cai

{"title":"Data-Based Estimator Design for Sideslip Angles of Autonomous Ground Vehicles","authors":"Chenchao Wang;Deyuan Meng;Honggui Han;Kaiquan Cai","doi":"10.1109/TITS.2025.3535828","DOIUrl":"https://doi.org/10.1109/TITS.2025.3535828","url":null,"abstract":"This paper deals with sideslip angle estimation problems of autonomous ground vehicles that repeatedly perform the specific tasks in the absence of model knowledge for their lateral dynamics. By designing appropriate estimators, the equivalence between estimator auxiliary input synthesis and output feedback stabilization along the iteration axis is established. Moreover, we propose an innovative data-based output feedback stabilization framework that leverages insufficient sampled data to formulate an output feedback controller without the need of identification. To be specific, with the application of some helpful linear matrix inequality (LMI) techniques, the data-based synthesis of required output feedback controller is transformed into solving the equivalent LMI conditions. By employing the proposed data-based estimation strategy and partial lateral dynamics information of ground vehicles, accurate estimation of sideslip angles over the entire estimation duration can be achieved even in the presence of disturbances. Experiments on an Ackermann steering intelligent vehicle are provided to demonstrate the effectiveness of the proposed estimation strategy.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"26 4","pages":"4795-4807"},"PeriodicalIF":7.9,"publicationDate":"2025-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143725109","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

Dynamic Option Policy Enabled Hierarchical Deep Reinforcement Learning Model for Autonomous Overtaking Maneuver 用于自主超车操纵的动态选项策略分层深度强化学习模型

IF 7.9 1区工程技术

IEEE Transactions on Intelligent Transportation Systems Pub Date : 2025-02-14 DOI: 10.1109/TITS.2025.3536020

Shikhar Singh Lodhi;Neetesh Kumar;Pradumn Kumar Pandey

{"title":"Dynamic Option Policy Enabled Hierarchical Deep Reinforcement Learning Model for Autonomous Overtaking Maneuver","authors":"Shikhar Singh Lodhi;Neetesh Kumar;Pradumn Kumar Pandey","doi":"10.1109/TITS.2025.3536020","DOIUrl":"https://doi.org/10.1109/TITS.2025.3536020","url":null,"abstract":"Driving an Autonomous Vehicle (AV) in dynamic traffic is a critical task, as the overtaking maneuver being considered one of the most complex due to involvement of several sub-maneuvers. Recent advances in Deep Reinforcement Learning (DRL) have resulted in AVs exhibiting exceptional performance in addressing overtaking-related challenges. However, the intricate nature of the overtaking presents difficulties for a RL agent to proficiently handle all its sub-maneuvers that include left lane change, right lane change and straight drive. Furthermore, the dynamic traffic restricts the RL agents to execute the sub-maneuvers at critical checkpoints involved in overtaking. To address this, we propose an approach inspired by semi-Markov options, called Dynamic Option Policy enabled Hierarchical Deep Reinforcement Learning (DOP-HDRL). This innovative approach allows the selection of sub-maneuver agents using a single dynamic option policy, while employing individual DRL agents specifically trained for each sub-maneuver to perform tasks during overtaking in dynamic environments. By breaking down overtaking maneuvers into several sub-maneuvers and controlling them using a single policy, the DOP-HDRL approach reduces training time and computational load compared to classical DRL agents. Moreover, DOP-HDRL easily integrates basic traffic safety rules into overtaking maneuvers to offer more robust solutions. The DOP-HDRL approach is rigorously evaluated through multiple overtaking and non-overtaking scenarios inspired by the National Highway Traffic Safety Administration (NHTSA) pre-crash scenarios in the CARLA simulator. On an average, the DOP-HDRL approach shows 100% completion rate, 14% least collision rate, 25% optimal clearance distance, and 7% more average speed compared to the state-of-the-art methods.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"26 4","pages":"5018-5029"},"PeriodicalIF":7.9,"publicationDate":"2025-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143740401","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