Pub Date : 2025-01-16DOI: 10.1109/TITS.2024.3521460
Qinghai Lin;Wei Huang;Zhigang Wu;Mengmeng Zhang;Zhaocheng He
Coordinated ramp metering (CRM) is one effective measure to alleviate urban expressway congestion. Traditional model-based methods generally concentrate on single-bottleneck scenarios, while ignoring the case of multiple bottlenecks. In addition, the fixed-sensor fails to fully capture the dynamic traffic characteristics. The rapid development of traffic detection technology has made available a large amount of automatic vehicle identification (AVI) data, which can record detailed individual trajectories. Taking advantage of the AVI data, CRM can be improved. Besides, multi-agent deep reinforcement learning (MADRL) and game theory have been proven to be effective for traffic signal control. These methods can address the challenges faced by CRM, such as solving nonlinear and high-dimensional optimization problems. This paper proposes a distributed CRM strategy with multi-bottleneck to minimize the total travel time and balance the multiple on-ramps equity, using the individual trajectory information from AVI data. Firstly, the paper defines road segment units, road segment groups, and bottlenecks. Next, the problem is formulated as a potential game that captures the interaction among multiple bottlenecks. The controllers utilize the MADDPG algorithm to determine the green duration of the on-ramps. Finally, the proposed strategy is tested on a real-world urban expressway in a microsimulation platform SUMO. Experimental results demonstrate that the proposed strategy performs better than the baseline methods in eliminating mainline congestion and improving the multiple on-ramps equity. Compared to the no-control scenario, the proposed strategy has improved the performance of the system throughput, average travel time, and average mainline speed by 1.31%, 44.36%, and 115.23%.
{"title":"Multi-Agent Game Theory-Based Coordinated Ramp Metering Method for Urban Expressways With Multi-Bottleneck","authors":"Qinghai Lin;Wei Huang;Zhigang Wu;Mengmeng Zhang;Zhaocheng He","doi":"10.1109/TITS.2024.3521460","DOIUrl":"https://doi.org/10.1109/TITS.2024.3521460","url":null,"abstract":"Coordinated ramp metering (CRM) is one effective measure to alleviate urban expressway congestion. Traditional model-based methods generally concentrate on single-bottleneck scenarios, while ignoring the case of multiple bottlenecks. In addition, the fixed-sensor fails to fully capture the dynamic traffic characteristics. The rapid development of traffic detection technology has made available a large amount of automatic vehicle identification (AVI) data, which can record detailed individual trajectories. Taking advantage of the AVI data, CRM can be improved. Besides, multi-agent deep reinforcement learning (MADRL) and game theory have been proven to be effective for traffic signal control. These methods can address the challenges faced by CRM, such as solving nonlinear and high-dimensional optimization problems. This paper proposes a distributed CRM strategy with multi-bottleneck to minimize the total travel time and balance the multiple on-ramps equity, using the individual trajectory information from AVI data. Firstly, the paper defines road segment units, road segment groups, and bottlenecks. Next, the problem is formulated as a potential game that captures the interaction among multiple bottlenecks. The controllers utilize the MADDPG algorithm to determine the green duration of the on-ramps. Finally, the proposed strategy is tested on a real-world urban expressway in a microsimulation platform SUMO. Experimental results demonstrate that the proposed strategy performs better than the baseline methods in eliminating mainline congestion and improving the multiple on-ramps equity. Compared to the no-control scenario, the proposed strategy has improved the performance of the system throughput, average travel time, and average mainline speed by 1.31%, 44.36%, and 115.23%.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"26 3","pages":"3643-3658"},"PeriodicalIF":7.9,"publicationDate":"2025-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143563905","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}
Pub Date : 2025-01-15DOI: 10.1109/TITS.2024.3525073
Guorong Zhang;Chee-Meng Chew;Yujie Xu;Mingyu Fu
This paper investigates discrete-time sliding mode trajectory tracking control for fully actuated autonomous surface vessels (ASVs) with unknown nonlinear dead-zone and saturation inputs, utilizing a switching dynamic event-triggered mechanism (DETM). Through model integration, a direct relationship between ASV position and control inputs is established, simplifying trajectory tracking strategy design. ASVs face dead-zone and saturation constraints in control inputs, where low input signals may not overcome static friction, hindering maneuverability, and further increases are ineffective once actuators reach maximum thrust. Unlike linear dead-zone and saturation input constraints with known parameters, this paper considers a more realistic scenario of unknown nonlinearity, employing adaptive neural networks to approximate and compensate for the resulting unknown dynamics. Moreover, limited internal communication resources constrain real-time inter-subsystem communication in ASVs, while frequent short-period sampling in stable conditions results in unnecessary energy and computational consumption, collectively degrading trajectory tracking performance. A novel switching DETM is proposed to reduce unnecessary data transmission, which switches triggering conditions based on variations in auxiliary dynamic variables. Meanwhile, the controller output variation is integrated into the event-triggered conditions to enhance tracking control performance. Based on this, a discrete-time sliding mode trajectory tracking controller suitable for large sampling periods is designed. This ensures satisfactory tracking control effectiveness while further reducing unnecessary data transmission frequency and conserving limited communication resources within a larger range of sampling periods. All tracking errors are proven to be controlled within a small vicinity near zero. The numerical simulation results validate the efficacy of the proposed control strategy.
