LGCVAug 25, 2023

Using Visual and Vehicular Sensors for Driver Behavior Analysis: A Survey

arXiv:2308.13406v16 citationsh-index: 3
Originality Synthesis-oriented
AI Analysis

It addresses the problem of risky driving, which accounts for 70% of fatal accidents in the U.S., by surveying existing methods for driver behavior analysis.

This survey examines techniques for analyzing driver behavior using visual and vehicular data to improve road safety, concluding that integrating these data sources can enhance accuracy and effectiveness, potentially reducing traffic accidents.

Risky drivers account for 70% of fatal accidents in the United States. With recent advances in sensors and intelligent vehicular systems, there has been significant research on assessing driver behavior to improve driving experiences and road safety. This paper examines the various techniques used to analyze driver behavior using visual and vehicular data, providing an overview of the latest research in this field. The paper also discusses the challenges and open problems in the field and offers potential recommendations for future research. The survey concludes that integrating vision and vehicular information can significantly enhance the accuracy and effectiveness of driver behavior analysis, leading to improved safety measures and reduced traffic accidents.

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