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NIH Public Access Author Manuscript Hum Factors. Author manuscript; available in PMC 2012 September 21.

NIH-PA Author Manuscript

Published in final edited form as: Hum Factors. 2012 June ; 54(3): 437–453.

Auditory Perception of Motor Vehicle Travel Paths Daniel H. Ashmead, Vanderbilt University, Nashville, Tennessee D. Wesley Grantham, Vanderbilt University, Nashville, Tennessee Erin S. Maloff, Cochlear Americas, Centennial, Colorado Benjamin Hornsby, Vanderbilt University, Nashville, Tennessee


Takabun Nakamura, Okayama Prefectural University, Soja, Okayama, Japan Timothy J. Davis, Vanderbilt University, Nashville, Tennessee Faith Pampel, and Vanderbilt University, Nashville, Tennessee Erin G. Rushing Louisiana State University, New Orleans

Abstract Objective—These experiments address concerns that motor vehicles in electric engine mode are so quiet that they pose a risk to pedestrians, especially those with visual impairments. Background—The “quiet car” issue has focused on hybrid and electric vehicles, although it also applies to internal combustion engine vehicles. Previous research has focused on detectability of vehicles, mostly in quiet settings. Instead, we focused on the functional ability to perceive vehicle motion paths.


Method—Participants judged whether simulated vehicles were traveling straight or turning, with emphasis on the impact of background traffic sound. Results—In quiet, listeners made the straight-or-turn judgment soon enough in the vehicle’s path to be useful for deciding whether to start crossing the street. This judgment is based largely on sound level cues rather than the spatial direction of the vehicle. With even moderate background traffic sound, the ability to tell straight from turn paths is severely compromised. The signal-tonoise ratio needed for the straight-or-turn judgment is much higher than that needed to detect a vehicle. Conclusion—Although a requirement for a minimum vehicle sound level might enhance detection of vehicles in quiet settings, it is unlikely that this requirement would contribute to pedestrian awareness of vehicle movements in typical traffic settings with many vehicles present.

Copyright © 2012, Human Factors and Ergonomics Society. Address correspondence to Daniel H. Ashmead, Department of Hearing and Speech Sciences, Vanderbilt University School of Medicine, 1215 21st Ave. South, MCE South Tower, Room 8310, Nashville, TN 37232-8242, [email protected].

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Application—The findings are relevant to deliberations by government agencies and automobile manufacturers about standards for minimum automobile sounds and, more generally, for solutions to pedestrians’ needs for information about traffic, especially for pedestrians with sensory impairments. Keywords pedestrian; traffic; quiet car; visual impairment

INTRODUCTION Pedestrians benefit from hearing traffic activity, including vehicles (Wiener et al., 1997) and audible signals (Szeto, Valerio, & Novak, 1991; Wall, Ashmead, Bentzen, & Barlow, 2004). The field of orientation and mobility provides instruction for individuals who are visually impaired to travel safely and efficiently in their environments (Wiener, Walsh, & Blasch, 2010), including at street crossings (Hill & Ponder, 1976; LaGrow & Weessies, 1994). In a quiet area, a pedestrian may listen and then cross a street if no nearby vehicles are detected. In busier traffic, patterns of vehicle movements are important. For example, traffic flow can be used to align oneself to the street (Guth, Hill, & Rieser, 1989), and traffic surge on a green light often specifies the walk signal to cross the other street.


Some patterns of vehicle movement are readily heard, but others are problematic. In freerunning traffic at unsignalized locations, pedestrians wait for gaps between vehicles. Studies of such crossing decisions by blind pedestrians at roundabouts indicate that acoustic guidance is unreliable and inefficient (Ashmead, Guth, Wall, Long, & Ponchillia, 2005; Guth, Ashmead, Long, Wall, & Ponchillia, 2005). Similarly, Wall Emerson and Sauerburger (2008) reported that pedestrians with visual impairments detected approaching vehicles well in quiet but not with moderate background noise or when curves, hills, and roadside trees obscured sounds. Quiet Cars


