Title: Learning to drive the human way: a step towards intelligent vehicles

Authors: Michel Pasquier, Richard J. Oentaryo

Addresses: Centre for Computational Intelligence, Nanyang Technological University, Nanyang Avenue, 639798, Singapore. ' Centre for Computational Intelligence, Nanyang Technological University, Nanyang Avenue, 639798, Singapore

Abstract: This paper describes a series of works on the development of an intelligent driving system that will learn from example. In this approach, inspired from the control mechanisms in the human cerebellum, driving skills are modelled as continuous decision-making processes using approximate rules that map sensory input onto control output. Since designing such a rule set is difficult, we aim at capturing human expertise by automatically extracting the rules from the sample data. A driving simulator provides both scenarios and data collection features while a learning system comprising several self-organising neuro-fuzzy rule-based subsystems realises the driving model. Driving skills successfully achieved so far include operational manoeuvres such as reverse/parallel parking and U-turn, validated both in simulation and using a microprocessor-controlled model car, as well as lane-following and lane-changing. Also discussed is the emergence of tactical driving skills, such as deciding when to overtake, to automatically handle various traffic situations.

Keywords: autonomous driving; brain-inspired cognitive architecture; neuro-fuzzy systems; self-organising neural networks; fuzzy neural networks; intelligent transportation systems; parallel parking; reverse parking; tactical driving; U-turns; intelligent vehicles; intelligent driving systems; learn by example; human expertise; rule extraction; driving skills; lane following; lane changing; tactical driving; driving automation.

DOI: 10.1504/IJVAS.2008.016477

International Journal of Vehicle Autonomous Systems, 2008 Vol.6 No.1/2, pp.24 - 47

Published online: 31 Dec 2007 *

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