Neural Network Approaches for Lateral Control of Autonomous Highway Vehicles 912871
The research reported in this paper focuses on the automated steering aspects of intelligent highway vehicles. Proposed is a machine vision system for capturing driver views of the on-coming highway environment. The objective is to investigate various designs of artificial neural networks for processing the resulting images and generating acceptable steering commands for the vehicle. The research effort has involved the construction of a computer graphical simulation system, called the Road Machine, which is used as the experimental environment for analyzing, through simulation, alternative neural network approaches for controlling autonomous highway vehicles. The Road Machine serves as both the training environment and the experimental testing environment for the autonomous highway vehicle. It is composed of five (5) major modules: Highway design, Driver view simulation, Image processing, Neural network design and training, and Autonomous driving simulation. Two types of neural network control structures are under active research, Back-propagation and Adaptive Resonance. The Road Machine is written in C and operates on Silicon Graphics workstations using Unix and the SGI graphics language.
Citation: Kornhauser, A., "Neural Network Approaches for Lateral Control of Autonomous Highway Vehicles," SAE Technical Paper 912871, 1991, https://doi.org/10.4271/912871. Download Citation
Author(s):
Alain L. Kornhauser
Pages: 9
Event:
Vehicle Navigation & Instrument Systems
ISSN:
0148-7191
e-ISSN:
2688-3627
Also in:
Vehicle Navigation and Information Systems Conference Proceedings-P-253
Related Topics:
Neural networks
Simulators
Autonomous vehicles
Computer simulation
Intelligent transportation Systems
Research and development
Education and training
Simulation and modeling
Vehicle drivers
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