An effective lane changing behaviour prediction model using optimized CNN and game theory

Accurately predicting lane changes, a crucial driving activity for preventing accidents and ensuring driver safety, is addressed in this study. An innovative predictive model that integrates game theory for precise lane change intention detection and an optimized Convolutional Neural Network (CNN) f...

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Bibliographic Details
Published in:Automatika 2024-07, Vol.65 (3), p.982-996
Main Authors: Prakash, D., Sathiyasekar, K.
Format: Article
Language:English
Subjects:
Accident prediction
Accident prevention
Accuracy
Algorithms
Artificial neural networks
CFCM
Communication
Data processing
Decision making
Efficiency
Engineering
Game theory
Heuristic methods
lane change prediction
Lane changing
Neural networks
Optimization
Optimization algorithms
Prediction models
Traffic accidents & safety
Vehicle safety
Vehicles
weiner filter
Wiener filtering
WOA-CNN
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Summary:Accurately predicting lane changes, a crucial driving activity for preventing accidents and ensuring driver safety, is addressed in this study. An innovative predictive model that integrates game theory for precise lane change intention detection and an optimized Convolutional Neural Network (CNN) for trajectory prediction is proposed in this study. The CNN's efficiency is enhanced through metaheuristic optimization of both the convolution and fully connected layers using the Whale Optimization Algorithm (WOA). Emphasizing robust data processing, a Wiener filter is applied for pre-processing, and the Cascaded Fuzzy C means (CFCM) technique is employed for segmentation. The resulting Whale Optimization Algorithm-based CNN (WOA-CNN) effectively forecasts the trajectory of lane-changing vehicles. Validation of the proposed approach in Python demonstrates exceptional accuracy, reaching 96.5%. This study showcases the effectiveness of the WOA-CNN model in advancing the prediction accuracy of lane-changing behaviour, contributing to enhanced driver safety and accident prevention.
ISSN:0005-1144
1848-3380
DOI:10.1080/00051144.2024.2327907