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9 Evolving Neural Network Controllers for Tractor-Trailer Vehicle Backward Path Tracking

  • John M. Maroli and Ümit Ö Özgüner
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Vehicles, Drivers, and Safety
This chapter is in the book Vehicles, Drivers, and Safety

Abstract

An overview of neuroevolutionary controller development is presented with an application to tractor-trailer backward path tracking. Controlling a tractortrailer vehicle driving in reverse is a difficult nonlinear control problem with widespread significance in the industry. Automated backward path tracking and docking has the ability to save substantial amounts of time and resources if implemented on a large scale. The presented work demonstrates both feedforward and recurrent neural network backward path tracking controllers for tractor-trailers evolved using a genetic algorithm. The example scenario demonstrates the utility of neuroevolved controllers for solving difficult nonlinear control problems. The neuroevolutionary techniques detailed in this work fall under the umbrella of reinforcement learning, and it is shown that the methods used for developing the tractor-trailer controller can be easily extended for solving more generic control problems.

Abstract

An overview of neuroevolutionary controller development is presented with an application to tractor-trailer backward path tracking. Controlling a tractortrailer vehicle driving in reverse is a difficult nonlinear control problem with widespread significance in the industry. Automated backward path tracking and docking has the ability to save substantial amounts of time and resources if implemented on a large scale. The presented work demonstrates both feedforward and recurrent neural network backward path tracking controllers for tractor-trailers evolved using a genetic algorithm. The example scenario demonstrates the utility of neuroevolved controllers for solving difficult nonlinear control problems. The neuroevolutionary techniques detailed in this work fall under the umbrella of reinforcement learning, and it is shown that the methods used for developing the tractor-trailer controller can be easily extended for solving more generic control problems.

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/10.1515/9783110669787-009

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
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