Autonomous Driving and Intelligent Transportation

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Transportation and Future Mobility".

Deadline for manuscript submissions: 29 February 2024 | Viewed by 2741

Special Issue Editors

Prof. Dr. Jianping Wu
E-Mail Website
Guest Editor
Department of Civil Engineering, Tsinghua University, Beijing 100084, China
Interests: travel behavior; traffic modeling; traffic simulation; big data; smart city and smart transportation; autonomous driving and future transportation systems
School of Civil and Environmental Engineering, Nanyang Technological University, Singapore 639798, Singapore
Interests: connected and automated vehicles; traffic flow; traffic control; traffic safety
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Special Issue Information

Dear Colleagues,

Autonomous driving and intelligent transportation could be the most important factors deciding the direction and pace of the development of the future of transportation, and they may even rewrite the definition of “travel” in the future.

This Special Issue will, therefore, be dedicated to the research on methodologies, technologies, and standards for the design, manufacture, testing and commercial application of autonomous driving vehicles, focusing on future transport infrastructure and future traffic management technologies in the era of autonomous driving.

The scope of this Special Issue includes but is not limited to the following research areas: sensing and recognition technology, V2X technology, driving path planning technology, vehicle control technology, road tests, simulation tests, road test evaluation index, simulation test evaluation index, road test specifications and standards, simulation test specifications and standards, road infrastructure for autonomous driving, traffic management technology in the autonomous driving era, robotaxi, autonomous driving buses, airport autonomous driving transportation, harbor autonomous driving transportation, expressway autonomous driving transportation, and other relevant topics.

Prof. Dr. Jianping Wu
Dr. Feng Zhu
Guest Editors

Manuscript Submission Information

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Keywords

  • autonomous driving technology
  • sensing and perception technology
  • connected vehicle and communication technology
  • driving path planning
  • vehicle control technology
  • autonomous driving road test and evaluation
  • autonomous driving simulation test and evaluation
  • road test method
  • road test process
  • road test specification standards
  • simulation test method
  • simulation test process
  • simulation test specification standards
  • future road infrastructure
  • future traffic management technology
  • future traffic management laws and regulations
  • implementation and operation of autonomous driving vehicles
  • robotaxi
  • autonomous driving buses
  • airport autonomous driving transportation
  • autonomous driving transportation in cargo (dock) terminals
  • collaborative driving of autonomous vehicles

Published Papers (3 papers)

