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International IEEE Conference on Intelligent Systems, IS

作者： K. Hirota Kewei Chen Fangyan Dong Department of Computational Intelligence and Systems Science Institute of Technology Yokohama Tokyo Japan Computing Technology Driving Laboratory Kawasaki Japan

The vehicle dispatching problem for cooperative deliveries from multiple depots (VDP/CD/MD) is an important and difficult problem in the transport industries. In order to solve the VDP/CD/MD, A hierarchical multiplex structure (HIMS/sup ++/) calculation model is proposed. The HIMS/sup ++/ model take advantage of object-oriented modeling, heuristic method, and fuzzy inference in (atomic, molecular, individual) 3 layers, so it can find a utility decision (vehicles plan) close to expert dispatcher. Furthermore, since the vital input parameters are few and the computational engine is packaged into a software component, the HIMS/sup ++/ model is a convenient tool for the VDP/CD/MD. The HIMS/sup ++/ model and its optimization algorithm are implemented as a software component using object-oriented paradigm, and through meta-programming. The performance of the HIMS/sup ++/ model is evaluated through experiments using 3 days oil delivery data taken from an actual dispatching center in Tokyo area. A total of 27 tank lorries are available for daily cooperative deliveries from 3 depots to about 30-60 destinations with different owners are used to save a running cost. The experimental results and the evaluations by human experts confirm that the HIMS/sup ++/ model are better than the results of experienced dispatchers in six evaluation objectives, and can be applied to the planning support system for the VDP/CD/MD. The HIMS/sup ++/ model will be able to cover similar transportation problems in the real-world.

关键词： Computational intelligence Intelligent vehicles Dispatching Object oriented modeling Health information management Software tools Atomic layer deposition Engines Software packages Packaging

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DNet-CNet: a novel cascaded deep network for real-time lane detection and classification

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Journal of Ambient Intelligence and Humanized computing 2023年第8期14卷 10745-10760页

作者： Zhang, Lu Jiang, Fengling Yang, Jing Kong, Bin Hussain, Amir Gogate, Mandar Dashtipour, Kia Hefei Institutes of Physical Science Chinese Academy of Sciences Hefei230031 China University of Science and Technology of China Hefei230026 China Anhui Engineering Laboratory for Intelligent Driving Technology and Application Hefei230088 China Hefei Normal University Hefei230601 China Hefei University Hefei230601 China School of Computing Edinburgh Napier University EdinburghEH10 5DT United Kingdom

Robust understanding of the lane position and type is essential for changing lanes in autonomous vehicles. However, accomplishing this task in real time with high level of precision is not trivial. In this paper, we propose a novel cascaded deep neural network (DNet-CNet) for real-time end-to-end lane detection (DNet) and classification (CNet). The proposed model can simultaneously predict the lanes position and types. DNet integrates the spatial features extracted from the encoder with those from the decoder to compensate for the lower dimensional encoded data and edge information. Furthermore, the output of DNet is fused with the input image for real time lightweight lane classification model (CNet). The combined features exploit the inherent colors and shape of lanes to improve classification accuracy. Experimental results on the benchmark TuSimple, Caltech-lanes and ELAS datasets show that, the model proposed achieves superior lane detection and classification accuracy in real-time as compared to Cascade-CNN. © 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Classification (of information)

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Parameterized Cost Volume for Stereo Matching

Parameterized Cost Volume for Stereo Matching

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International Conference on Computer Vision (ICCV)

作者： Jiaxi Zeng Chengtang Yao Lidong Yu Yuwei Wu Yunde Jia Beijing Key Laboratory of Intelligent Information Technology School of Computer Science & Technology Beijing Institute of Technology China Autonomous Driving Algorithm Deeproute Guangdong Laboratory of Machine Perception and Intelligent Computing Shenzhen MSU-BIT University China

Stereo matching becomes computationally challenging when dealing with a large disparity range. Prior methods mainly alleviate the computation through dynamic cost volume by focusing on a local disparity space, but it requires many iterations to get close to the ground truth due to the lack of a global view. We find that the dynamic cost volume approximately encodes the disparity space as a single Gaussian distribution with a fixed and small variance at each iteration, which results in an inadequate global view over disparity space and a small update step at every iteration. In this paper, we propose a parameterized cost volume to encode the entire disparity space using multi-Gaussian distribution. The disparity distribution of each pixel is parameterized by weights, means, and variances. The means and variances are used to sample disparity candidates for cost computation, while the weights and means are used to calculate the disparity output. The above parameters are computed through a JS-divergence-based optimization, which is realized as a gradient descent update in a feed-forward differential module. Experiments show that our method speeds up the runtime of RAFT-Stereo by 4 ~ 15 times, achieving real-time performance and comparable accuracy. The code is available at https://***/jiaxiZeng/Parameterized-Cost-Volume-for-Stereo-Matching.

