JACIII Vol.24 No.3 pp. 368-376
doi: 10.20965/jaciii.2020.p0368


Modular Neural Network for Learning Visual Features, Routes, and Operation Through Human Driving Data Toward Automatic Driving System

Shun Otsubo*, Yasutake Takahashi*, and Masaki Haruna**

*Graduate School of Engineering, University of Fukui
3-9-1 Bunkyo, Fukui, Fukui 910-8507, Japan

**Advanced Technology R&D Center, Mitsubishi Electric Corporation
8-1-1 Tsukaguchi-honmachi, Amagasaki, Hyogo 661-8661, Japan

October 11, 2019
January 30, 2020
May 20, 2020
deep learning, automatic driving, modular neural networks, supervised learning
Modular Neural Network for Learning Visual Features, Routes, and Operation Through Human Driving Data Toward Automatic Driving System

Modular neural network for automatic driving

This paper proposes an automatic driving system based on a combination of modular neural networks processing human driving data. Research on automatic driving vehicles has been actively conducted in recent years. Machine learning techniques are often utilized to realize an automatic driving system capable of imitating human driving operations. Almost all of them adopt a large monolithic learning module, as typified by deep learning. However, it is inefficient to use a monolithic deep learning module to learn human driving operations (accelerating, braking, and steering) using the visual information obtained from a human driving a vehicle. We propose combining a series of modular neural networks that independently learn visual feature quantities, routes, and driving maneuvers from human driving data, thereby imitating human driving operations and efficiently learning a plurality of routes. This paper demonstrates the effectiveness of the proposed method through experiments using a small vehicle.

Cite this article as:
S. Otsubo, Y. Takahashi, and M. Haruna, “Modular Neural Network for Learning Visual Features, Routes, and Operation Through Human Driving Data Toward Automatic Driving System,” J. Adv. Comput. Intell. Intell. Inform., Vol.24, No.3, pp. 368-376, 2020.
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Last updated on Jul. 04, 2020