IEEE Transactions on Intelligent Transportation Systems最新文献_第2页

From Diesel to Electric: Exploring Fleet Increment Curves for Zero-Emission Bus Transition 从柴油到电动：探索零排放公交车转型的车队增量曲线

IF 8.4 1区工程技术

IEEE Transactions on Intelligent Transportation Systems Pub Date : 2026-03-01 Epub Date: 2026-01-26 DOI: 10.1109/TITS.2026.3654606

Yiyang Peng;Zhuowei Wang;Anthony Chen

{"title":"From Diesel to Electric: Exploring Fleet Increment Curves for Zero-Emission Bus Transition","authors":"Yiyang Peng;Zhuowei Wang;Anthony Chen","doi":"10.1109/TITS.2026.3654606","DOIUrl":"https://doi.org/10.1109/TITS.2026.3654606","url":null,"abstract":"Bus electrification is a key trend in the global evolution of public transportation systems. However, replacing diesel buses (DBs) with Battery electric buses (BEBs) is a long-term process, where the limited driving range and prolonged charging times might necessitate a larger BEB fleet to maintain trip services compared to the replaced DB fleet. To quantify this fleet expansion across variable replacement decisions, we introduce the fleet increment curve (FIC), a novel conceptual idea that guides BEB procurement decisions during the transition to the zero-emission bus (ZEB) system. First, the FIC is derived from solving a series of mixed-integer linear programming (MILP) (namely MILP-FIC model) by varying the replaced DB fleet as inputs, where each MILP is developed by means of linearization techniques, while formulating the mixed-fleet operation under limited charging accessibility. To solve the MILP-FIC, Lagrangian relaxation (LR) is applied to relax charging accessibility constraints, decomposing the problem into route-specific subproblems. Subsequently, representing FIC by-products as piecewise linear functions enables extended models developed for addressing long-term fleet replacement scheduling and charging resource allocation. A general fleet replacement scheduling is presented, which accommodates multiple BEB types (varying battery capacities and charging power) by deriving type-specific fleet procurement curves. We use real-world bus route data from Hong Kong to explore the FIC, revealing how route characteristics—such as trip frequency, trip duration, and energy consumption—interact with charging site characteristics (e.g., siting and sizing) to shape the FIC. The curves typically follow a non-decreasing trend, while an S-shaped trend occurs across certain routes. Additionally, the results demonstrate the effective incorporation of FIC into the planning for ZEB transition, providing valuable insights for bus operators.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"27 5","pages":"6110-6120"},"PeriodicalIF":8.4,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147828377","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 Predictive UAV Framework for Tracking Fast-Moving Vehicles in Dynamic Environments 一种用于动态环境下快速移动车辆跟踪的预测无人机框架

IF 8.4 1区工程技术

IEEE Transactions on Intelligent Transportation Systems Pub Date : 2026-03-01 Epub Date: 2025-12-10 DOI: 10.1109/TITS.2025.3639545

Ananya Hazarika;Mehdi Rahmati

{"title":"A Predictive UAV Framework for Tracking Fast-Moving Vehicles in Dynamic Environments","authors":"Ananya Hazarika;Mehdi Rahmati","doi":"10.1109/TITS.2025.3639545","DOIUrl":"https://doi.org/10.1109/TITS.2025.3639545","url":null,"abstract":"In the near future, Uncrewed Aerial Vehicles (UAVs) are expected to evolve from simple data collectors to intelligent data gatekeepers capable of processing and analyzing information directly on board. In this paper, a decentralized UAV framework is proposed to address potential challenges in communication and coordination among fast-moving vehicles due to difficulties in maintaining seamless information exchange and synchronized operation across the network. While Integrated Sensing and Communications (ISAC) provides significant advantages, it still faces considerable challenges when dealing with such scenarios. This paper proposes DynaMo, a decentralized UAV framework addressing the challenges of tracking fast-moving vehicles in dynamic environments. DynaMo prioritizes spatiotemporal relevance and adapts to diverse vehicle behaviors with a novel freshness metric. Integrated with a Partially Observable Markov Decision Process (POMDP), the framework enables UAVs to make informed, real-time decisions under uncertainty. Simulation results highlight the superior tracking precision and adaptability of our proposed approach, offering a robust solution for ISAC systems in high-mobility scenarios.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"27 3","pages":"3594-3604"},"PeriodicalIF":8.4,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147557851","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

Reinforcement Learning-Based Adaptive Mobile Charging Station Placements in Mobile-Fixed Charging Stations Collaboration Network 移动固定充电站协作网络中基于强化学习的自适应移动充电站布局

