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

Date

Type

Examensarbete för masterexamen
Master Thesis

Model builders

Journal Title

Journal ISSN

Volume Title

Publisher

Abstract

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.

Description

Keywords

Robotteknik och automation, Farkostteknik, Informations- och kommunikationsteknik, Transport, Robotics, Vehicle Engineering, Information & Communication Technology, Transport

Citation

Architect

Location

Type of building

Build Year

Model type

Scale

Material / technology

Index

Collections

Endorsement

Review

Supplemented By

Referenced By