{"title":"Switching Dynamic Event-Triggered Sliding Mode Based Trajectory Tracking Control for ASVs With Nonlinear Dead-Zone and Saturation Inputs","authors":"Guorong Zhang;Chee-Meng Chew;Yujie Xu;Mingyu Fu","doi":"10.1109/TITS.2024.3525073","DOIUrl":"https://doi.org/10.1109/TITS.2024.3525073","url":null,"abstract":"This paper investigates discrete-time sliding mode trajectory tracking control for fully actuated autonomous surface vessels (ASVs) with unknown nonlinear dead-zone and saturation inputs, utilizing a switching dynamic event-triggered mechanism (DETM). Through model integration, a direct relationship between ASV position and control inputs is established, simplifying trajectory tracking strategy design. ASVs face dead-zone and saturation constraints in control inputs, where low input signals may not overcome static friction, hindering maneuverability, and further increases are ineffective once actuators reach maximum thrust. Unlike linear dead-zone and saturation input constraints with known parameters, this paper considers a more realistic scenario of unknown nonlinearity, employing adaptive neural networks to approximate and compensate for the resulting unknown dynamics. Moreover, limited internal communication resources constrain real-time inter-subsystem communication in ASVs, while frequent short-period sampling in stable conditions results in unnecessary energy and computational consumption, collectively degrading trajectory tracking performance. A novel switching DETM is proposed to reduce unnecessary data transmission, which switches triggering conditions based on variations in auxiliary dynamic variables. Meanwhile, the controller output variation is integrated into the event-triggered conditions to enhance tracking control performance. Based on this, a discrete-time sliding mode trajectory tracking controller suitable for large sampling periods is designed. This ensures satisfactory tracking control effectiveness while further reducing unnecessary data transmission frequency and conserving limited communication resources within a larger range of sampling periods. All tracking errors are proven to be controlled within a small vicinity near zero. The numerical simulation results validate the efficacy of the proposed control strategy.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"26 3","pages":"4019-4031"},"PeriodicalIF":7.9,"publicationDate":"2025-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143535584","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}
Pub Date : 2025-01-14DOI: 10.1109/TITS.2024.3518293
{"title":"IEEE Intelligent Transportation Systems Society Information","authors":"","doi":"10.1109/TITS.2024.3518293","DOIUrl":"https://doi.org/10.1109/TITS.2024.3518293","url":null,"abstract":"","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"26 1","pages":"C3-C3"},"PeriodicalIF":7.9,"publicationDate":"2025-01-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10841913","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142976043","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}
Pub Date : 2025-01-14DOI: 10.1109/TITS.2024.3524882
Zhiming Hong
Autonomous vehicles in Intelligent Transportation Systems aim to boost the adaptability performances of complex problem-solving behaviour in the Sim-to-Real self-driving mission. However, the difficulty for Sim2Real adaption is the so-called “catastrophic forgetting” challenge, i.e., the pre-training policy exposes the flaws of the inability to retain previously skill motion when generalizing to the mixed real-world scenario, which affects learning in an inefficient way. This paper could deal with the above challenge by taking advantage of reconfigurable Sim2Real policies from simpler, previously learned sub-tasks, which are superior to those of pre-defined artificial systems. Specifically, a novel reward-oriented hierarchical learning framework based on hierarchical cognitive mechanisms is proposed dedicated to Sim2Real autonomous driving. Such a learning mechanism breaks down the behavior-aware experience into two distinguished types concerning environmental rewards: basic task-agnostic background and dynamic object-specific foreground. It further reveals the intrinsic association between previously learned knowledge and multiple changing events, by utilizing goal-conditioned key skill motion tailored for specific sub-task rewards. Moreover, the reconfigurable Sim2Real rehearsal is developed to boost the efficiency of high-level policies’ generalization ability according to the reuse of the configurable skill motion via mirrored composition. Extensive validation on both simulated and real-world Sim2Real testbench of challenging autonomous driving scenarios outperforms, demonstrating the superiority of the proposed learning mechanism in improving task efficiency and handling stochasticity throughout learning.
