Deep Learning Based Motion Planning For Autonomous Vehicle Using Spatiotemporal LSTM Network

2019/03/05 by Zhengwei Bai, Baigen Cai, Bai, Zhengwei +5 · 2 citations
Computer Science · Engineering · #Advanced Neural Network Applications #Advanced Vision and Imaging #Artificial Intelligence (cs.AI) #Autonomous Vehicle Technology and Safety #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #cs.AI #cs.CV

paper · pdf · doi:10.48550/arxiv.1903.01712

5 pages, 8 figures, Accepted to 2018 Chinese Automation Congress (CAC)

arxiv created 2019/03/05 · openalex publication_date 2019/03/05 · arxiv updated 2019/03/06 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

Motion Planning, as a fundamental technology of automatic navigation for the autonomous vehicle, is still an open challenging issue in the real-life traffic situation and is mostly applied by the model-based approaches. However, due to the complexity of the traffic situations and the uncertainty of the edge cases, it is hard to devise a general motion planning system for the autonomous vehicle. In this paper, we proposed a motion planning model based on deep learning (named as spatiotemporal LSTM network), which is able to generate a real-time reflection based on spatiotemporal information extraction. To be specific, the model based on spatiotemporal LSTM network has three main structure. Firstly, the Convolutional Long-short Term Memory (Conv-LSTM) is used to extract hidden features through sequential image data. Then, the 3D Convolutional Neural Network(3D-CNN) is applied to extract the spatiotemporal information from the multi-frame feature information. Finally, the fully connected neural networks are used to construct a control model for autonomous vehicle steering angle. The experiments demonstrated that the proposed method can generate a robust and accurate visual motion planning results for the autonomous vehicle.

Citations

• Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting
• Deep Learning in Neural Networks: An Overview
• End to End Learning for Self-Driving Cars

Cited by

• Real-Time Obstacle Avoidance for a Mobile Robot Using CNN-Based Sensor Fusion
• MIND-Stack: Modular, Interpretable, End-to-End Differentiability for Autonomous Navigation

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