IF 8.4 1区工程技术

IEEE Transactions on Intelligent Transportation Systems Pub Date : 2026-03-01 Epub Date: 2025-09-29 DOI: 10.1109/TITS.2025.3607869

Peisong Li;Sihao Chen;Bing Li;Minzhen Wang;Changle Li;M. Shamim Hossain

{"title":"Reinforcement Learning-Based Adaptive Mobile Charging Station Placements in Mobile-Fixed Charging Stations Collaboration Network","authors":"Peisong Li;Sihao Chen;Bing Li;Minzhen Wang;Changle Li;M. Shamim Hossain","doi":"10.1109/TITS.2025.3607869","DOIUrl":"https://doi.org/10.1109/TITS.2025.3607869","url":null,"abstract":"Today, electric vehicles (EVs) have gained significant recognition in the global market as an innovative mode of transport. However, the development of EVs is based on the interaction with the energy Internet. The increasing number of EVs has presented significant challenges to the existing charging infrastructure. Traditional fixed-location charging stations are increasingly inadequate to meet fluctuating charging demand, leading to inefficiencies such as long waiting times and uneven distribution of charging load. To address the problem, this paper proposes a Mobile-Fixed Charging Stations Collaboration Network (MFCSCN), which integrates fixed charging stations with mobile charging stations to dynamically adapt to real-time charging demands. Firstly, LightGBM is utilized to predict the charging load at fixed charging stations considering historical data and real-time EV mobility patterns. Secondly, the reinforcement learning algorithm is employed to optimize the placement of mobile charging stations based on predicted demand. Third, within the MFCSCN framework, LPPO (LightGBM and Proximal Policy Optimization) is proposed, which combines predictive modeling and reinforcement learning to optimize the dynamic placement of mobile charging stations. Through extensive simulations, we demonstrate that the MFCSCN significantly improves the responsiveness and scalability of the EV charging infrastructure, offering a robust solution to the evolving needs of urban mobility.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"27 3","pages":"3773-3787"},"PeriodicalIF":8.4,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147558006","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 Dehazing Network and Self-Supervised Transfer Learning Method in Highway Surveillance Scenes 高速公路监控场景中的除雾网络及自监督迁移学习方法

IF 8.4 1区工程技术

IEEE Transactions on Intelligent Transportation Systems Pub Date : 2026-03-01 Epub Date: 2026-02-06 DOI: 10.1109/TITS.2026.3656964

Zhiyong Peng;Yuxiang Chen;Jiang Du;Yulong Qiao

{"title":"A Dehazing Network and Self-Supervised Transfer Learning Method in Highway Surveillance Scenes","authors":"Zhiyong Peng;Yuxiang Chen;Jiang Du;Yulong Qiao","doi":"10.1109/TITS.2026.3656964","DOIUrl":"https://doi.org/10.1109/TITS.2026.3656964","url":null,"abstract":"This paper focuses on the application of image dehaze algorithms in highway scenarios, proposing a novel dehaze algorithm and a self-supervised transfer learning method for practical highway surveillance applications. The new lightweight dehazing network with the pyramid network structure is designed by combining the information multi-distillation network (IMDN), the channel and pixel attention module. In the deployed highway monitoring application, the self-supervised transfer learning method by proposed by integrating the pre-trained dehazing model with a dynamic target detection network. Through multiple alternating learning processes, the dehazing model continuously transfer and suitable for the current real-world application scenarios. The proposed algorithm is rigorously tested on an RTX 3090 GPU by using several public standard datasets and real-world highway datasets. The results demonstrate that the new algorithm outperforms state-of-the-art algorithms, achieving significantly higher Peak Signal-to-Noise Ratio (PSNR) and structural similarity (SSIM) on the public datasets. Furthermore, the visual quality of the dehazed images from new algorithm after transfer learning is markedly superior compared to other algorithms in the real-world highway scenarios. In terms of speed, the new algorithm exhibits faster inference speed than other comparative algorithms, achieving a frame rate 25 frames per second (FPS) for the <inline-formula> <tex-math>$1920times 1080$ </tex-math></inline-formula> real video. On the 4KID dataset, the inference speed can reach 26ms.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"27 5","pages":"6016-6026"},"PeriodicalIF":8.4,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147828333","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

Social-WITRAN: Multi-Modal Trajectory Prediction With Social-Aware Information Transmission 基于社会意识信息传递的多模态轨迹预测