{"title":"Effective Learning Mechanism Based on Reward-Oriented Hierarchies for Sim-to-Real Adaption in Autonomous Driving Systems","authors":"Zhiming Hong","doi":"10.1109/TITS.2024.3524882","DOIUrl":"https://doi.org/10.1109/TITS.2024.3524882","url":null,"abstract":"Autonomous vehicles in Intelligent Transportation Systems aim to boost the adaptability performances of complex problem-solving behaviour in the Sim-to-Real self-driving mission. However, the difficulty for Sim2Real adaption is the so-called “catastrophic forgetting” challenge, i.e., the pre-training policy exposes the flaws of the inability to retain previously skill motion when generalizing to the mixed real-world scenario, which affects learning in an inefficient way. This paper could deal with the above challenge by taking advantage of reconfigurable Sim2Real policies from simpler, previously learned sub-tasks, which are superior to those of pre-defined artificial systems. Specifically, a novel reward-oriented hierarchical learning framework based on hierarchical cognitive mechanisms is proposed dedicated to Sim2Real autonomous driving. Such a learning mechanism breaks down the behavior-aware experience into two distinguished types concerning environmental rewards: basic task-agnostic background and dynamic object-specific foreground. It further reveals the intrinsic association between previously learned knowledge and multiple changing events, by utilizing goal-conditioned key skill motion tailored for specific sub-task rewards. Moreover, the reconfigurable Sim2Real rehearsal is developed to boost the efficiency of high-level policies’ generalization ability according to the reuse of the configurable skill motion via mirrored composition. Extensive validation on both simulated and real-world Sim2Real testbench of challenging autonomous driving scenarios outperforms, demonstrating the superiority of the proposed learning mechanism in improving task efficiency and handling stochasticity throughout learning.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"26 3","pages":"3527-3542"},"PeriodicalIF":7.9,"publicationDate":"2025-01-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143563907","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}
Pub Date : 2025-01-14DOI: 10.1109/TITS.2024.3518135
Simona Sacone
“Scanning the Issue.“
“扫描问题”。”
{"title":"Scanning the Issue","authors":"Simona Sacone","doi":"10.1109/TITS.2024.3518135","DOIUrl":"https://doi.org/10.1109/TITS.2024.3518135","url":null,"abstract":"“Scanning the Issue.“","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"26 1","pages":"3-21"},"PeriodicalIF":7.9,"publicationDate":"2025-01-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10841924","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142992917","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}
Pub Date : 2025-01-14DOI: 10.1109/TITS.2024.3503496
Maurice Kolff;Joost Venrooij;Elena Arcidiacono;Daan M. Pool;Max Mulder
This paper presents a three-step validation approach for subjective rating predictions of driving simulator motion incongruences based on objective mismatches between reference vehicle and simulator motion. This approach relies on using high-resolution rating predictions of open-loop driving (participants being driven) for ratings of motion in closed-loop driving (participants driving themselves). A driving simulator experiment in an urban scenario is described, of which the rating data of 36 participants was recorded and analyzed. In the experiment’s first phase, participants actively drove themselves (i.e., closed-loop). By recording the drives of the participants and playing these back to themselves (open-loop) in the second phase, participants experienced the same motion in both phases. Participants rated the motion after each maneuver and at the end of each drive. In the third phase they again drove open-loop, but rated the motion continuously, only possible in open-loop driving. Results show that a rating model, acquired through a different experiment, can well predict the measured continuous ratings. Second, the maximum of the measured continuous ratings correlates to both the maneuver-based ($rho =0.94$ ) and overall ($rho =0.69$ ) ratings, allowing for predictions of both rating types based on the continuous rating model. Third, using Bayesian statistics it is then shown that both the maneuver-based and overall ratings between the closed-loop and open-loop drives are equivalent. This allows for predictions of maneuver-based and overall ratings using the high-resolution continuous rating models. These predictions can be used as an accurate trade-off method of motion cueing settings of future closed-loop driving simulator experiments.