Vehicle sound comes from the power train (engine, etc.), tires, and aerodynamics. At speeds slower than about 32 km (20 miles) per hour, engine sound predominates, whereas tire and aerodynamic sound are prominent at higher speeds (Sandberg & Ejsmont, 2002). Vehicle sound has been regulated in many countries to reduce adverse effects of noise (Miedema, 2007; U.S. Noise Pollution and Abatement Act of 1972). Sound levels from passenger vehicles may have decreased by 5 to 10 dB in the past 40 years, but they are difficult to monitor, and the reduction is mitigated by increased numbers of vehicles. In any event, vehicle noise regulation has until recently focused on limiting noise, not increasing it. Since commercially viable hybrid vehicles appeared, around 2000, concern has been expressed that pedestrians may be at risk because of the low level of sound from vehicles traveling in electric engine mode (National Federation of the Blind, 2010). In the present article, the term gasoline refers to vehicles with internal combustion engines fueled by gasoline or diesel, electric refers to vehicles powered by electricity stored in batteries, and hybrid refers to vehicles powered by a combination of electricity and internal combustion. Government agencies and automobile manufacturers have responded to concern about quiet vehicles, primarily by an arrangement to play add-on sound through loudspeakers mounted on the vehicle. This sound would occur when the vehicle is stopped and at speeds lower than about 32 km/h, since tire noise predominates at higher speeds. No standards for this system exist currently, and various sounds and operation modes are under consideration.

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The Pedestrian Safety Enhancement Act of 2010 directed federal transportation authorities to recommend standards for a minimum sound level to be emitted by vehicles. Although focused on electric and hybrid vehicles, the legislation also stipulated consideration of whether a standard should apply to all. Existing research on the quiet-car issue consists of crash data, direct acoustic measurements, and assessment of detection of approaching vehicles in fairly quiet backgrounds. As reviewed here, this literature confirms that cars in electric mode at low speeds tend to be quieter and less detectable than many cars in gasoline mode. However, a wider question is what sound level, if any, provides pedestrians with information needed to make safe road-crossing decisions. Crash data—Hanna (2009) analyzed crash data from hybrid and gasoline vehicles on similar model platforms. Although only 0.6% of all accidents involved pedestrians, the odds of pedestrian involvement were 1.44 times greater for hybrid than for gasoline vehicles. Similar odds ratios favoring gasoline vehicles held for subsets of crashes, such as when the vehicle turned before the crash, or in areas with speed limit lower than about 50 km/h. These findings are consistent with the idea that pedestrians hear approaching hybrid vehicles less well than they do gasoline vehicles, although this finding is complicated by possible differences in driver behavior and frequency of driving in pedestrian settings.


Acoustical measures of vehicle sounds—Measurement of pass-by sound from vehicles is standardized (e.g., Moore, 2006), although studies of hybrid and/or electric compared with gasoline vehicles have varied from the standard, making comparisons more conclusive within studies than between. In the standard procedure, a microphone is positioned 1.5 m high and 7.5 m (2.5 m in some studies) from the travel centerline as the vehicle passes, either accelerating or at constant velocity. Wiener et al. (1997) reported that as gasoline vehicles accelerated from a stop for 5 s, maximum sound level was 75 to 80 dBA (the A-weighted decibel scale approximates human loudness perception). Sound level from a gasoline vehicle approaching at moderate speed measured 67 dB-A from 33.5 m away, the distance needed to cross safely in typical pedestrian situations. Wiener, Naghshineh, Salisbury, and Rozema (2006) measured hybrid and gasoline cars built on similar body platforms. On acceleration to about 30 km/h, hybrids were quieter by 8 to 9 dB but still audible. At low speeds, the hybrids, in electric mode, emitted sound quieter than the low level of background noise. On approach from 33.5 m at moderate speed, hybrid and gasoline vehicles had similar sound levels, mostly from tire noise. When stopped, the hybrids emitted no measurable sound.


