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Recent Advances in Vision-Based On-Road Behaviors Understanding: A Critical Survey.
Rim Trabelsi1, Redouane Khemmar1, Benoit Decoux1
1Normandie University, UNIROUEN, ESIGELEC, IRSEEM, 76000 Rouen, France.
Sensors (Basel, Switzerland)
|April 12, 2022
Summary
This review synthesizes over two decades of research on on-road behavior analysis for autonomous driving. It highlights deep learning
Area of Science:
- Computer Vision
- Machine Learning
- Deep Learning
- Autonomous Driving
Background:
- On-road behavior analysis is critical for autonomous driving safety.
- Advancements in AI have spurred significant progress in understanding road user behavior.
- Existing research often focuses on isolated tasks, necessitating a holistic approach.
Purpose of the Study:
- To provide a comprehensive review of on-road behavior analysis research spanning the last two decades.
- To highlight the potential impact of road behavior understanding on smart mobility and road safety.
- To identify research gaps and suggest future directions in the field.
Main Methods:
- Systematic literature review of over 100 papers on on-road behavior analysis.
- Categorization of research into key components: situational awareness, driver-road interaction, road scene understanding, trajectory forecasting, and driving activity/status analysis.
- Analysis of deep learning contributions and recent benchmarks.
Main Results:
- Identified key milestones and advancements in road behavior understanding over 20 years.
- Established complementary relationships between different behavioral analysis tasks.
- Provided a taxonomy of deep learning architectures for behavior analysis.
Conclusions:
- A holistic understanding of road behavior is essential for enhancing road safety for all agents.
- Deep learning methods have shown significant promise in various aspects of behavior analysis.
- Novel research directions are identified, with some already validated in current smart mobility research.
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