Advanced Driver Intention Inference
1st Edition
Theory and Design
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Description
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.
Key Features
- 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
Readership
Automotive engineers and graduate students
Table of Contents
PART I: INTRODUCTION AND MOTIVATION
1. Introduction and Motivation
PART II: LITERATURE REVIEW. State-of-art of driver intention inference
2. Survey to Driver Intention Inference
PART III: TRAFFIC CONTEXT PERCEPTION. Integrated lane detection systems
3. Survey to Lane Detection Systems Integration and Evaluation
4. Integrated Lane Detection Systems Design
PART IV: DRIVER BEHAVIOUR REASONING. Driving actions and secondary tasks recognition
5. Driver Behaviour Recognition with Feature Evaluation
6. Driver Behaviour Detection with an End-to-End Approach
PART V: DRIVER BRAKING AND LANE CHANGE MANOEUVERS. Intention inference
7. Driver Braking Intensity Classification and Quantitative Inference
8. Driver Lane Change Intention Inference
PART VI: CONCLUSION AND FINAL REMARKS
9. Conclusions, Discussions and Directions for Future Work
Details
- No. of pages:
- 258
- Language:
- English
- Copyright:
- © Elsevier 2020
- Published:
- 18th March 2020
- Imprint:
- Elsevier
- Paperback ISBN:
- 9780128191132
- eBook ISBN:
- 9780128191149
About the Authors
Yang Xing
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.
Affiliations and Expertise
Postdoctoral Fellow in Division of Environment and Sustainability, Hong Kong University of Science and Technology.
Chen Lv
Chen Lv is currently an Assistant Professor at the Nanyang Technological University in Singapore. He received the Ph.D. degree at Department of Automotive Engineering, Tsinghua University, China in 2016. From 2014 to 2015, he was a joint PhD researcher at EECS Dept., University of California, Berkeley. His research focuses on cyber-physical system, hybrid system, advanced vehicle control and intelligence, where he has contributed over 40 papers and obtained 11 granted China patents. Dr. Lv serves as a Guest Editor for IEEE/ASME Transactions on Mechatronics, IEEE Transactions on Industrial Informatics and International Journal of Powertrains, and an Associate Editor for International Journal of Electric and Hybrid Vehicles, International Journal of Vehicle Systems Modelling and Testing, International Journal of Science and Engineering for Smart Vehicles, and Journal of Advances in Vehicle Engineering. He received the Highly Commended Paper Award of IMechE UK in 2012, the National Fellowship for Doctoral Student in 2013, the NSK Outstanding Mechanical Engineering Paper Award in 2014, the Tsinghua University Graduate Student Academic Rising Star Nomination Award in 2015, the China SAE Outstanding Paper Award in 2015, the 1st Class Award of China Automotive Industry Scientific and Technological Invention in 2015, and the Tsinghua University Outstanding Doctoral Thesis Award in 2016.
Affiliations and Expertise
Assistant Professor, School of Mechanical and Aerospace Engineering, Nanyang Technological University, Singapore
Dongpu Cao
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.
Affiliations and Expertise
Associate Professor, University of Waterloo, Canada
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