Garay-Vega, Hastings, Pollard, Zuschlag, and Stearns (2010) studied three hybrid and three gasoline vehicles. In decelerative pass-by, average sound level differed by less than 1 dB (54.2 dB-A, hybrid; 54.9 dB-A, gasoline). In slow (constant 10 km/h) pass-by, the hybrid sound level was lower (49.0 and 53.7 dB-A for hybrid and gasoline, respectively), and in reverse gear, the hybrid level was much lower (46.2 and 54.1 dB-A for hybrid and gasoline, respectively). The sound levels did not differ across engine types at speeds higher than 25 to 30 km/h, as expected because of tire noise. Although this report showed differences between vehicle types, even the gasoline vehicles had low sound levels, in the mid-50s dB-A. In contrast, pedestrians routinely make roadcrossing decisions with background sound levels in the 60s and 70s dB-A (Lawson & Wiener, 2010). Signal-to-noise ratio is a key factor in whether a vehicle can be heard, so the quiet-car issue applies to vehicles of all engine types. Wall Emerson, Naghshineh, Hapeman, and Wiener (2011) studied pedestrian road-crossing decisions using gasoline vehicles from ambient traffic and hybrid vehicles operated by the investigators. Pass-by sound was only 2 to 3 dB-A lower for hybrid than for gasoline vehicles at 15 to 30 km/h and equivalent at higher speeds. Kim, Wall Emerson, Naghshineh, Pliskow, and Myers (2012) had listeners detect approaching vehicles of three types: hybrid Hum Factors. Author manuscript; available in PMC 2012 September 21.

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in electric mode, hybrid in electric mode with an add-on sound played through an onboard loudspeaker, and gasoline. At moderate speed, sound levels were 59.9, 63.3, and 59.7 dB-A, respectively (but the add-on sound was played louder than is likely to be done in practice). The acoustical findings indicate that vehicles in electric mode are quieter than those in gasoline mode when stopped or at low speed. However, at moderate speeds typical of pedestrian settings, sound level is similar across engine types and likewise at higher speeds because of tire noise. Besides overall sound level, the spectral composition of a vehicle’s sound relative to the background merits consideration because a distinct “signature” might stand out. This possibility has not been investigated, but such an effect would be mitigated as more vehicles have such a sound. Detection of approaching vehicles—Robart and Rosenblum (2009) recorded hybrid (electric mode) and gasoline vehicles approaching at 8 km/h from the left or right. Listening through headphones, participants reported the approach direction. The hybrid was reported 1 to 3 s later than the gasoline vehicle, and with moderate background noise, not until just after passing the listener. Even the gasoline vehicle was not reported until quite close, approximately 6 m, with background traffic noise, which could be dangerous.


Wall Emerson et al. (2011) studied roadcrossing decisions at an unsignalized intersection with hybrid and gasoline vehicles. Ratings of riskiness did not break down neatly between vehicle types. For speeds lower than 30 km/h, the odds of a risky crossing were close to zero for two hybrids, 0.05 for gasoline engine vehicles, and 0.22 for one hybrid model. Detection of the traffic surge on a green light at a signalized intersection was poor for one hybrid that accelerates in electric mode, but even when gasoline vehicles were present, only two thirds of surges were detected soon enough to afford safe crossing. Garay-Vega et al. (2010) studied detection of approaching vehicles that were backing up, approaching at low speed, or approaching while slowing from 32 to 16 km/h, all in a very quiet setting. For the backing-up and slow approaches, gasoline vehicles were detected from about 1.5 times farther than hybrids. For the decelerative approach, an opposite result occurred, with hybrid vehicles detected at about twice the distance of gasoline vehicles, mainly because of brake-related electrical conversion in one hybrid model. Even for gasoline vehicles, the range of times remaining until the vehicle would have reached the listener was less than 6 s and just 2 or 3 s in some conditions. Given these short times, and the quiet background, detection of approaching vehicles might be problematic in a real roadcrossing situation.


Kim et al. (2012) found that vehicles approaching at about 15 km/h were detected at 38 m for gasoline vehicles, 34 m for hybrids in electric mode with add-on sound, and 27 m for hybrids in electric mode. Using the same vehicles and test sites, Kim, Wall Emerson, Naghshineh, Pliskow, and Myers (2011) had vehicles stop for a random time and then accelerate, either continuing straight or turning rightward in front of the listener. The listeners, including blind persons accustomed to pedestrian travel, indicated when the vehicle started moving and when they judged that it had gone straight or turned. For the straight-or-turn judgment, accuracy was 93%, but latency averaged 6.3 s and was similar across vehicle types. At that latency, a turning vehicle was almost in front of the listener. Thus, even when listeners could hear a vehicle, judgments about turns were quite delayed. In summary, detection studies suggest that vehicles in electric mode (with no added sound, at low speeds, in quiet backgrounds) must be closer to be detected, compared with similar gasoline vehicles. However, even the detection distances for gasoline vehicles are sometimes too short to afford safe road crossing, especially considering that most tests have


