Learning to drive the human way: a step towards intelligent vehicles
Michel Pasquier, Richard J. Oentaryo · International Journal of Vehicle Autonomous Systems · 2008
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.