Hybrid Deep Learning Optimization for Accurate Trajectory Prediction in Vehicular Networks

Vehicular Ad-Hoc Networks (VANETs) are essential for enhancing traffic management and road safety through vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communication. Accurately predicting vehicle trajectories is critical yet challenging in dynamic traffic conditions. The paper proposes an optimized hybrid deep learning model combining Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks to improve trajectory prediction. Simulation results on real-world datasets (GPS tracks of vehicles on highways and in urban areas) demonstrate higher prediction accuracy and efficiency, making the model highly effective for deployment in real-time smart city systems.