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had background traffic noise lower than pedestrians frequently encounter. This finding raises the possibility that a public policy to add sound to electric and hybrid vehicles, so as to match them to gasoline vehicles, might be of little help to pedestrians in many roadcrossing situations. Auditory Motion Perception Barlow, Bentzen, Sauerburger, and Franck (2010) summarized strategies used by blind pedestrians to navigate complex intersections, noting the importance of listening for subtle patterns of vehicle movement. For example, listening to traffic in the near lane on the street parallel to the pedestrian’s direction of crossing is useful, because vehicles often move there during the pedestrian walk interval. For the present study, we examined the ability to listen for straight or turning vehicles from the near lane as a key auditory motion perception task. Even sighted pedestrians may benefit from hearing, especially when a vehicle comes from behind. Pedestrian risk from turning vehicles is an important category of injuries (Lord, Smiley, & Haroun, 1998; Roudsari, Kaufman, & Koepsell, 2006). Pedestrians tend to look in their own travel direction, and hearing provides an important extension of awareness.


Research on auditory motion perception provides clues about the perceptual capability for tracking vehicle paths, but the present study is the first we know of that focuses on motion paths relevant to pedestrian activity. Auditory motion perception is somewhat crude compared with localization of stationary sound sources. The minimum audible angle for stationary sound sources is often found by presenting two sounds consecutively from different horizontal directions, asking the listener to report whether the second sound was left or right of the first. In good conditions, a direction difference of 1° to 2° can be judged correctly (Mills, 1958). By comparison, the minimum audible movement angle for circular paths requires a larger change in direction for telling whether a sound in continuous motion went leftward or rightward. For angular velocities comparable to traffic settings (45° per second), a motion extent of 5° to 10° is required (Chandler & Grantham, 1992; Perrott & Tucker, 1988).


Research has also been conducted on sound sources moving on straight paths, including direct approach toward the listener (Bach, Neuhoff, Perrig, & Seifritz, 2009; Rosenblum, Wuestefeld, & Saldana, 1993) and transverse or “miss” paths (Kaczmarek, 2005; Lutfi & Wang, 1999). A distinction between the underlying cues for changing direction or distance of a sound source is that direction is cued primarily by interaural differences, whereas distance is cued by information available at either ear. Sound from off to one side arrives sooner and with greater level at the ear on that side, which specifies horizontal direction. As a sound source changes in horizontal direction, the values of these interaural differences in time and level change, specifying motion. Sensitivity to continuous changes in these cues is rather crude, leading to the phrase “binaural sluggishness” (Grantham, 1984). The information for changing distance includes changes in sound level, because level varies inversely with distance, and the ratio of direct to reverberant sound (Coleman, 1963). Most motion paths that pedestrians listen for involve combinations of change in direction and distance. Transverse motion, such as a car moving past a pedestrian standing at the side of the street, provides a predictable pattern of direction and distance change. If the vehicle turns, there is a different pattern of change in the underlying cues. Transverse motion also introduces a sound frequency change known as the Doppler shift (McBeath & Neuhoff, 2002) caused by acceleration (during approach) in the distance component of motion, noticeable at fairly high velocities. Doppler shift can specify that a sound source is approaching (or receding from) a listener, with some indication of the rate of approach.


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Study Overview


We simulated vehicle motion paths acoustically in a laboratory to provide control of factors such as speed, specific paths, and background noise. Two acoustic “signatures” were considered, a gasoline engine idling and the add-on sound proposed for an electric vehicle built on the same platform. In Experiment 1, the ability to distinguish between straight and turn paths was measured in a quiet background to determine thresholds in ideal listening conditions. In Experiment 2, we evaluated the effect of background noise level using recordings of live traffic. In Experiment 3, we varied the vehicle sound levels adaptively to determine signal-to-noise ratios necessary for making the straight-or-turn judgment in the presence of moderate background traffic noise.

