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Chapter 3 Understanding driver attention to objects for ADASs: what do drivers see?

  • Kolby Sarson and Michael Bauer
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Imaging Science
This chapter is in the book Imaging Science

Abstract

A driver’s actions and intent can be important factors in enabling advance driver assistance systems to assist drivers and avoid accidents. Having some knowledge of what traffic relevant objects the driver has seen or not seen could provide indications of how aware a driver is about the environment or their attentiveness. Determining what a driver has seen, i.e., recognized, is challenging, requiring determining whether the individual cognitively identified the object. Studies of human perception, however, have determined that as a precursor to recognition the individual’s gaze must fixate on an object for a period of time, estimated to be 250-500 ms. Determining a driver’s gaze can then be used to determine what objects a driver could have seen and what objects they did not gaze at, i.e., missed or ignored. Gaze, therefore, can provide insight into the driver’s intent or awareness of situations. This work presents a study of what driving relevant objects a driver gazes at during an actual drive. The data consists of a sequence of images from a stereo camera on an instrumented vehicle and a measurement of the driver’s point of gaze (PoG) for each frame. The analysis relies on object recognition of typical objects, such as traffic lights, vehicles, and traffic signs, and determining the driver’s PoG on a frame-by-frame basis. This enables a multiframe analysis to determine the length of time that a driver’s gaze fell on a particular object. The computations rely on two thresholds, one for determining whether two objects from consecutive frames can be considered to be the same object and another for the minimum number of consecutive frames. Different values for these thresholds are explored in the analysis. The methods presented can be useful in other scenarios involving the analysis of driver gaze and have implications for the design of future advanced driving assistance systems and for understanding of driver gaze and awareness.

Abstract

A driver’s actions and intent can be important factors in enabling advance driver assistance systems to assist drivers and avoid accidents. Having some knowledge of what traffic relevant objects the driver has seen or not seen could provide indications of how aware a driver is about the environment or their attentiveness. Determining what a driver has seen, i.e., recognized, is challenging, requiring determining whether the individual cognitively identified the object. Studies of human perception, however, have determined that as a precursor to recognition the individual’s gaze must fixate on an object for a period of time, estimated to be 250-500 ms. Determining a driver’s gaze can then be used to determine what objects a driver could have seen and what objects they did not gaze at, i.e., missed or ignored. Gaze, therefore, can provide insight into the driver’s intent or awareness of situations. This work presents a study of what driving relevant objects a driver gazes at during an actual drive. The data consists of a sequence of images from a stereo camera on an instrumented vehicle and a measurement of the driver’s point of gaze (PoG) for each frame. The analysis relies on object recognition of typical objects, such as traffic lights, vehicles, and traffic signs, and determining the driver’s PoG on a frame-by-frame basis. This enables a multiframe analysis to determine the length of time that a driver’s gaze fell on a particular object. The computations rely on two thresholds, one for determining whether two objects from consecutive frames can be considered to be the same object and another for the minimum number of consecutive frames. Different values for these thresholds are explored in the analysis. The methods presented can be useful in other scenarios involving the analysis of driver gaze and have implications for the design of future advanced driving assistance systems and for understanding of driver gaze and awareness.

Chapters in this book

  1. Frontmatter I
  2. Preface V
  3. Contents VII
  4. Section: Image processing
  5. Chapter 1 Magnetic resonance image re-parameterization on real data 1
  6. Chapter 2 Denoising and gradient fusion for effective edge detection for noisy color images 17
  7. Chapter 3 Understanding driver attention to objects for ADASs: what do drivers see? 39
  8. Chapter 4 Image clustering enhanced with refined image classification 59
  9. Chapter 5 AI-powered framework for objective scoring of product design innovation 89
  10. Section: Computer vision
  11. Chapter 6 Image inpainting using GAN transformerbased model 111
  12. Chapter 7 Enhanced image watermarking through cross-attention and noise-invariant domain learning 127
  13. Chapter 8 Online melt pool monitoring using a deep transformer image processing solution 153
  14. Chapter 9 Implementation of deep learning techniques on thermal image classification 173
  15. Chapter 10 Drishti: a generative AI-based application for gesture recognition and execution 203
  16. Section: Pattern recognition
  17. Chapter 11 Exploring muzzle biometrics: a deep learning framework for noninvasive cattle recognition 239
  18. Chapter 12 Utilizing real-world data to develop a userindependent sensor-based human activity recognition system 253
  19. Index 273
https://doi.org/10.1515/9783111436425-003

Chapters in this book

  1. Frontmatter I
  2. Preface V
  3. Contents VII
  4. Section: Image processing
  5. Chapter 1 Magnetic resonance image re-parameterization on real data 1
  6. Chapter 2 Denoising and gradient fusion for effective edge detection for noisy color images 17
  7. Chapter 3 Understanding driver attention to objects for ADASs: what do drivers see? 39
  8. Chapter 4 Image clustering enhanced with refined image classification 59
  9. Chapter 5 AI-powered framework for objective scoring of product design innovation 89
  10. Section: Computer vision
  11. Chapter 6 Image inpainting using GAN transformerbased model 111
  12. Chapter 7 Enhanced image watermarking through cross-attention and noise-invariant domain learning 127
  13. Chapter 8 Online melt pool monitoring using a deep transformer image processing solution 153
  14. Chapter 9 Implementation of deep learning techniques on thermal image classification 173
  15. Chapter 10 Drishti: a generative AI-based application for gesture recognition and execution 203
  16. Section: Pattern recognition
  17. Chapter 11 Exploring muzzle biometrics: a deep learning framework for noninvasive cattle recognition 239
  18. Chapter 12 Utilizing real-world data to develop a userindependent sensor-based human activity recognition system 253
  19. Index 273
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