This master’s thesis investigates how recorded driving data from humans can be used for improving the behaviour of the motion controller in an autonomous vehicle. Manual driving behaviour is analysed and the findings are compared to autonomous driving. The results show that human drivers have less abrupt speed control and that humans have a larger position variation between different tests. In some of the situations where the two driving styles differ the human driving behaviour is preferred. One such example is the speed control, especially when driving through curves. As a result, alterations to the speed control are implemented in an autonomous vehicle and evaluated in a vehicle test. The new speed reference is based on the average speed kept by the manual drivers. The results show improvements to the speed and acceleration control. However, further refinements to the controller are required for a less abrupt acceleration. ; Detta examensarbete undersöker hur inspelad data från mänskliga förare kan användas för att förbättra regulatorn i ett autonomt fordon. Mänskligt körbeteende analyseras och jämförs med det autonoma beteendet. Resultaten visar att mänskliga förare har mindre abrupta hastighetsändringar och människors positionering i sidled varierar mer mellan olika testomgångar. I vissa fall där körstilen skiljer sig åt är det mänskliga beteendet att föredra, som till exempel vid hastighetsreglering. Följaktligen implementeras justeringar av hastighetsregulatorn i ett autonomt fordon som sedan kontrolleras i ett fordonstest. Resultaten visar förbättringar gällande regleringen av fart och acceleration. Däremot behövs vidare justeringar av regulatorn för ett mindre ryckigt beteende.
Improving Autonomous Driving Using Human Driver Data
2021-01-01
Theses
Electronic Resource
English
DDC: | 629 |
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