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Sparse Point Guided 3D Lane Detection

Sparse Point Guided 3D Lane Detection

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International Conference on Computer Vision (ICCV)

作者： Chengtang Yao Lidong Yu Yuwei Wu Yunde Jia School of Computer Science & Technology Beijing Key Laboratory of Intelligent Information Technology Beijing Institute of Technology China Autonomous Driving Algorithm DeepRoute Guangdong Laboratory of Machine Perception and Intelligent Computing Shenzhen MSU-BIT University China

3D lane detection usually builds a dense correspondence between the front-view space and the BEV space to estimate lane points in the 3D space. 3D lanes only occupy a small ratio of the dense correspondence, while most correspondence belongs to the redundant background. This sparsity phenomenon bottlenecks valuable computation and raises the computation cost of building a high-resolution correspondence for accurate results. In this paper, we propose a sparse point-guided 3D lane detection, focusing on points related to 3D lanes. Our method runs in a coarse-to-fine manner, including coarse-level lane detection and iterative fine-level sparse point refinements. In coarse-level lane detection, we build a dense but efficient correspondence between the front view and BEV space at a very low resolution to compute coarse lanes. Then in fine-level sparse point refinement, we sample sparse points around coarse lanes to extract local features from the high-resolution front-view feature map. The high-resolution local information brought by sparse points refines 3D lanes in the BEV space hierarchically from low resolution to high resolution. The sparse point guides a more effective information flow and greatly promotes the SOTA result by 3 points on the overall F1-score and 6 points on several hard situations while reducing almost half memory cost and speeding up 2 times.

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Solving Truck Delivery Problems Using Integrated Evaluation Criteria Based on Neighborhood Degree and Evolutionary Algorithm

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Journal of Advanced Computational Intelligence and Intelligent Informatics 2004年第3期8卷 336-345页

作者： Dong, Fangyan Chen, Kewei Iyoda, Eduardo Masato Nobuhara, Hajime Hirota, Kaoru Department of Computational Intelligence and Systems Science Tokyo Institute of Technology G3-49 4259 Nagatsuta Midori-ku Yokohama226-8502 Japan Computing Technology Driving Laboratory Fujimi 1-6-14-605 Kawasaki-ku Kawasaki210-0011 Japan

To solve a real-world truck delivery and dispatch problem (TDDP) that involves multiple mutually conflicting objectives, such as running and loading costs, a concept of neighborhood degree (ND) and an integrated evaluation criteria (IEC) of the solution based on ND are proposed. The IEC makes the weight setting easier than by using conventional methods. To find a high-quality solution to a TDDP in practical computational time, an evolutionary algorithm is proposed. It involves 3 components: (i) a simulated annealing (SA)-based method for finding an optimal or a suboptimal route for each vehicle;(ii) an evolutionary computation (EC)-based method for finding an optimal schedule for a group of vehicles;and (iii) threshold-based evolutionary operations, utilizing the ND concept. The TDDP viewed from real-world application is formulated and the proposed algorithm is implemented on a personal computer using C++. The proposed algorithm is evaluated in 2 experiments involving real-world data representative of the TDDP, and applied to food product delivery to a chain of 46 convenience stores in Saitama Prefecture. In the 2 experiments, our proposed algorithm resulted in a better schedule (with 80%-90% shorter computational time) than a schedule produced by an expert. By incorporating application-specific evaluation criteria, the proposed algorithm is applied to problems such as home-delivery of parcels or mail, and to problems of multidepot delivery and dispatch. © Fuji technology Press Ltd. Creative Commons CC BY-ND: This is an Open Access article distributed under the terms of the Creative Commons Attribution-NoDerivatives 4.0 International License (http://***/licenses/by-nd/4.0/).

关键词： Simulated annealing

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RoW-based Parallel Control for Mixed Traffic Scenario: A Case Study on Lane-Changing

RoW-based Parallel Control for Mixed Traffic Scenario: A Cas...

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International Conference on Intelligent Transportation

作者： Jingru Yu Yi Yu Shengyue Yao Ding Wang Pinlong Cai Honghai Li Li Li Fei-Yue Wang Yilun Lin Shanghai Artificial Intelligence Laboratory Urban Computing Shanghai China Research and Development Center of Transport Industry of Autonomous Driving Technology RIOH High and Technology Group Department of Automation Tsinghua University Beijing China Macau Institute of Systems Engineering Macau University of Science and Technology Macau China Institute of Automation Chinese Academy of Sciences Beijing China

To address the challenge of developing effective control methods for governing the entire driving process of both connected and automated vehicles (CAVs) and human-driven vehicles (HDVs) in mixed traffic scenario, this paper proposes a parallel control framework with calibrating module and pricing control module. Based on parallel learning theory, this framework enables intelligent agents to take over both HDVs and CAVs and engage in right-of-way (RoW) negotiations in the mixed traffic network. Game theory is employed to model RoW transactions and incentivize transactions through utility compensation. Empirical datasets are used to showcase the performance of the proposed framework in a lane-changing scenario. Our findings demonstrate that the introduction of incentives can increase the RoW trading probability of drivers significantly. To the best of our knowledge, this is the first parallel control framework based on pricing mechanism allowing both CAVs and HDVs to negotiate based on intelligent agents.

