In English

Autonomous vehicle control: Exploring driver modelling conventions by implementation of neuroevolutionary and knowledge-based algorithms

Linus Arnö ; Jonas Eriksson
Göteborg : Chalmers tekniska högskola, 2018. Master's thesis - Department of Mechanics and Maritime Sciences; 2018:83, 2018.
[Examensarbete på avancerad nivå]

In this paper an investigation of driver modelling conventions is presented. The goal was to compare traditional driver modelling with machine learning, to nd indications of when one approach could be preferred over the other. This was done by implementing some representatives of the di erent approaches and evaluating them in the same conditions. The traditional approach was represented with one well established model by Sharp et al., as well as one self made aim point model. Both of these required a path planner and velocity control, that were also designed by the authors themselves. The machine learning approach was represented by neuroevolution, an alternative technique for solving reinforcement learning problems, and speci cally the method called NEAT. The results showed that all implemented methods were able to solve the task, but in the speci c scenario and with the current amount of training the two traditional models were superior to the evolved neural network. Similarities and potential reasons for di erences between the models are discussed, as well as some identi ed advantages and disadvantages to both approaches.

Nyckelord: Autonomous control, driver modelling, motion planning, neural networks, neuroevolution



Publikationen registrerades 2018-11-08.

CPL ID: 256268

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