Advanced Driver Intention Inference

Theory and Design

Häftad, Engelska, 2020

Av Yang Xing, Chen Lv, Dongpu Cao, Hong Kong University of Science and Technology.) Xing, Yang (Postdoctoral Fellow in Division of Environment and Sustainability, Singapore) Lv, Chen (Assistant Professor, School of Mechanical and Aerospace Engineering, Nanyang Technological University, Canada) Cao, Dongpu (Associate Professor, University of Waterloo, Chen LV

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Advanced Driver Intention Inference: Theory and Design describes one of the most important function for future ADAS, namely, the driver intention inference. The book contains the state-of-art knowledge on the construction of driver intention inference system, providing a better understanding on how the human driver intention mechanism will contribute to a more naturalistic on-board decision system for automated vehicles.

Features examples of using machine learning/deep learning to build industry products
Depicts future trends for driver behavior detection and driver intention inference
Discuss traffic context perception techniques that predict driver intentions such as Lidar and GPS

Produktinformation

Utgivningsdatum2020-03-18
Mått191 x 235 x undefined mm
Vikt450 g
FormatHäftad
SpråkEngelska
Antal sidor258
FörlagElsevier Science
ISBN9780128191132

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Yang Xing received his Ph. D. degree from Cranfield University, UK, in 2018. He is currently a research fellow with the department of mechanical and aerospace engineering at Nanyang Technological University, Singapore. His research interests include machine learning, driver behavior modeling, intelligent multi-agent collaboration, and intelligent/autonomous vehicles. His work focuses on the understanding of driver behaviors using machine-learning methods and intelligent and automated vehicle design. He received the IV2018 Best Workshop/Special Issue Paper Award. Dr. Xing serves as a Guest Editor for IEEE Internet of Thing, and he is an active reviewer for IEEE Transactions on Vehicular Technology, Industrial Electronics, and Intelligent Transportation Systems. Dr Chen Lv is an Assistant Professor at the School of Mechanical and Aerospace Engineering and the Cluster Director in Future Mobility Solutions at Nanyang Technological University. His research focuses on intelligent vehicles, automated driving, and human-machine systems, where he has contributed 2 books, more than 100 papers, and obtained 12 Chinese patents. He serves as Associate Editor for IEEE T-ITS, IEEE TVT, and IEEE T-IV. He received IEEE IV Best Workshop/Special Session Paper Award in 2018, Automotive Innovation Best Paper Award in 2020, the winner of Waymo Open Dataset Challenges at CVPR 2021, and Machines Young Investigator Award in 2022. Dongpu Cao received the Ph.D. degree from Concordia University, Canada, in 2008. He is currently an Associate Professor at University of Waterloo, Canada. His research focuses on vehicle dynamics and control, automated driving and parallel driving, where he has contributed more than 100 publications and 1 US patent. He received the ASME AVTT’2010 Best Paper Award and 2012 SAE Arch T. Colwell Merit Award. Dr. Cao serves as an Associate Editor for IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS, IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY, IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS, IEEE/ASME TRANSACTIONS ON MECHATRONICS and ASME JOURNAL OF DYNAMIC SYSTEMS, MEASUREMENT, AND CONTROL. He has been a Guest Editor for VEHICLE SYSTEM DYNAMICS, and IEEE TRANSACTIONS ON HUMAN-MACHINE SYSTEMS. He serves on the SAE International Vehicle Dynamics Standards Committee and a few ASME, SAE, IEEE technical committees.

PART I: INTRODUCTION AND MOTIVATION1. Introduction and MotivationPART II: LITERATURE REVIEW. State-of-art of driver intention inference 2. Survey to Driver Intention InferencePART III: TRAFFIC CONTEXT PERCEPTION. Integrated lane detection systems3. Survey to Lane Detection Systems Integration and Evaluation4. Integrated Lane Detection Systems DesignPART IV: DRIVER BEHAVIOUR REASONING. Driving actions and secondary tasks recognition5. Driver Behaviour Recognition with Feature Evaluation6. Driver Behaviour Detection with an End-to-End ApproachPART V: DRIVER BRAKING AND LANE CHANGE MANOEUVERS. Intention inference7. Driver Braking Intensity Classification and Quantitative Inference 8. Driver Lane Change Intention InferencePART VI: CONCLUSION AND FINAL REMARKS9. Conclusions, Discussions and Directions for Future Work