5.0 Applications of AI in Transportation Industry

This introductory article opens the section on “Applications of AI in Transportation Industry”, giving a broad overview of the latest AI technologies in the transportation industry, with an additional focus on the developments enabling automated Mobility-as-a-Service (MaaS). It presents future capabilities and opportunities for AI, together with covering state-of-the-art Intelligent Transport Systems (ITS) trends, including advancements on the vehicle, infrastructure, and management level. Finally, the article outlines the two papers included in this section, highlighting concepts and challenges of using AI for automated, optimised, and individual passenger transport.

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[2]  Tarik Taleb,et al.  Roads Infrastructure Digital Twin: A Step Toward Smarter Cities Realization , 2021, IEEE Network.

[3]  Richard Bowden,et al.  A Survey of Deep Learning Applications to Autonomous Vehicle Control , 2019, IEEE Transactions on Intelligent Transportation Systems.

[4]  Alberto Ferreira de Souza,et al.  Self-Driving Cars: A Survey , 2019, Expert Syst. Appl..

[5]  Hong-Yuan Mark Liao,et al.  YOLOv4: Optimal Speed and Accuracy of Object Detection , 2020, ArXiv.

[6]  Dragomir Anguelov,et al.  Scalability in Perception for Autonomous Driving: Waymo Open Dataset , 2019, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[7]  Tiberiu T. Cocias,et al.  A survey of deep learning techniques for autonomous driving , 2019, J. Field Robotics.

[8]  Trevor Darrell,et al.  BDD100K: A Diverse Driving Dataset for Heterogeneous Multitask Learning , 2018, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[9]  Björn W. Schuller,et al.  A Deep Learning Approach for Location Independent Throughput Prediction , 2019, 2019 IEEE International Conference on Connected Vehicles and Expo (ICCVE).

[10]  Christof Büskens,et al.  Controlling an Autonomous Vehicle with Deep Reinforcement Learning , 2019, 2019 IEEE Intelligent Vehicles Symposium (IV).

[11]  Marcin Budka,et al.  Survey of ETA prediction methods in public transport networks , 2019, ArXiv.

[12]  Jingyu Wang,et al.  Knowledge-Driven Service Offloading Decision for Vehicular Edge Computing: A Deep Reinforcement Learning Approach , 2019, IEEE Transactions on Vehicular Technology.

[13]  Zhu Han,et al.  A Deep Reinforcement Learning Network for Traffic Light Cycle Control , 2018, IEEE Transactions on Vehicular Technology.

[14]  Chaiyaphum Siripanpornchana,et al.  Travel-time prediction with deep learning , 2016, 2016 IEEE Region 10 Conference (TENCON).

[15]  Sebastian Ramos,et al.  The Cityscapes Dataset for Semantic Urban Scene Understanding , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[16]  Andreas Geiger,et al.  Vision meets robotics: The KITTI dataset , 2013, Int. J. Robotics Res..