IF 8.4 1区工程技术

IEEE Transactions on Intelligent Transportation Systems Pub Date : 2026-03-01 Epub Date: 2025-12-10 DOI: 10.1109/TITS.2025.3640659

Chu Wang;Di Liu;Simone Baldi;Jia Hu;Lan Feng;Alexandre Alahi

{"title":"Social-WITRAN: Multi-Modal Trajectory Prediction With Social-Aware Information Transmission","authors":"Chu Wang;Di Liu;Simone Baldi;Jia Hu;Lan Feng;Alexandre Alahi","doi":"10.1109/TITS.2025.3640659","DOIUrl":"https://doi.org/10.1109/TITS.2025.3640659","url":null,"abstract":"Multi-modal vehicle trajectory prediction is crucial for autonomous driving in dynamic environments. Despite the significant progress in the field, the uncertainty and heterogeneity caused by the diversity in driving intentions and driving scenes still present major challenges to multi-modal prediction. Existing query-based prediction paradigms take into account the social context arising from the driving scene, but neglect priors regarding vehicle intentions that the historical trajectory may contain. We propose a Social-aware Water-wave Information Transmission Recurrent Acceleration Network, abbreviated as S-WITRAN, based on decoupling multi-modal prediction into an ego-aware and a social-aware learning stage. The ego-aware stage aims to relax the constraints from the driving scene to explore a diversity of future trajectory candidates. The social-aware stage aligns the candidates with respect to the social context arising from the driving scene. The information transmission is designed to extract from the vehicle’s historical trajectory priors about its possible intentions and dynamic states, which are integrated to form a multi-modal set of trajectories. Extensive experiments on the NGSIM, highD, and Argoverse2 datasets, as well as a benchmark evaluation in UniTraj, demonstrate the superior performance of the proposed model in both map-free and map-based datasets.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"27 3","pages":"3640-3655"},"PeriodicalIF":8.4,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147557964","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

Harmonizing Time-of-Use Pricing and Rigid Driver Schedule in Battery Swapping Service 电池交换服务中使用时间定价与刚性驾驶员计划的协调

IF 8.4 1区工程技术

IEEE Transactions on Intelligent Transportation Systems Pub Date : 2026-03-01 Epub Date: 2025-12-15 DOI: 10.1109/TITS.2025.3639263

Dongshang Deng;Chaocan Xiang;Chengyi Gu;Bincan Yu;Hao Chen;Xuangou Wu;Ruipeng Gao

{"title":"Harmonizing Time-of-Use Pricing and Rigid Driver Schedule in Battery Swapping Service","authors":"Dongshang Deng;Chaocan Xiang;Chengyi Gu;Bincan Yu;Hao Chen;Xuangou Wu;Ruipeng Gao","doi":"10.1109/TITS.2025.3639263","DOIUrl":"https://doi.org/10.1109/TITS.2025.3639263","url":null,"abstract":"Electric scooters (ESs) serve as smart city terminals by uploading real-time charging data to improve mobility and energy efficiency. Battery swapping service has emerged as a transformative solution for energy replenishmen of ESs. Time-of-use (TOU) pricing is commonly adopted to encourage off-peak electricity usage by offering different rates. However, the rigid work schedules of ES drivers always hinder the optimization of battery swapping, leading to escalated charging costs. Existing literature fails to account for the practical constraints of battery charging in real-world scenarios. To tackle this <italic>paradox, we studied battery swap stations in Chengdu. Our findings reveal that the cost of swapping stations could be remarkably reduced by optimizing <italic>intrinsic, energy-greedy charging power strategies rather than by altering the <italic>extrinsic, rigid swapping schedules. Further analysis indicates 63% of charged batteries remain idle, presenting an opportunity to optimize charging power for cost savings without disrupting swapping schedules. Motivated by these insights, we propose PowerRL, a <italic>new adaptive charging power strategy empowered by spatio-temporal reinforcement learning. Specifically, we employ a spatio-temporal gated recurrent unit to predict the demand for battery swapping, which is then used to optimize charging power through reinforcement learning. Applied to the large-scale dataset, PowerRL could reduce per-unit electricity costs by 25.7% while still accommodating driver swapping schedules, thereby harmonizing TOU pricing with the rigidity of ES swapping service.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"27 3","pages":"3605-3618"},"PeriodicalIF":8.4,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147558012","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

Exact Predictor-Feedback CACC of Heterogeneous Vehicular Platoons With Distinct Actuation Delays 具有不同驱动延迟的异构车辆队列的精确预测反馈CACC