{"title":"Predicting Motion Incongruence Ratings in Closed- and Open-Loop Urban Driving Simulation","authors":"Maurice Kolff;Joost Venrooij;Elena Arcidiacono;Daan M. Pool;Max Mulder","doi":"10.1109/TITS.2024.3503496","DOIUrl":"https://doi.org/10.1109/TITS.2024.3503496","url":null,"abstract":"This paper presents a three-step validation approach for subjective rating predictions of driving simulator motion incongruences based on objective mismatches between reference vehicle and simulator motion. This approach relies on using high-resolution rating predictions of open-loop driving (participants being driven) for ratings of motion in closed-loop driving (participants driving themselves). A driving simulator experiment in an urban scenario is described, of which the rating data of 36 participants was recorded and analyzed. In the experiment’s first phase, participants actively drove themselves (i.e., closed-loop). By recording the drives of the participants and playing these back to themselves (open-loop) in the second phase, participants experienced the same motion in both phases. Participants rated the motion after each maneuver and at the end of each drive. In the third phase they again drove open-loop, but rated the motion continuously, only possible in open-loop driving. Results show that a rating model, acquired through a different experiment, can well predict the measured continuous ratings. Second, the maximum of the measured continuous ratings correlates to both the maneuver-based (<inline-formula> <tex-math>$rho =0.94$ </tex-math></inline-formula>) and overall (<inline-formula> <tex-math>$rho =0.69$ </tex-math></inline-formula>) ratings, allowing for predictions of both rating types based on the continuous rating model. Third, using Bayesian statistics it is then shown that both the maneuver-based and overall ratings between the closed-loop and open-loop drives are equivalent. This allows for predictions of maneuver-based and overall ratings using the high-resolution continuous rating models. These predictions can be used as an accurate trade-off method of motion cueing settings of future closed-loop driving simulator experiments.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"26 1","pages":"517-528"},"PeriodicalIF":7.9,"publicationDate":"2025-01-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142976138","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}
Pub Date : 2025-01-13DOI: 10.1109/TITS.2024.3520177
Zhuhua Hu;Wenlu Qi;Kunkun Ding;Hao Qi;Yaochi Zhao;Xuebo Zhang;Mingfeng Wang
VSLAM is one of the key technologies for indoor mobile robots, used to perceive the surrounding environment, achieve accurate positioning and mapping. However, traditional VSLAM algorithms based on the assumption of a static environment still face certain challenges. The movement, occlusion, and appearance changes of dynamic objects can lead to feature point-matching errors, making data association difficult and causing biases in motion estimation. In order to address this challenge, this paper proposes a dynamic feature point removal method and a closed-loop detection method for high dynamic scenes, aiming to effectively improve the robustness and positioning accuracy in dynamic environments. First, the YOLOv7-tiny object detection network and LK optical flow algorithm are combined to detect the dynamic area, and the adaptive threshold keyframe selection method is adopted to solve the problem of poor quality of keyframe caused by the existing heuristic threshold selection method. Then, this paper proposes a dynamic keyframe sequence creation method based on the angle difference between keyframes, which reduces the workload of loop back detection and accelerates the efficiency of loop back detection in the system. Next, the ParC_NetVLAD image matching algorithm is proposed. In this paper, ConvNeXt-Tiny network is used for feature extraction of images, and ParC-Net network and CBAM attention mechanism are added to the feature extraction network. Finally, NetVLAD is used to cluster the extracted local features to obtain global features that can represent images. Experiments are conducted on public TUM RGB-D datasets and in real-world situations. The proposed algorithm reduces the ATE (Absolute Trajectory Error) by 96.4% and the RPE (Relative Trajectory Error) by 82.8% on average in highly dynamic scenarios. In the Pittsburgh30k dataset, the average accuracy of loop closure detection has been improved by 2.6%.
