期刊
出版社
WORLD SCIENTIFIC PUBL CO PTE LTD
DOI: 10.1142/S0218001418500258
关键词
Self-driving; convolutional neural network; MobileNets; BatchNormalization; Swish; steering angle
资金
- National Natural Science Foundation of China [61473078]
- Program for Changjiang Scholars from the Ministry of Education
- International Collaborative Project of the Shanghai Committee of Science and Technology [16510711100]
- Innovation Program of Shanghai Municipal Education Commission [14ZZ067]
Today, in the construction of smart city, the development of self-driving technology plays the key role. The explosion of convolutional neural network (CNN) technology has made it possible to utilize end-to-end tasks with images. However, today's CNN has deeper, more accurate characteristics. If we do not improve the calculation method to reduce the number of network parameters, this feature makes it very difficult for us to run neural network computing in small devices. In this paper, we further optimize the network computing methods based on Mobile-Nets to reduce number of network parameters. At the same time, in the network structure, we add BatchNormalization and Swish activation function. We designed our own network in the end-to-end prediction for steering angle in the self-driving car task. From the final simulation results, our neural network's storage space can be reduced and the execution speed of neural network can be improved while maintaining the accuracy of the neural network.
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