Automatic Driving Decision Algorithm Based on Multi-dimensional Deep Space-Time Network - Intelligent Information Processing IX Access content directly
Conference Papers Year : 2018

Automatic Driving Decision Algorithm Based on Multi-dimensional Deep Space-Time Network

Abstract

A model of autopilot decision algorithm based on multidimensional depth space-time network was studied in this paper. The forward images of vehicle driving was taken by the camera mounted on the vehicle. The images and the steering wheel angle and speed were collected as the model training input data. The multi frame vehicle image was pre-processed, the underlying feature image and the original image were used as the input of the multi-dimensional space-time decision network. The multi-dimensional space-time decision network was set up. The multiple three-dimensional convolution paths were used to extract and fuse the high level spatiotemporal features of the original and the underlying features, and the fusion features were used. In the decision of autopilot. The multidimensional spatiotemporal network was trained by using the driver’s driving data, and the multidimensional spatiotemporal decision-making model was obtained. The decision model of the autopilot makes use of multidimensional space-time information to directly output the decision information of autopilot. The model can effectively output the driver’s decision data.
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hal-02197802 , version 1 (30-07-2019)

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Jianguo Zhang, Jianghua Yuan, Hanzhong Pan, Qing Ma, Yong Yu. Automatic Driving Decision Algorithm Based on Multi-dimensional Deep Space-Time Network. 10th International Conference on Intelligent Information Processing (IIP), Oct 2018, Nanning, China. pp.71-79, ⟨10.1007/978-3-030-00828-4_8⟩. ⟨hal-02197802⟩
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