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This paper compares the results from three human factors studies conducted in a motion-based simulator in 2008, 2014 and 2023, to highlight the trends in driver's response to Forward Collision Warning (FCW). The studies were motivated by the goal to develop an effective HMI (Human-Machine Interface) strategy that enables the required driver's response to FCW while minimizing the level of annoyance of the feature. All three studies evaluated driver response to a baseline-FCW and no-FCW conditions. Additionally, the 2023 study included two modified FCW chime variants: a softer FCW chime and a fading FCW chime. Sixteen (16) participants, balanced for gender and age, were tested for each group in all iterations of the studies. The participants drove in a high-fidelity simulator with a visual distraction task (number reading). After driving 15 minutes in a nighttime rural highway environment, a surprise forward collision threat arose during the distraction task.

This paper investigates the effects on response time of a forward collision event in a repeated-measures design. Repeated-measures designs are often used in forward collision warning (FCW) testing despite concerns that the first exposure creates expectancy effects that may dilute or bias future outcomes. For this evaluation, 32 participants were divided into groups of 8 for an AA, BB, AB, BA design (A= No Warning; B=FCW alert). They drove in a high-fidelity simulator with a visual distraction task. After driving 15 min in a nighttime rural highway environment, a forward collision threat arose during the distraction task (Period 1). A second drive was then run and the forward collision threat was repeated again after ∼10 min (Period 2). The response times from these consecutive events were analyzed.

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.

Many engine tick and knock issues are clearly audible, yet cannot be characterized by common sound quality metrics such as time-varying loudness, sharpness, fluctuation strength, or roughness. This paper summarizes the recent development and application of an objective metric that agrees with subjective impressions of impulsive engine noise. The metric is based on a general impulsive noise model [1], consisting of a psychoacoustic processing stage followed by a transient detection stage. The psychoacoustic stage is extracted from portions of a time-varying loudness model. The primary output of the impulsive engine noise model is a time series that indicates the location and “intensity” of impulsive engine noise events. The information in this time series is reduced either to a single number metric, or to a frequency-based vector of numbers that indicates the amount of impulsiveness in the recorded sound.

One of the primary correlates to customer annoyance with door-closing sound is peak loudness. In addition, customer annoyance also increases with the existence of secondary impacts, such as rattles. While these secondary impacts are typically not seen in the time-varying loudness trace (or other common sound quality metrics), it is often possible to visually identify the impacts in a time-frequency display of the door-closing sound. But the reduction of this display information to a single-number objective metric that agrees with subjective assessments has previously proved elusive. This paper summarizes the recent development and application of an objective metric that agrees with subjective classifications of secondary impacts in door-closing sounds.

A wavelet-based technique for reducing the impulsive character of sound recordings is presented. The amount of impulsive content removed may be adjusted by varying a statistical threshold. The technique is validated for a diesel idle sound-quality application. The wavelet-based modification produces a substantial decrease in impulsive character as verified by an objective sound-quality metric for engine “ticking”. Informal subjective assessment of the modified results found them to be realistic and free from artifacts. The procedure is expected to be useful for sound-quality simulation and target-setting for diesel powertrain noise and other automotive sounds containing both impulsive and non-impulsive content.

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.

Impact harshness characterizes interior sound and vibration resulting from tire interactions with discrete road disturbances. Typical interactions are expansion joints, railroad crossings, and other road discontinuities at low-to-medium vehicle speeds. One goal of the current study was to validate for light trucks and SUVs the metric that was developed for cars: a weighted combination of peak loudness values from the front and rear impacts after lowpass filtering at 1 kHz. Another goal was to see if other sound characteristics of impact harshness needed to be captured with a metric. A listening study was conducted with participants evaluating several different trucks and SUVs for impact harshness. Results show that the existing metric correlates well with subjective preferences for most of the vehicles.

In assessing the sound quality of electric motors (e.g., seat, mirror, and adjustable pedal motors), the sensation of Fluctuation Strength - a measure of intensity or frequency variation - has become important. For electric motors, it is typically caused by variation in the load, creating frequency modulation in the sound. An existing method for calculating Fluctuation Strength proved useful initially, but more extensive testing identified unacceptable performance. There were unacceptable levels of both false positives and false negatives. A new method is presented, which shows improved correlation with perceived fluctuation in sounds. Comparisons are made to the previous method and improvement is shown through examples of objective-subjective correlation for both seat motor sounds and adjustable pedal motor sounds. The new method is also shown to match subjective data from which the original measure of Fluctuation Strength was derived.