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Multi-Agent Game Theory-Based Coordinated Ramp Metering Method for Urban Expressways With Multi-Bottleneck
IF 7.9 1区 工程技术 Q1 ENGINEERING, CIVIL Pub Date : 2025-01-16 DOI: 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%.
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引用次数: 0
Switching Dynamic Event-Triggered Sliding Mode Based Trajectory Tracking Control for ASVs With Nonlinear Dead-Zone and Saturation Inputs
IF 7.9 1区 工程技术 Q1 ENGINEERING, CIVIL Pub Date : 2025-01-15 DOI: 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.
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引用次数: 0
IEEE Intelligent Transportation Systems Society Information IEEE智能交通系统学会信息
IF 7.9 1区 工程技术 Q1 ENGINEERING, CIVIL Pub Date : 2025-01-14 DOI: 10.1109/TITS.2024.3518293
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引用次数: 0
Effective Learning Mechanism Based on Reward-Oriented Hierarchies for Sim-to-Real Adaption in Autonomous Driving Systems
IF 7.9 1区 工程技术 Q1 ENGINEERING, CIVIL Pub Date : 2025-01-14 DOI: 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.
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引用次数: 0
IEEE INTELLIGENT TRANSPORTATION SYSTEMS SOCIETY Ieee智能交通系统学会
IF 7.9 1区 工程技术 Q1 ENGINEERING, CIVIL Pub Date : 2025-01-14 DOI: 10.1109/TITS.2024.3518292
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引用次数: 0
Scanning the Issue 扫描问题
IF 7.9 1区 工程技术 Q1 ENGINEERING, CIVIL Pub Date : 2025-01-14 DOI: 10.1109/TITS.2024.3518135
Simona Sacone
“Scanning the Issue.“
“扫描问题”。”
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引用次数: 0
Predicting Motion Incongruence Ratings in Closed- and Open-Loop Urban Driving Simulation 闭环和开环城市驾驶仿真中运动不一致等级的预测
IF 7.9 1区 工程技术 Q1 ENGINEERING, CIVIL Pub Date : 2025-01-14 DOI: 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.
本文提出了一种基于参考车辆与模拟器运动客观不匹配的驾驶模拟器运动不一致性主观评定预测的三步验证方法。这种方法依赖于使用开环驾驶(参与者被驾驶)的高分辨率评级预测来对闭环驾驶(参与者自己驾驶)的运动进行评级。介绍了一种城市场景下的驾驶模拟器实验,记录并分析了36名参与者的评分数据。在实验的第一阶段,参与者主动驱动自己(即闭环)。在第二阶段,通过记录参与者的驱动并回放给自己听(开环),参与者在两个阶段都经历了相同的运动。参与者在每次动作后和每次驾驶结束时对动作进行评分。在第三阶段,他们再次开环驱动,但连续额定运动,只有在开环驱动下才有可能。结果表明,通过不同的实验获得的评级模型可以很好地预测实测的连续评级。其次,测量的连续评级最大值与基于机动的评级($rho =0.94$)和总体评级($rho =0.69$)相关,从而允许基于连续评级模型的两种评级类型的预测。第三,使用贝叶斯统计,然后表明闭环和开环驱动器之间基于机动和总体评级是等效的。这允许使用高分辨率连续评级模型预测基于机动和总体评级。这些预测可以作为未来闭环驾驶模拟器实验中运动提示设置的精确权衡方法。
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引用次数: 0
Optimized Feature Points and Keyframe Methods for VSLAM in High-Dynamic Indoor Environments
IF 7.9 1区 工程技术 Q1 ENGINEERING, CIVIL Pub Date : 2025-01-13 DOI: 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%.
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引用次数: 0
LiDAR-BEVMTN: Real-Time LiDAR Bird’s-Eye View Multi-Task Perception Network for Autonomous Driving
IF 7.9 1区 工程技术 Q1 ENGINEERING, CIVIL Pub Date : 2025-01-10 DOI: 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.
激光雷达对于自动驾驶中稳健的三维场景感知至关重要。激光雷达感知的文献数量仅次于摄像头感知。然而,使用激光雷达进行检测、分割和运动估计等任务的多任务学习仍相对欠缺,尤其是在汽车级嵌入式平台上。我们提出了一种实时多任务卷积神经网络,用于基于激光雷达的物体检测、语义和运动分割。该统一架构由共享编码器和特定任务解码器组成,实现了联合表示学习。我们提出了一个新颖的语义加权和引导(SWAG)模块,用于转移语义特征,从而有选择性地改进物体检测。我们的异构训练方案结合了不同的数据集,并利用了任务之间的互补线索。这项工作提供了首个嵌入式实施方案,在嵌入式英伟达 Xavier 平台上实现了 3 毫秒的延迟,统一了来自激光雷达点云的这些关键感知任务。我们在语义和运动分割这两项任务上取得了最先进的结果,在三维物体检测方面也接近最先进的性能。通过最大限度地提高硬件效率和利用多任务协同效应,我们的方法为现实世界的自动驾驶部署提供了准确高效的解决方案。定性结果见 https://youtu.be/H-hWRzv2lIY。
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引用次数: 0
Efficient Federated Connected Electric Vehicle Scheduling System: A Noncooperative Online Incentive Approach
IF 7.9 1区 工程技术 Q1 ENGINEERING, CIVIL Pub Date : 2025-01-10 DOI: 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.
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引用次数: 0
期刊
IEEE Transactions on Intelligent Transportation Systems
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