Dynamic Multi-Objective None-Sorted Genetic Algorithm III Deep Long Short-Term Memory Incorporating Dropout for Collision Detection in Internet of Vehicles

Abstract

Abstract. The Internet of Vehicles (IoV) is an emerging field with significant research and commercial potential. A central challenge in IoV is to aggregate the vast volumes of data generated by interconnected vehicles and transform it into actionable knowledge for intelligent decision-making, such as detecting vehicle collisions. While various classifiers have been developed for collision detection, there is a lack of rigorous research on selecting the most impactful features for these detections. Many existing studies rely on single algorithms and simple correlation coefficients for feature identification, which can be a limitation. To address this, this paper proposes a dynamic model that combines a Non-dominated Sorting Genetic Algorithm III (NSGA-III) with a deep Long Short-Term Memory (LSTM) network incorporating dropout to detect vehicle collisions in IoV environments. The model aims to simultaneously minimize the feature subset, reduce computational time, and maximize collision detection accuracy. NSGA-III evolutionary algorithm efficiently explores the feature space to optimize multiple objectives, while the deep LSTM is well-suited for capturing temporal dependencies in the sequential data generated by vehicles. The model was trained and evaluated using a dataset generated in a VISSIM traffic simulation environment, which recreated various urban driving scenarios. The experimental results demonstrate that the NSGA-III + Deep LSTM-RD model significantly outperforms baseline algorithms like deep LSTM, DRNN, GANN, and ANN, especially when using a reduced set of features. The model achieved high accuracy and lower error rates while maintaining fast execution times, highlighting the importance of combining advanced optimization techniques with deep learning for robust and adaptable predictive models.