Low Memory Footprint CNN Models for end-to-end Driving of Autonomous Ground Vehicle and Custom Adaptation to Various Road Conditions.
Bishwajit Pal1, Samitha Khaiyum2

1Bishwajit Pal, Department of MCA, Dayananda Sagar College Of Engineering, Bangalore, India.
2Dr. Samitha Khaiyum, Department of MCA, Dayananda Sagar College Of Engineering, Bangalore, India.

Manuscript received on October 12, 2019. | Revised Manuscript received on 22 October, 2019. | Manuscript published on November 10, 2019. | PP: 3252-3259 | Volume-9 Issue-1, November 2019. | Retrieval Number: A9165119119/2019©BEIESP | DOI: 10.35940/ijitee.A9165.119119
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: The Idea presented in this paper is to establish that different types of Convolution Neural Network (CNN) model are required to be used for different types of terrains, to optimally drive the vehicle. The Layers and the trainable variables required to be configured as per the road conditions. For simpler roads, lighter and simpler networks and for complicated road, a CNN model with heavier network is required. Existing CNN models were used to build the performance baseline with respect to simple and complicated road conditions. In the proposed CNN models, layers and trainable parameters are adjusted according to the road conditions. The objective of the proposed solution is to successfully drive with minimum number of convolution layers and trainable variables, fit for deployment on computer consuming less than 100 watts, without GPU, for moderate speed autonomous vehicle. These new sets of proposed CNN models are either equal or smaller in network size and trainable parameters, memory footprint and refresh rate compared to Alexnet and Nvidia model. The overall requirement of computational power, cost, and size is also reduced. In this paper, we recommend designing AV software with multiple CNN models to address different road conditions instead of on one Model for all road conditions. Finally, we establish that when the terrain is more complex and requires more features to be extracted then the CNN layers need to be tweaked to extract more features and the speed of the vehicle needs to be reduced.
Keywords: Autonomous Driving, Camera, Convolution Neural Network, Deep Neural Network, Machine Learning.
Scope of the Article: Machine Learning