{"title":"Optimized Feature Points and Keyframe Methods for VSLAM in High-Dynamic Indoor Environments","authors":"Zhuhua Hu;Wenlu Qi;Kunkun Ding;Hao Qi;Yaochi Zhao;Xuebo Zhang;Mingfeng Wang","doi":"10.1109/TITS.2024.3520177","DOIUrl":"https://doi.org/10.1109/TITS.2024.3520177","url":null,"abstract":"VSLAM is one of the key technologies for indoor mobile robots, used to perceive the surrounding environment, achieve accurate positioning and mapping. However, traditional VSLAM algorithms based on the assumption of a static environment still face certain challenges. The movement, occlusion, and appearance changes of dynamic objects can lead to feature point-matching errors, making data association difficult and causing biases in motion estimation. In order to address this challenge, this paper proposes a dynamic feature point removal method and a closed-loop detection method for high dynamic scenes, aiming to effectively improve the robustness and positioning accuracy in dynamic environments. First, the YOLOv7-tiny object detection network and LK optical flow algorithm are combined to detect the dynamic area, and the adaptive threshold keyframe selection method is adopted to solve the problem of poor quality of keyframe caused by the existing heuristic threshold selection method. Then, this paper proposes a dynamic keyframe sequence creation method based on the angle difference between keyframes, which reduces the workload of loop back detection and accelerates the efficiency of loop back detection in the system. Next, the ParC_NetVLAD image matching algorithm is proposed. In this paper, ConvNeXt-Tiny network is used for feature extraction of images, and ParC-Net network and CBAM attention mechanism are added to the feature extraction network. Finally, NetVLAD is used to cluster the extracted local features to obtain global features that can represent images. Experiments are conducted on public TUM RGB-D datasets and in real-world situations. The proposed algorithm reduces the ATE (Absolute Trajectory Error) by 96.4% and the RPE (Relative Trajectory Error) by 82.8% on average in highly dynamic scenarios. In the Pittsburgh30k dataset, the average accuracy of loop closure detection has been improved by 2.6%.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"26 3","pages":"3101-3114"},"PeriodicalIF":7.9,"publicationDate":"2025-01-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143535544","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}
Pub Date : 2025-01-10DOI: 10.1109/TITS.2024.3510642
Sambit Mohapatra;Senthil Yogamani;Varun Ravi Kumar;Stefan Milz;Heinrich Gotzig;Patrick Mäder
LiDAR is crucial for robust 3D scene perception in autonomous driving. LiDAR perception has the largest body of literature after camera perception. However, multi-task learning across tasks like detection, segmentation, and motion estimation using LiDAR remains relatively unexplored, especially on automotive-grade embedded platforms. We present a real-time multi-task convolutional neural network for LiDAR-based object detection, semantics, and motion segmentation. The unified architecture comprises a shared encoder and task-specific decoders, enabling joint representation learning. We propose a novel Semantic Weighting and Guidance (SWAG) module to transfer semantic features for improved object detection selectively. Our heterogeneous training scheme combines diverse datasets and exploits complementary cues between tasks. The work provides the first embedded implementation unifying these key perception tasks from LiDAR point clouds achieving 3ms latency on the embedded NVIDIA Xavier platform. We achieve state-of-the-art results for two tasks, semantic and motion segmentation, and close to state-of-the-art performance for 3D object detection. By maximizing hardware efficiency and leveraging multi-task synergies, our method delivers an accurate and efficient solution tailored for real-world automated driving deployment. Qualitative results can be seen at https://youtu.be/H-hWRzv2lIY.
