When further demands are placed on emissions and performance of cars, trucks and busses, the vehicle manufacturers are looking to have cheap ways to evaluate their products for specific customers' needs. Using simulation tools to quickly compare use cases instead of manually recording data is a possible way forward. However, existing traffic simulation tools do not provide enough detail in each vehicle for the driving to represent real life driving patterns with regards to road features. For the purpose of this thesis data has been recorded by having different people drive a specific route featuring highway driving, traffic lights and many curves. Using this data, models have then been estimated that describe how human drivers adjust their speed through curves, how long braking distances typically are with respect to the driving speed, and the varying deceleration during braking sequences. An additional model has also been created that produces a speed variation when driving on highways. In the end all models are implemented in Matlab using a traffic control interface to interact with the traffic simulation tool SUMO. The results of this work are promising with the improved simulation being able to replicate the most significant characteristics seen from human drivers when approaching curves, traffic lights and intersections.

Modern cars have a multitude of driver assistance functions that aim to support the driver in his/her everyday driving. One part of this is the Adaptive Cruise Control (ACC) that aims to keep a driver-specified speed. However, this set speed might be perceived as too high for some curves and as a result the driver will interrupt the system and take control again. To avoid this, a Curve Speed Adaptation (CSA) system aims to adapt the speed for an upcoming curve. Such a system should aim to mimic a driver and take into consideration how the driver would behave if he/she were to drive. This work aims to find a set of so-called Driving Modes that can describe how drivers with different driving styles drive through curves with different road properties by analysing recorded manual driving. A nested clustering approach is tested to divide curves into groups based on the driving style they were driven at and their road properties. The results show that this approach is able to capture different driving behaviours through curves. The road type and speed limit of a curve seem thereby to have the main influence on the driving behaviour. Clustering curves first by their driving style followed by the road properties yields thereby the more distinguishable Driving Modes. However, further improvements of the clustering methods are necessary to improve the obtained Driving Modes. The results of this thesis can form the basis for the development of a Curve Speed Adaptation system that adjusts for both the individual driver as well as particular road properties to improve the driver's comfort. / Moderna bilar har en mängd förarstödsystemer som syftar till att stödja föraren i sin dagligakörning. En av dem är Adaptive Cruise Control (ACC) som syftar till att hålla en hastighetspecificerad av föraren. Men denna hastighet kan uppfattas som för hög för vissa kurvoroch som resultat tar föraren kontrollen igen själv. För att undvika detta ska ett Curve SpeedAdaptation (CSA) system anpassa hastigheten för en kommande kurva. Ett sådant systembör sträva efter att efterlikna en förare och ta hänsyn till hur föraren skulle köra själv. Dethär examensarbetet syftar till att hitta så kallade körlägen som kan beskriva hur förare medolika körstilar kör genom kurvor med olika omständigheter genom att analysera manuellakörningar. En nestad klustringsmetod testas för att dela upp kurvor i grupper baserat påkörstilen som de kördes på och deras vägegenskaper. Resultaten visar att denna metod kanfånga olika körningsbeteenden genom kurvor. Vägtypen och hastighetsbegränsningen fören kurva verkar därmed ha huvudinverkan på körbeteendet. Att dela kurvorna först efterderas körstil följt av vägegenskaper ger bättre körlägena. Men ytterligare förbättringar avklustringsmetoderna är nödvändig för att förbättra de erhållna körningsmetoderna. Resul-taten av detta examensarbetet kan utgöra grunden för utvecklingen av ett kurvhastighetsanpassningssystem som anpassar både för den enskilda föraren och speciella vägegenskaperför att förbättra förarens komfort.