EXPERIMENT 1 In this experiment, we measured listeners’ ability to distinguish straight from turn paths, compared performance for an electric vehicle add-on sound and a gasoline engine sound, and compared low and moderately high sound levels of the signals. The background sound level was very quiet. Method


Participants—The participants were four young adults, one woman and three men, with normal vision by self-report and normal hearing in both ears at frequencies of 0.25, 0.5, 1, 2, 4, and 8 kHz (ability to hear tones through earphones at sound level within 25 dB of ideal thresholds). Test setting—Testing occurred in a 4.6 × 6.4 × 6.7 m (width, length, height) full anechoic chamber (100 Hz lower frequency limit) with a horizontal circular (1.97-m radius) array of 64 loudspeakers (RCA 40–5000, housing 4 in. wide × 6.5 in. tall × 4.25 in. deep, two speaker cones upper 2.5 in. and lower 1 in. diameter), spaced at intervals of 5.625°. The ambient sound level was very low, 20 dB-A. The participant sat in a chair at the center of the loudspeaker array. Acoustic motion paths were simulated for motor vehicles going along straight or turn paths, as shown in Figure 1. This setup simulates a pedestrian on the southeast corner of a perpendicular intersection of two streets, waiting to walk northward across the east-west street. The motion paths originate behind and to the left of the pedestrian, with vehicles moving northward. The path is either straight, as though the vehicle were headed north through the intersection, or turning right onto the east-west street.


Motion path simulation—The objective was to find out how far the motion path had to go for the listener to decide whether it was straight or turning. In Figure 1, consider the coordinate (0,0) the intersection of north-south and eastwest traffic lanes. The path started with the vehicle 15.75 to 20.75 m south of the intersection. From there, a path of twice the starting distance from the intersection was made. For example, if the path started at (0,−17) and the path were straight, it would go to (0,17). The path was divided into five segments, each with a distinct velocity so that there was deceleration before a turn and acceleration afterward. On average, each segment was 20% of the path, but that percentage varied randomly between 15% and 25%. Consider three velocities, with v1 > v2 and v3 > v2. For turn paths, velocity was as follows: constant at v1 in Segment 1, uniform deceleration from v1 to v2 in Segment 2, constant at v2 in Segment 3 (where most of the turn occurred), uniform acceleration from v2 to v3 in Segment 4, and constant at v3 in Segment 5. Thus, the simulated vehicle started at constant


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velocity, slowed down and held that speed through the turn, and then sped up. An overall lower or higher velocity range was randomly chosen on each trial: lower range (v1, 16.9 to 21.7 km/h; v2, 10.5 to 15.3 km/h; v3, 16.9 to 21.7 km/h) or higher range (v1, 21.7 to 26.6 km/h; v2, 13.7 to 18.5 km/h; v3, 21.7 to 26.6 km/h). Although v1 and v3 were both greater than v2, v1 and v3 had similar but not necessarily identical values. For straight paths, the same scheme was used except that v1, v2, and v3 were all selected from either a low range of 16.9 to 21.7 km/h or a high range of 21.7 to 26.6 km/h. This method created patterns of acceleration and deceleration in the straight paths, which simulates driver behavior. Figure 2 shows examples of velocity profiles for turn and straight paths in the lower and higher velocity ranges. Although an entire path was created for each trial, as described previously, only the portion of the path up to the current psychophysical path length was presented. For the example of a straight path that began at (0,−17), and assuming a current path length of 7 m, the path would end at (0,−1), that is, 7 m beyond the nominal start position of (0,−8) from which path lengths were measured psychophysically.