IF 8.4 1区工程技术

IEEE Transactions on Intelligent Transportation Systems Pub Date : 2026-03-01 Epub Date: 2025-12-09 DOI: 10.1109/TITS.2025.3639208

Amirhossein Samii;Nikolaos Bekiaris-Liberis

{"title":"Exact Predictor-Feedback CACC of Heterogeneous Vehicular Platoons With Distinct Actuation Delays","authors":"Amirhossein Samii;Nikolaos Bekiaris-Liberis","doi":"10.1109/TITS.2025.3639208","DOIUrl":"https://doi.org/10.1109/TITS.2025.3639208","url":null,"abstract":"We develop a predictor-feedback cooperative adaptive cruise control (CACC) design for platoons with heterogeneous vehicles, each featuring a different input delay. The control laws introduced achieve complete/exact compensation of input delays by constructing predictor states over a prediction horizon equal to the corresponding input delay for each vehicle. Such construction of the predictor states is enabled through V2V (vehicle-to-vehicle) or V2X (vehicle-to-everything) communication, which allows the ego vehicle to receive the state (including the actuator state) and model/control information from a certain number of vehicles ahead of it. The design guarantees individual vehicle stability, <inline-formula> <tex-math>$mathcal {L}_{2}$ </tex-math></inline-formula> string stability, and speed/spacing regulation, which is established capitalizing on the exact input delay compensation property of the design. We illustrate the design in simulation, including a comparison with an alternative predictor-based CACC scheme, which cannot guarantee string stability for large differences in the delay values among different vehicles. We also present consistent simulation results for even more practically realistic scenarios, in which we employ real traffic data for the trajectory of the leading vehicle, obtained from the NGSIM dataset, as well as we account for communication delays and parameter uncertainties.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"27 3","pages":"3520-3529"},"PeriodicalIF":8.4,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147558058","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

Federated Deep Reinforcement Learning for Combating Cyber-Threats Specific to EV Charging in Next-Gen WPT Infrastructure 联合深度强化学习对抗下一代WPT基础设施中电动汽车充电的网络威胁

IF 8.4 1区工程技术

IEEE Transactions on Intelligent Transportation Systems Pub Date : 2026-03-01 Epub Date: 2025-03-22 DOI: 10.1109/TITS.2025.3569065

Miaojiang Chen;Kaiwen Luo;Pengshuo Wang;Wenjing Xiao;Zhiquan Liu;Anfeng Liu;Ahmed Farouk;Min Chen

{"title":"Federated Deep Reinforcement Learning for Combating Cyber-Threats Specific to EV Charging in Next-Gen WPT Infrastructure","authors":"Miaojiang Chen;Kaiwen Luo;Pengshuo Wang;Wenjing Xiao;Zhiquan Liu;Anfeng Liu;Ahmed Farouk;Min Chen","doi":"10.1109/TITS.2025.3569065","DOIUrl":"https://doi.org/10.1109/TITS.2025.3569065","url":null,"abstract":"With the popularity of electric vehicles (EVs), wireless power transmission (WPT) technology has become a hot research topic for next-generation battery charging technology. However, the vulnerability of wireless networks to malicious interference attacks is inherited by WPT. To alleviate the privacy and security issues of WPT, we propose a novel FedDQ, a <underline>federated <underline>deep reinforcement learning with <underline>Q-ensemble, to cope with interference attacks in EV wireless charging network environments. Federated learning protects the security privacy of EVs by training a global model that exploits the property that data and models will not be transmitted. In order to trade-off the training cost and efficiency, we introduce offline-to-online training models by pre-training the offline Q-network with pre-collected data, and the trained model serves as an initialization of the online model. Then, the online Q-network is obtained by weakening or removing the original pessimistic constraints to enhance the training speed. Secondly, we introduce the intelligent reflective surface (IRS) to enhance the security performance of WPT by modifying the IRS phase shift and amplitude to cancel the malicious interference signal. Experimental results show that our proposed FedDQ algorithm has superior performance and outperforms existing baseline methods in terms of anti-jamming metrics.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"27 3","pages":"3761-3772"},"PeriodicalIF":8.4,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147558071","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

Energy Minimization in UAV-Enabled Cargo Pickup Systems: A Radio Map-Aided Hierarchical Optimization Framework 无人机货物拾取系统的能量最小化：无线电地图辅助分层优化框架

IF 8.4 1区工程技术

IEEE Transactions on Intelligent Transportation Systems Pub Date : 2026-03-01 Epub Date: 2025-12-22 DOI: 10.1109/TITS.2025.3639674