{"title":"LiDAR-BEVMTN: Real-Time LiDAR Bird’s-Eye View Multi-Task Perception Network for Autonomous Driving","authors":"Sambit Mohapatra;Senthil Yogamani;Varun Ravi Kumar;Stefan Milz;Heinrich Gotzig;Patrick Mäder","doi":"10.1109/TITS.2024.3510642","DOIUrl":"https://doi.org/10.1109/TITS.2024.3510642","url":null,"abstract":"LiDAR is crucial for robust 3D scene perception in autonomous driving. LiDAR perception has the largest body of literature after camera perception. However, multi-task learning across tasks like detection, segmentation, and motion estimation using LiDAR remains relatively unexplored, especially on automotive-grade embedded platforms. We present a real-time multi-task convolutional neural network for LiDAR-based object detection, semantics, and motion segmentation. The unified architecture comprises a shared encoder and task-specific decoders, enabling joint representation learning. We propose a novel Semantic Weighting and Guidance (SWAG) module to transfer semantic features for improved object detection selectively. Our heterogeneous training scheme combines diverse datasets and exploits complementary cues between tasks. The work provides the first embedded implementation unifying these key perception tasks from LiDAR point clouds achieving 3ms latency on the embedded NVIDIA Xavier platform. We achieve state-of-the-art results for two tasks, semantic and motion segmentation, and close to state-of-the-art performance for 3D object detection. By maximizing hardware efficiency and leveraging multi-task synergies, our method delivers an accurate and efficient solution tailored for real-world automated driving deployment. Qualitative results can be seen at <uri>https://youtu.be/H-hWRzv2lIY</uri>.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"26 2","pages":"1547-1561"},"PeriodicalIF":7.9,"publicationDate":"2025-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10838288","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143184005","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}
Pub Date : 2025-01-10DOI: 10.1109/TITS.2024.3523488
Shiyao Zhang;Shengyu Zhang
As one of the most promising elements in Intelligent Transportation Systems (ITSs), connected electric vehicles (CEVs) can be collectively utilized to improve the quality of essential transportation services. However, involving CEVs to provide vehicle-to-grid (V2G) services becomes a crucial problem since they are selfish and belong to different parties. To solve this problem, we propose an efficient federated CEV scheduling framework that implements noncooperative online incentive approach. In particular, the proposed system is designed for providing privacy-preserving power grid signals to each CEV aggregator (CEVA) within the citywide region. To motivate the CEVs to participate in V2G services, a noncooperative interaction scheme is designed between the selfish CEVs and each CEVA. The purpose of the game is to let the CEVA to determine the real-time electricity trading prices, while the CEVs decide their own real-time service schedules. Case studies assess the feasibility and effectiveness of proposed noncooperative incentive approach, in which the efficient motivation on the CEVs contribute to a high quality V2G services. Additionally, the use of sufficient online parking allocation method can further increase the quality of V2G services.
{"title":"Efficient Federated Connected Electric Vehicle Scheduling System: A Noncooperative Online Incentive Approach","authors":"Shiyao Zhang;Shengyu Zhang","doi":"10.1109/TITS.2024.3523488","DOIUrl":"https://doi.org/10.1109/TITS.2024.3523488","url":null,"abstract":"As one of the most promising elements in Intelligent Transportation Systems (ITSs), connected electric vehicles (CEVs) can be collectively utilized to improve the quality of essential transportation services. However, involving CEVs to provide vehicle-to-grid (V2G) services becomes a crucial problem since they are selfish and belong to different parties. To solve this problem, we propose an efficient federated CEV scheduling framework that implements noncooperative online incentive approach. In particular, the proposed system is designed for providing privacy-preserving power grid signals to each CEV aggregator (CEVA) within the citywide region. To motivate the CEVs to participate in V2G services, a noncooperative interaction scheme is designed between the selfish CEVs and each CEVA. The purpose of the game is to let the CEVA to determine the real-time electricity trading prices, while the CEVs decide their own real-time service schedules. Case studies assess the feasibility and effectiveness of proposed noncooperative incentive approach, in which the efficient motivation on the CEVs contribute to a high quality V2G services. Additionally, the use of sufficient online parking allocation method can further increase the quality of V2G services.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"26 3","pages":"3934-3946"},"PeriodicalIF":7.9,"publicationDate":"2025-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143535546","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}
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