Standing at the side of a road, one can hear the sound of the cars passing by. In cities, where the speed of the cars barely exceeds 50 km/h, the main noise from the car consists of the tyres, rolling on the road surface. For environmentally conscious cities, it is of interest to reduce that tyre noise. To assist tyre manufacturers in their development of new products, this thesis introduces a tool that creates an acoustic impression on how a newly designed tyre or road surface might sound. For this, no expensive manufacturing of test samples is needed. The sounds can simply be generated based on the design properties of tyre and road.
In one of the investigations, a group of sound examples was generated and used to find out how it is possible to differ between sounds generated by different tyres or road surfaces. The sound can for instance differ in how loud it is, how rough it sounds and how pleasant it sounds. It was investigated which descriptors that can be used to describe and differ between different tyres and road surfaces.
Another aspect that was investigated in this thesis is how different tyres and road surfaces assist us in detecting, or not noticing an approaching car in traffic. Normally, recordings of traffic noise are used for such investigations. The tool presented in this study allows to do these types of investigations much faster and cheaper. Even newly designed tyres and road surfaces can be used. In listening experiments, the reaction time until a pedestrian noticed an approaching car in background traffic noise was measured. With this method, different traffic situations were tested. It was found that distance between test car and background traffic, traffic amount of the background traffic and the speed of both test car and background could strongly affect the reaction time. Further, electric cars were tested against combustion cars. The increase in reaction time for the electric cars was smaller than expected. Additional warning sounds in form of added single tones were able to improve the detection. But this only helped, if they were not present in the background as well. An interesting finding was, that a decrease of 6 dB in the level of the background traffic noise, clearly improved earlier detection. If the background levels were below 50 to 55 dB, test cars were detected sufficiently early in all cases.