Original scientific paper
https://doi.org/10.1080/00051144.2024.2327907
An effective lane changing behaviour prediction model using optimized CNN and game theory
D. Prakash
; Department of Electronics and Communication Engineering, S.A. Engineering College, Thiruverkadu, India
*
K. Sathiyasekar
; Department of Electrical and Electronics Engineering,KSRInstitute for Engineering and Technology, Tiruchengode, India
* Corresponding author.
Abstract
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.
Keywords
WOA-CNN; CFCM; weiner filter; game theory; lane change prediction
Hrčak ID:
326217
URI
Publication date:
15.3.2024.
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