Control Strategy for VANET Autonomous Driving Vehicles in Emergency Situations Based on Deep Learning

VANETs are essential for communication and coordination between autonomous vehicles, particularly in emergency scenarios where quick decisions are necessary. The proposed Deep Learning‐based Control Approach for Autonomous Vehicles (DL‐CA‐AV) introduces a hybrid DL control framework that integrates Convolutional Neural Networks (CNNs) and Long Short‐Term Memory (LSTM) with an attention‐driven decision‐fusion mechanism for real‐time maneuver control in VANET‐enabled environments. The CNN module extracts spatial features from sensor and VANET communication data, while the LSTM network models temporal dependencies to predict dynamic vehicular states across time. These learned spatiotemporal representations are then passed to a reinforcement learning (RL) layer, where the actor–critic mechanism evaluates potential maneuvers and selects optimal control actions based on collision probability and situational awareness. The proposed approach encourages vehicles to autonomously choose the best emergency response (lane change, deceleration, or cooperative braking) while preserving stability and minimizing secondary risks. This study demonstrates the capability of DL‐based VANET architectures to enable real‐time autonomous driving control in hazardous environments, facilitating safer and more dependable intelligent mobility. The proposed framework achieves a collision probability of 29%, a latency below 225 ms, a detection accuracy above 85%, and a packet delivery ratio above 88%.