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Research

19 pages, 4911 KiB  
Article
Trajectory Prediction with Attention-Based Spatial–Temporal Graph Convolutional Networks for Autonomous Driving
Appl. Sci. 2023, 13(23), 12580; https://doi.org/10.3390/app132312580 - 22 Nov 2023
Viewed by 652
Abstract
Accurate and reliable trajectory prediction is crucial for autonomous vehicles to achieve safe and efficient operation. Vehicles perceive the historical trajectories of moving objects and make predictions of behavioral intentions for a future period of time. With the predicted trajectories of moving objects [...] Read more.
Accurate and reliable trajectory prediction is crucial for autonomous vehicles to achieve safe and efficient operation. Vehicles perceive the historical trajectories of moving objects and make predictions of behavioral intentions for a future period of time. With the predicted trajectories of moving objects such as obstacle vehicles, pedestrians, and non-motorized vehicles as inputs, self-driving vehicles can make more rational driving decisions and plan more reasonable and safe vehicle motion behaviors. However, due to traffic environments such as intersection scenes with highly interdependent and dynamic attributes, the task of motion anticipation becomes challenging. Existing works focus on the mutual relationships among vehicles while ignoring other potential essential interactions such as vehicle–traffic rules. These studies have not yet deeply explored the intensive learning of interactions between multi-agents, which may result in evaluation deviations. Aiming to meet these issues, we have designed a novel framework, namely trajectory prediction with attention-based spatial–temporal graph convolutional networks (TPASTGCN). In our proposal, the multi-agent interaction mechanisms, including vehicle–vehicle and vehicle–traffic rules, are meticulously highlighted and integrated into one homogeneous graph by transferring the time-series data of traffic lights into the spatial–temporal domains. Through integrating the attention mechanism into the adjacency matrix, we effectively learn the different strengths of interactive association and improve the model’s ability to capture critical features. Simultaneously, we construct a hierarchical structure employing the spatial GCN and temporal GCN to extract the spatial dependencies of traffic networks. Profiting from the gated recurrent unit (GRU), the scene context in temporal dimensions is further attained and enhanced with the encoder. In such a way, the GCN and GRU networks are fused as a features extractor module in the proposed framework. Finally, the future potential trajectories generation tasks are performed by another GRU network. Experiments on real-world datasets demonstrate the superior performance of the scheme compared with several baselines. Full article
(This article belongs to the Special Issue Autonomous Driving and Intelligent Transportation)
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18 pages, 3606 KiB  
Article
Stability Analysis of the Vehicular Platoon with Sensing Delay and Communication Delay: CTCR Paradigm via Dixon Resultant
Appl. Sci. 2023, 13(21), 11807; https://doi.org/10.3390/app132111807 - 28 Oct 2023
Cited by 1 | Viewed by 708
Abstract
For the vehicular platoon consisting of connected automotive vehicles, time delays degrade both the internal stability and string stability. In this study, the internal stability and string stability of the vehicular platoon suffering from sensing delay and communication delay are investigated. In the [...] Read more.
For the vehicular platoon consisting of connected automotive vehicles, time delays degrade both the internal stability and string stability. In this study, the internal stability and string stability of the vehicular platoon suffering from sensing delay and communication delay are investigated. In the internal stability analysis, the necessary and sufficient internal stability condition is obtained and the exact time delay margins (ETDMs) are derived via the cluster treatment of characteristic root (CTCR) paradigm. A Dixon resultant matrix–based method is proposed to determine the kernel and offspring hypersurfaces of the CTCR paradigm, and then the computational burden of deriving the ETDMs is reduced significantly. In the string stability analysis, we first propose the string stability conditions for the situation no matter how large the frequency of the leader vehicle’s maneuver is. Furthermore, the more practical string stability conditions are studied by considering only the region of low frequency, where most of the energy of the spacing errors exists. Then, a lower bound of the time headway is deduced to enhance road capacity, so the potential of the vehicular platoon is fully motivated. Numerical simulations are provided to illustrate the effectiveness of the theoretical claims. Full article
(This article belongs to the Special Issue Autonomous Driving and Intelligent Transportation)
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20 pages, 4706 KiB  
Article
CQDFormer: Cyclic Quasi-Dynamic Transformers for Hourly Origin-Destination Estimation
Appl. Sci. 2023, 13(20), 11257; https://doi.org/10.3390/app132011257 - 13 Oct 2023
Cited by 1 | Viewed by 463
Abstract
Due to the inherent difficulty in direct observation of traffic demand (including generation, attraction, and assignment), the estimation of origin–destination (OD) poses a significant and intricate challenge in the realm of Intelligent Transportation Systems. As the state-of-the-art methods usually focus on a single [...] Read more.
Due to the inherent difficulty in direct observation of traffic demand (including generation, attraction, and assignment), the estimation of origin–destination (OD) poses a significant and intricate challenge in the realm of Intelligent Transportation Systems. As the state-of-the-art methods usually focus on a single traffic demand distribution, accurate estimation of OD in the face of diverse traffic demand and road structures remains a formidable task. To this end, this study proposes a novel model, Cyclic Quasi-Dynamic Transformers (CQDFormer), which leverages forward and backward neural networks for effective OD estimation and traffic assignment. The employment of quasi-dynamic assumption and self-attention mechanism enables CQDFormer to capture the diverse and non-linear characteristics inherent in traffic demand. We utilize calibrated simulations to generate traffic count-OD pairwise data. Additionally, we incorporate real prior matrices and traffic count data to mitigate the distributional shift between simulation and the reality. The proposed CQDFormer is examined using Simuation of Urban Mobility (SUMO), on a large-scale downtown area in Haikou, China, comprising 2328 roads and 1171 junctions. It is found that CQDFormer shows satisfied convergence performance, and achieves a reduction of RMSE by 46.98%, MAE by 45.40% and MAPE by 29.76%, in comparison to the state-of-the-art method with the best performance. Full article
(This article belongs to the Special Issue Autonomous Driving and Intelligent Transportation)
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