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
Yang Xing is a Postdoctoral Fellow in Division of Environment and Sustainability at The Hong Kong University of Science and Technology. She researches urban air quality, especially open green spaces.
Autorentext
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.
Klappentext
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
Inhalt
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