Home Technology 16 Connected and Automated Vehicles: Study of Platooning
Chapter
Licensed
Unlicensed Requires Authentication

16 Connected and Automated Vehicles: Study of Platooning

  • Dhruvkumar Patel and Rym Zalila-Wenkstern
Become an author with De Gruyter Brill
Explore this Subject How to publish with us
Vehicles, Drivers, and Safety
This chapter is in the book Vehicles, Drivers, and Safety

Abstract

Connected and automated vehicles combine leading edge technologies to allow vehicles to be self-aware, and communicate roadway information with other vehicles and drivers. Among the most promising applications for connected and automated vehicles (CAVs) is platooning, that is, the synchronized movement of two or more vehicles as a unit. Platooning holds great potential to make road transport safer, cleaner, and more efficient. In this chapter, we discuss CAV technologies, architectures, and computing approaches for platooning.

Abstract

Connected and automated vehicles combine leading edge technologies to allow vehicles to be self-aware, and communicate roadway information with other vehicles and drivers. Among the most promising applications for connected and automated vehicles (CAVs) is platooning, that is, the synchronized movement of two or more vehicles as a unit. Platooning holds great potential to make road transport safer, cleaner, and more efficient. In this chapter, we discuss CAV technologies, architectures, and computing approaches for platooning.

Chapters in this book

  1. Frontmatter I
  2. Contents V
  3. Contributing Authors VII
  4. Introduction XI
  5. Part A: Driver/Vehicle Interaction Systems
  6. 1 MobileUTDrive: A Portable Device Platform for In-vehicle Driving Data Collection 3
  7. 2 Semantic Analysis of Driver Behavior by Data Fusion 25
  8. 3 Predicting When Drivers Need AR Guidance 35
  9. 4 Driver’s Mental Workload Estimation with Involuntary Eye Movement 49
  10. 5 Neurophysiological Driver Behavior Analysis 67
  11. 6 Modeling the Relationship between Driver Gaze Behavior and Traffic Context during Lane Changes Using a Recurrent Neural Network 87
  12. 7 A Multimodal Control System for Autonomous Vehicles Using Speech, Gesture, and Gaze Recognition 101
  13. 8 Head Pose as an Indicator of Drivers’ Visual Attention 113
  14. Part B: Models & Theories of Driver/Vehicle Systems
  15. 9 Evolving Neural Network Controllers for Tractor-Trailer Vehicle Backward Path Tracking 135
  16. 10 Spectral Distance Analysis for Quality Estimation of In-Car Communication Systems 149
  17. 11 Combination of Hands-Free and ICC Systems 165
  18. 12 Insights into Automotive Noise PSD Estimation Based on Multiplicative Constants 183
  19. 13 In-Car Communication: From Single- to Four-Channel with the Frequency Domain Adaptive Kalman Filter 213
  20. Part C: Self–driving and the Mobility in 2050
  21. 14 The PIX Moving KuaiKai: Building a Self-Driving Car in Seven Days 233
  22. 15 Vehicle Ego-Localization with a Monocular Camera Using Epipolar Geometry Constraints 251
  23. 16 Connected and Automated Vehicles: Study of Platooning 263
  24. 17 Epilogue – Future Mobility 2050 285
  25. Index 311
https://doi.org/10.1515/9783110669787-016

Chapters in this book

  1. Frontmatter I
  2. Contents V
  3. Contributing Authors VII
  4. Introduction XI
  5. Part A: Driver/Vehicle Interaction Systems
  6. 1 MobileUTDrive: A Portable Device Platform for In-vehicle Driving Data Collection 3
  7. 2 Semantic Analysis of Driver Behavior by Data Fusion 25
  8. 3 Predicting When Drivers Need AR Guidance 35
  9. 4 Driver’s Mental Workload Estimation with Involuntary Eye Movement 49
  10. 5 Neurophysiological Driver Behavior Analysis 67
  11. 6 Modeling the Relationship between Driver Gaze Behavior and Traffic Context during Lane Changes Using a Recurrent Neural Network 87
  12. 7 A Multimodal Control System for Autonomous Vehicles Using Speech, Gesture, and Gaze Recognition 101
  13. 8 Head Pose as an Indicator of Drivers’ Visual Attention 113
  14. Part B: Models & Theories of Driver/Vehicle Systems
  15. 9 Evolving Neural Network Controllers for Tractor-Trailer Vehicle Backward Path Tracking 135
  16. 10 Spectral Distance Analysis for Quality Estimation of In-Car Communication Systems 149
  17. 11 Combination of Hands-Free and ICC Systems 165
  18. 12 Insights into Automotive Noise PSD Estimation Based on Multiplicative Constants 183
  19. 13 In-Car Communication: From Single- to Four-Channel with the Frequency Domain Adaptive Kalman Filter 213
  20. Part C: Self–driving and the Mobility in 2050
  21. 14 The PIX Moving KuaiKai: Building a Self-Driving Car in Seven Days 233
  22. 15 Vehicle Ego-Localization with a Monocular Camera Using Epipolar Geometry Constraints 251
  23. 16 Connected and Automated Vehicles: Study of Platooning 263
  24. 17 Epilogue – Future Mobility 2050 285
  25. Index 311
Sign up now to receive a 20% welcome discount
Institutional Access
How does access work?
Have an idea on how to improve our website?
Please write us.
© 2025 De Gruyter Brill
Downloaded on 16.10.2025 from https://www.degruyterbrill.com/document/doi/10.1515/9783110669787-016/html
Scroll to top button