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TransWorldNG: Traffic Simulation via Foundation Model

TransWorldNG: Traffic Simulation via Foundation Model

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International Conference on Intelligent Transportation

作者： Ding Wang Xuhong Wang Liang Chen Shengyue Yao Ming Jing Honghai Li Shiqiang Bao Li Li Fei-Yue Wang Yilun Lin Urban Computing Lab Shanghai Artificial Intelligence Laboratory Shanghai China 51 WORLD Shanghai China Research and Development Center of Transport Industry of Autonomous Driving Technology RIOH High and Technology Group Department of Automation Tsinghua University Beijing China Institute of Automation Chinese Academy of Sciences Beijing China Macau Institute of Systems Engineering Macau University of Science and Technology Macau China

Traffic simulation is a crucial tool for transportation decision-making and policy development. However, achieving realistic simulations in the face of the high dimensionality and heterogeneity of traffic environments is a longstanding challenge. In this paper, we present TransWordNG, a traffic simulator that uses Data-driven algorithms and Graph computing techniques to learn traffic dynamics from real data. The functionality and structure of TransWorldNG are introduced, which utilize a foundation model for transportation management and control. The results demonstrate that TransWorldNG can generate more realistic traffic patterns compared to traditional simulators. Additionally, TransWorldNG exhibits better scalability, as it shows linear growth in computation time as the scenario scale increases. To the best of our knowledge, this is the first traffic simulator that can automatically learn traffic patterns from real-world data and efficiently generate accurate and realistic traffic environments.

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TransWorldNG: Traffic Simulation via Foundation Model

arXiv

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arXiv 2023年

作者： Wang, Ding Wang, Xuhong Chen, Liang Yao, Shengyue Jing, Ming Li, Honghai Li, Li Bao, Shiqiang Wang, Fei-Yue Lin, Yilun Urban Computing Lab Shanghai Artificial Intelligence Laboratory Shanghai China 51 World Shanghai China Research and Development Center of Transport Industry of Autonomous Driving Technology RIOH High and Technology Group China Department of Automation Tsinghua University Beijing China Institute of Automation Chinese Academy of Sciences Beijing China Macau Institute of Systems Engineering Macau University of Science and Technology China

关键词： Decision making

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Towards Integrated Traffic Control with Operating Decentralized Autonomous Organization

arXiv

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arXiv 2023年

作者： Yao, Shengyue Yu, Jingru Yu, Yi Xu, Jia Dai, Xingyuan Li, Honghai Wang, Fei-Yue Lin, Yilun Urban Computing Shanghai AI Lab Shanghai China The State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China The Research and Development Center of Transport Industry of Autonomous Driving Technology RIOH High and Technology Group The Institute of Automation Chinese Academy of Sciences Beijing China The Macau Institute of Systems Engineering Macau University of Science and Technology China

With a growing complexity of the intelligent traffic system (ITS), an integrated control of ITS that is capable of considering plentiful heterogeneous intelligent agents is desired. However, existing control methods based on the centralized or the decentralized scheme have not presented their competencies in considering the optimality and the scalability simultaneously. To address this issue, we propose an integrated control method based on the framework of Decentralized Autonomous Organization (DAO). The proposed method achieves a global consensus on energy consumption efficiency (ECE), meanwhile to optimize the local objectives of all involved intelligent agents, through a consensus and incentive mechanism. Furthermore, an operation algorithm is proposed regarding the issue of structural rigidity in DAO. Specifically, the proposed operation approach identifies critical agents to execute the smart contract in DAO, which ultimately extends the capability of DAO-based control. In addition, a numerical experiment is designed to examine the performance of the proposed method. The experiment results indicate that the controlled agents can achieve a consensus faster on the global objective with improved local objectives by the proposed method, compare to existing decentralized control methods. In general, the proposed method shows a great potential in developing an integrated control system in the ITS. © 2023, CC BY.

关键词： Integrated control

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Towards Integrated Traffic Control with Operating Decentralized Autonomous Organization

Towards Integrated Traffic Control with Operating Decentrali...

引用

International Conference on Intelligent Transportation

作者： Shengyue Yao Jingru Yu Yi Yu Jia Xu Xingyuan Dai Honghai Li Fei-Yue Wang Yilun Lin Urban Computing Shanghai AI Lab Shanghai China The State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China Research and Development Center of Transport Industry of Autonomous Driving Technology RIOH High and Technology Group Institute of Automation Chinese Academy of Sciences Beijing China Macau Institute of Systems Engineering Macau University of Science and Technology Macau China

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