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

Motion Planning, as a fundamental technology of automatic navigation for autonomous vehicle, is still an open challenging issue in 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 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 autonomous vehicle.