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.
1. Chapter 1. Introduction - p. 1 2. Chapter 2. State of the Art of Driver Lane Change Intention Inference - p. 21 3. Chapter 3. Road Perception in Driver Intention Inference System - p. 53 4. Chapter 4. Design of Integrated Road Perception and Lane Detection System for Driver Intention Inference - p. 77 5. Chapter 5. Driver Behavior Recognition in Driver Intention Inference Systems - p. 99 6. Chapter 6. Application of Deep Learning Methods in Driver Behavior Recognition - p. 135 7. Chapter 7. Longitudinal Driver Intention Inference - p. 157 8. Braking Intention Recognition Based on Unsupervised Machine Learning Methods - p. 157 9. Levenberg-Marquardt Backpropagation for State Estimation of a Safety-Critical Cyber-Physical System - p. 163 10. Hybrid-Learning-Based Classification and Quantitative Inference of Driver Braking Intensity - p. 172 11. Chapter 8. Driver Lane-Change Intention Inference - p. 193 12. Chapter 9. Conclusions, Discussions, and Directions for Future Work - p. 235 13. Index - p. 241
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.
1. Chapter 1. Introduction - p. 1 2. Chapter 2. State of the Art of Driver Lane Change Intention Inference - p. 21 3. Chapter 3. Road Perception in Driver Intention Inference System - p. 53 4. Chapter 4. Design of Integrated Road Perception and Lane Detection System for Driver Intention Inference - p. 77 5. Chapter 5. Driver Behavior Recognition in Driver Intention Inference Systems - p. 99 6. Chapter 6. Application of Deep Learning Methods in Driver Behavior Recognition - p. 135 7. Chapter 7. Longitudinal Driver Intention Inference - p. 157 8. Braking Intention Recognition Based on Unsupervised Machine Learning Methods - p. 157 9. Levenberg-Marquardt Backpropagation for State Estimation of a Safety-Critical Cyber-Physical System - p. 163 10. Hybrid-Learning-Based Classification and Quantitative Inference of Driver Braking Intensity - p. 172 11. Chapter 8. Driver Lane-Change Intention Inference - p. 193 12. Chapter 9. Conclusions, Discussions, and Directions for Future Work - p. 235 13. Index - p. 241
