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Deep learning based predictive models for real time accident prevention in autonomous vehicle networks
Ahmed Almutairi1, Abdullah Faiz Al Asmari2, Fayez Alanazi3
1Department of Civil and Environmental Engineering, College of Engineering, Majmaah University, 11952, Majmaah, Saudi Arabia. a.alaoni@mu.edu.sa.
Scientific Reports
|July 2, 2025
Summary
This study introduces A-LAPPM, an autonomous vehicle safety model, to predict and prevent road accidents. The model significantly improves prediction accuracy and response time, reducing accident rates in complex driving scenarios.
Area of Science:
- Artificial Intelligence
- Autonomous Vehicle Technology
- Road Safety Engineering
Background:
- Increasing traffic volume poses significant road safety challenges.
- Sophisticated accident prediction and prevention technologies are crucial.
- Autonomous vehicle (AV) networks offer real-time collision avoidance capabilities.
Purpose of the Study:
- To present an innovative accident prediction and prevention model for autonomous vehicle networks.
- To enhance road safety by identifying and responding to potential accident hazards in real-time.
Main Methods:
- Developed the Attention-based Long- and Short-Term Memory Autoencoder (A-LAPPM) model.
- Integrated data from vehicle sensors, Vehicle-to-Vehicle (V2V) communication, and ambient variables.
- Utilized Long- and Short-Term Memory (LSTM) units for sequential learning and an attention mechanism for enhanced focus.
Main Results:
- Achieved approximately 11.8% greater prediction accuracy.
- Demonstrated a 28.5% faster response time.
- Reduced accident rates by 50% in complex driving scenarios.
Conclusions:
- The A-LAPPM model effectively predicts and prevents accidents in autonomous vehicle networks.
- The model enhances overall autonomous vehicle performance and road safety.
- Experimental results validate the model's accuracy, speed, efficiency, and resilience.
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