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This study presents a robust model predictive control (RMPC) framework integrated with deep neural networks, specifically a long short-term memory layer (DNN-LSTM), to enhance the safety and stability of autonomous vehicles in collision avoidance under varying road conditions. The proposed approach effectively captures temporal dependencies and significantly reduces computational complexity compared to traditional RMPC methods, enabling real-time implementation. Simulation results demonstrate that the DNN-L

Author

tiendaica

Language

EN