The content discusses the importance of autonomous driving technology in enhancing traffic safety, reducing accidents, improving traffic flow, and increasing travel efficiency. It emphasizes the need for predicting surrounding vehicle trajectories for safe decision-making. The proposed deep learning model based on LSTM shows significant advantages over traditional methods in long-term trajectory prediction and intention recognition. By considering safety and efficiency, the model aims to improve lane change processes in autonomous vehicles.
The content also delves into the structure of LSTM neural networks for lane change planning, collision avoidance algorithms, and verification results of the LSTM prediction model. Experimental scenarios demonstrate the effectiveness of the proposed algorithm in ensuring driving safety and efficiency during active lane changes and emergency braking situations. Comparative analyses highlight the superiority of using LSTM-MPC algorithms for path planning and tracking in autonomous vehicles.
Overall, the study presents an improved approach to lane change trajectory planning using deep learning techniques to enhance safety measures and efficiency in autonomous driving systems.
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by Wenjian Sun,... at arxiv.org 03-13-2024
https://arxiv.org/pdf/2403.06993.pdfDeeper Inquiries