Detecting Driver Distraction Using Deep-Learning Approach
Source abstract
Abstract
Currently, distracted driving is among the most important causes of traffic accidents. Consequently, intelligent vehicle driving systems have become increasingly important. Recently, interest in driver-assistance systems that... | Find, read and cite all the research you need on Tech Science Press
Source: openalex_inverted_index · Quality: baseline
Research context
OpenAlex topics: Human-Automation Interaction and Safety · Autonomous Vehicle Technology and Safety · Traffic Prediction and Management Techniques
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (46)
Show source-provided references
1. WHO, “Global Status Report on Road Safety 2018: Summary,” 2018.
2. Modeling traffic accidents in Saudi Arabia using classification techniques
3. Preventing distracted driving: A program from initiation through to evaluation10.1016/j.amjsurg.2019.07.043 ↗
4. Administrator national highway traffic safety administrationitle
5. Driving to safety
6. Traffic safety facts 2015: A compilation of motor vehicle crash data from the fatality analysis reporting system and the general estimates system
7. The impact of hand-held and hands-free cell phone use on driving performance and safety-critical event risk
8. DarNet: A deep learning solution for distracted driving detection
9. Driver gaze tracking and eyes off the road detection system10.1109/TITS.2015.2396031 ↗
10. Real-time eye, gaze, and face pose tracking for monitoring driver vigilance10.1006/rtim.2002.0279 ↗
11. Crash dieting: The effects of eating and drinking on driving performance10.1016/j.aap.2007.04.012 ↗
12. Are child occupants a significant source of driving distraction?10.1016/j.aap.2011.01.005 ↗
13. How many crashes are caused by driver interaction with passengers? A meta-analysis approach10.1016/j.jsr.2018.02.001 ↗
14. Driving distracted with friends: Effect of passengers and driver distraction on young drivers’ behavior10.1016/j.aap.2019.07.022 ↗
15. Detailed analysis of distraction induced by in-vehicle verbal interactions on visual search performance
16. App analytics: Predicting the distraction potential of in-vehicle device applications
17. Effects of advertising billboards during simulated driving10.1016/j.apergo.2010.08.013 ↗
18. Takeover time in highly automated vehicles: Noncritical transitions to and from Manual Control10.1177/0018720816685832 ↗
19. End-to-end deep learning for steering autonomous vehicles considering temporal dependencies
20. Static free space detection with laser scanner using occupancy grid maps
21. Reactive collision avoidance using evolutionary neural networks
22. A pattern recognition system for detecting use of mobile phones while driving
23. Driver cell phone usage detection from HOV/HOT NIR images
24. Driver distraction detection and recognition using RGB-D sensor
25. Visual recognition of driver hand-held cell phone use based on hidden CRF
26. Multiple scale faster-RCNN approach to driver’s cell-Phone usage and hands on steering wheel detection
27. On performance evaluation of driver hand detection algorithms: Challenges, dataset, and metrics
28. Driver cell phone usage detection on Strategic Highway Research Program (SHRP2) face view videos
29. Recognition of driving postures by contourlet transform and random forests10.1049/iet-its.2011.0116 ↗
30. Recognition of driving postures by combined features and random subspace ensemble of multilayer perceptron classifiers10.1007/s00521-012-1057-4 ↗
31. Faster R-CNN: Towards real-time object detection with region proposal networks10.1109/TPAMI.2016.2577031 ↗
32. Head, eye, and hand patterns for driver activity recognition
33. Driving posture recognition by convolutional neural networks10.1049/iet-cvi.2015.0175 ↗
34. Background subtraction based on Gaussian mixture models using color and depth information
35. ImageNet classification with deep convolutional neural networks10.1145/3065386 ↗
36. Safe driving: Driver action recognition using SURF keypoints
37. Detecting distracted driving with deep learning
38. State Farm Distracted Driver Detection · 2016
39. Driver distraction identification with an ensemble of convolutional neural networks10.1155/2019/4125865 ↗
40. Detection of distracted driver using convolutional neural network
41. Context-driven multi-stream LSTM (M-LSTM) for recognizing fine-grained activity of drivers
42. CNN features off-the-shelf: An astounding baseline for recognition
43. Very deep convolutional networks for large-scale image recognition
44. A robust 2D-Cochlear transform-based palmprint recognition10.1007/s00500-019-04062-8 ↗
45. 3D Face reconstruction from single 2D image using distinctive features10.1109/ACCESS.2020.3028106 ↗
46. Towards computationally efficient and realtime distracted driver detection with MobileVGG network