Jiangling Cao;Shi Peng;Dingcheng Yang;Qinghua Wu;Tiankui Zhang

{"title":"Energy Minimization in UAV-Enabled Cargo Pickup Systems: A Radio Map-Aided Hierarchical Optimization Framework","authors":"Jiangling Cao;Shi Peng;Dingcheng Yang;Qinghua Wu;Tiankui Zhang","doi":"10.1109/TITS.2025.3639674","DOIUrl":"https://doi.org/10.1109/TITS.2025.3639674","url":null,"abstract":"This article studies the energy efficiency optimization of cargo uncrewed aerial vehicle (UAV) pickup systems, with constraints on on-board energy and load capacity. In the UAV-enabled cargo pickup system, minimizing the total energy consumption and ensuring the safe flight of the cargo UAV is a problem to be solved. However, due to building blockages, the channel between the UAV and ground base stations (GBSs) frequently switches between line-of-sight (LoS) and non-line-of-sight (NLoS), thereby affecting the UAV’s communication quality. This effect is further aggravated by environmental noise interference. Moreover, limited by the on-board energy, it is unrealistic for the UAV to pick up all the cargo in a single flight without charging or replacing the battery. To address the above-mentioned challenges, we propose a UAV pickup system energy efficiency optimization (UPSEEO) framework. In this framework, the UAV’s trajectory between any two pickup points is optimized via the A<inline-formula> <tex-math>${}^{*}$ </tex-math></inline-formula> algorithm to ensure the stability of the UAV communication link. Next, we employ the particle swarm optimization (PSO) algorithm to optimize both task allocation and flight speed to minimize the total energy consumption, subject to constraints on UAV on-board energy limits and payload capacity. Numerical results show that the proposed framework can ensure the UAV’s communication quality in any spatial topology, with an improvement in energy efficiency of approximately 5% to 50% compared to the comparison experiment.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"27 3","pages":"3669-3684"},"PeriodicalIF":8.4,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147558089","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

CrossRay3D: Geometry and Distribution Guidance for Efficient Multimodal 3D Detection crosssray3d：几何和分布指导有效的多模态3D检测

IF 8.4 1区工程技术

IEEE Transactions on Intelligent Transportation Systems Pub Date : 2026-03-01 Epub Date: 2026-01-16 DOI: 10.1109/TITS.2026.3651273

Huiming Yang;Wenzhuo Liu;Yicheng Qiao;Lei Yang;Xianzhu Zeng;Li Wang;Zhiwei Li;Zijian Zeng;Zhiying Jiang;Huaping Liu;Kunfeng Wang

{"title":"CrossRay3D: Geometry and Distribution Guidance for Efficient Multimodal 3D Detection","authors":"Huiming Yang;Wenzhuo Liu;Yicheng Qiao;Lei Yang;Xianzhu Zeng;Li Wang;Zhiwei Li;Zijian Zeng;Zhiying Jiang;Huaping Liu;Kunfeng Wang","doi":"10.1109/TITS.2026.3651273","DOIUrl":"https://doi.org/10.1109/TITS.2026.3651273","url":null,"abstract":"The sparse cross-modality detector offers more advantages than its counterpart, the Bird’s-Eye-View (BEV) detector, particularly in terms of adaptability for downstream tasks and computational cost savings. However, existing sparse detectors overlook the quality of token representation, leaving it with a sub-optimal foreground quality and limited performance. In this paper, we identify that the geometric structure preserved and the class distribution are the key to improving the performance of the sparse detector, and propose a Sparse Selector (SS). The core module of SS is Ray-Aware Supervision (RAS), which preserves rich geometric information during the training stage, and Class-Balanced Supervision, which adaptively reweights the salience of class semantics, ensuring that tokens associated with small objects are retained during token sampling. Thereby, outperforming other sparse multi-modal detectors in the representation of tokens. Additionally, we design Ray Positional Encoding (Ray PE) to address the distribution differences between the LiDAR modality and the image. Finally, we integrate the aforementioned module into an end-to-end sparse multi-modality detector, dubbed CrossRay3D. Experiments show that, on the challenging nuScenes benchmark, CrossRay3D achieves state-of-the-art performance with 72.4% mAP and 74.7% NDS, while running <inline-formula> <tex-math>$1.84times $ </tex-math></inline-formula> faster than other leading methods. Moreover, CrossRay3D demonstrates strong robustness even in scenarios where LiDAR or camera data are partially or entirely missing. The code is available on <uri>https://github.com/xuehaipiaoxiang/CrossRay3D</uri>","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"27 5","pages":"6027-6039"},"PeriodicalIF":8.4,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147828335","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