
Advanced Driver Intention Inference
Theory and Design
Description
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
Table of Contents
PART I: INTRODUCTION AND MOTIVATION
1. 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 systems
3. Survey to Lane Detection Systems Integration and Evaluation
4. Integrated Lane Detection Systems DesignPART 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 ApproachPART V: DRIVER BRAKING AND LANE CHANGE MANOEUVERS. Intention inference
7. Driver Braking Intensity Classification and Quantitative Inference
8. Driver Lane Change Intention InferencePART VI: CONCLUSION AND FINAL REMARKS
9. Conclusions, Discussions and Directions for Future Work
Product details
- No. of pages: 258
- Language: English
- Copyright: © Elsevier 2020
- Published: March 15, 2020
- Imprint: Elsevier
- Paperback ISBN: 9780128191132
- eBook ISBN: 9780128191149
About the Authors
Yang Xing
Affiliations and Expertise
Chen Lv
Affiliations and Expertise
Dongpu Cao
Affiliations and Expertise
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