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Optical based sensor systems for vehicle based detection and warning systems are under development to reduce accidents and limit injuries caused by accidents. (1, 2, 3) In order to validate these types of detection systems, it is necessary to perform real world tests. In the case of pedestrian detection systems, this is very difficult in the field for safety reasons. Instead, simulated tests are more desirable. This paper describes work to understand the effectiveness of using virtual pedestrians as surrogates for real world pedestrian detection.

Recent trends show a growing demand for improved powertrain NVH and sound quality. In particular, there is little customer acceptance of tonal annoyances under any driving condition. Thus, powertrain NVH and product development engineers have a strong need to confidently determine acceptable noise levels for commodities that produce narrow band noise. Components such as power steering, transmission gears, pumps, engine timing chains, axle gearing, etc., all may produce significant tones under various vehicle conditions. The perception of the tone is highly influenced by its frequency and background noise. Background noise is composed of wind, road, and engine noise. A methodology and toolset of masking perception algorithms has been developed to meet these needs. The Masking Perception Analysis Software (MPAS) is used to address the development and verification of acceptable powertrain tonal levels as well as the diagnosis of tonal-related issues.

Customer annoyance of steady-state wind noise correlates well with loudness. A common objective metric to capture average loudness is the ISO532B or Zwicker method. However, it has been shown previously that time-varying wind noise can also significantly affect customer annoyance, independent of average loudness. Causes of time-varying wind noise include wind buffeting generated by other vehicles, and also wind gusting. This paper summarizes the development of an objective metric that correlates well with subjective impressions of wind gusting/buffeting. The model is based on a general impulsive noise model with parameters tuned specifically for time-varying wind characteristics. The model consists of a psychoacoustic processing stage followed by a gusting detection stage, where the psychoacoustic stage is extracted from a time-varying loudness model. The output of the gusting model is a time series that indicates the location and “intensity” of wind gusts.