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Recognition of aggressive driving behavior under abnormal weather based on Convolutional Neural Network and transfer
Ziyu Zhang1,2, Shuyan Chen1,2, Hong Yao1,2
1School of Transportation, Southeast University, Nanjing, China.
Traffic Injury Prevention
|July 24, 2024
Summary
Transfer learning improves aggressive driving recognition in abnormal weather. Models trained on normal conditions achieved 0.81 accuracy in fog and rain, outperforming non-transferred models.
Area of Science:
- Traffic safety and artificial intelligence.
- Machine learning applications in transportation.
Background:
- Aggressive driving increases collision risks, especially during abnormal weather.
- Collecting sufficient driving data in adverse weather is challenging.
Purpose of the Study:
- To develop robust aggressive driving recognition models for diverse climate conditions.
- To address data scarcity issues in abnormal weather scenarios using transfer learning.
Main Methods:
- Utilized a driving simulator to collect data under normal and abnormal weather.
- Employed K-means clustering to categorize driving behaviors (aggressive, normal, cautious).
- Trained a Convolutional Neural Network (CNN) model on normal weather data and fine-tuned it for foggy and rainy conditions via transfer learning.
Main Results:
- Transferred CNN models achieved 0.81 accuracy in foggy and rainy conditions.
- This significantly outperformed non-transferred models (0.72 in fog, 0.69 in rain).
Conclusions:
- Transfer learning offers significant value for recognizing aggressive driving with limited data.
- This approach is feasible for tackling driving behavior recognition challenges in adverse weather conditions.
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