The velocity rules meant that turning paths were a little slower and longer lasting than straight paths. This difference was probably not a reliable cue. At the threshold path lengths, the straight paths lasted about 0.5 s less than the turn paths. However, the durations were rather long, about 3 to 5 s, and duration variability attributable to the random starting location was about 0.8 s. It is also unlikely that the deceleration during turns was a reliable cue, because about half of the straight paths included decelerating segments. After being calculated, the motion path was converted to a set of directions and distances relative to the listener’s position and heading direction. This information was sent to a function that presented the acoustic signal to the loudspeakers. For each time sample, we coded direction by selecting a pair of loudspeakers (11.25° apart, that is, every other loudspeaker) that bracketed the target direction and refined it further by amplitude panning. The panning algorithm apportioned amplitude between the two loudspeakers according to and A2 = 1 – A1, where the A terms are amplitude scaling factors for the formula the two loudspeakers (Pulkki & Karjalainen, 2001). For example, if Loudspeaker 2 is δ = 11.25° to the left of Loudspeaker 1, and the current sound source direction is supposed to be directly at Loudspeaker 2, then θ = 0° and the amplitude is apportioned all to Loudspeaker 2 . If the source direction is 3° to the right of Loudspeaker 2, then θ =3° and the scaling factors are A1 = 0.732 and A1 = 0.268.


In practice, the panning formula produces a linear trade-off of amplitude between the loudspeakers as the target direction moves between them. Although the panning procedure is sometimes misunderstood to affect perceived sound direction by means of interaural sound level differences, it does so by introducing phase, or interaural time differences (Pulkki & Karjalainen, 2001). Distance was coded by appropriate scaling of the sound level (Coleman, 1963), in decibels relative to a reference distance of 2 m. For example, a distance of 10 m was dB from the sound level at 2 m. The distance coding allowed the simulated motion paths to extend well beyond the actual loudspeaker array (see Figure 1). Signals were low passed at 5 kHz to remove artifacts from panning. The signal was presented with a sampling rate of 44.1 kHz, but spatial updating for direction and distance was done at 244.14 Hz, adequate for a sense of smooth motion. Figure 3 shows the direction and sound-level cues corresponding to the motion paths. Doppler shift was not included as a distance cue because it would be negligible in the low velocity range used (e.g., frequency Hum Factors. Author manuscript; available in PMC 2012 September 21.

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shift of 1.76% at 21.7 km/h, below detection threshold for frequency glides of nontonal sounds; Madden & Fire, 1997).


Psychophysical threshold procedure—On each trial, the participant was presented with a single motion path while looking straight ahead (north in Figure 1), then voted via a response box whether the path was straight or turning. Feedback lights showed the correct response after the vote. The one-interval task could allow response bias if, for example, a participant tended to vote straight more often than turning. To protect against this bias, participants were told that the occurrence of straight or turning paths was random with equal probability on each trial and were presented with obvious examples of both paths prior to formal testing. All participants used the straight and turning response options with similar frequency.


The psychophysical method was a threedown, one-up staircase (Levitt, 1971), tracking 79.4% correct. After three consecutive correct responses, the path length was reduced, making the task more difficult. After each incorrect response, the path length was increased. The initial path length was 7 m. Decreases in path length were by a factor of 0.833; increases, by 1.2. The maximum path length was 10 m, which put the end of the turn path well to the listener’s right. Testing continued through eight reversals in the direction of change in path length. Thresholds were estimated by the geometric mean of path lengths on Reversals 3 through 8. Vehicle acoustic signatures—Two vehicle signals were used, electric and gasoline. Figure 4 shows the frequency spectra.


The electric vehicle signal was a proprietary sound provided by a manufacturer of electric cars. It has a whoosh or hum characteristic with mostly low pitch along with a higher-pitch tonal aspect, designed to sound somewhat futuristic. The gasoline vehicle signal was a recording of engine idling from a vehicle manufactured on the same body platform as the electric vehicle. Two levels were used for the vehicle sounds, 52 dB-A and 65 dB-A, measured with a half-inch omnidirectional microphone positioned 2 m from the loudspeakers. The 52 dB-A level corresponds to the engine idling sound of the real gasoline vehicle, measured 2 m from the front of the vehicle. From two vehicle signals (electric, gasoline) and two sound levels (52, 65 dB-A), there were four experimental conditions. Each participant was tested at least three times in each condition. Testing occurred in blocks, such that all four conditions were run as separate psychophysical tracks in each block, in orders set by Latin squares. So practice or fatigue effects were distributed similarly across conditions. Statistical analyses were based on individual participants’ mean thresholds across multiple runs in each of the four experimental conditions. Results and Discussion Figure 5 shows mean threshold path lengths. We conducted analyses of variance using a univariate repeated-measures approach, and for all analyses reported, no sphericity correction was required according to the Mauchly (1940) criterion. In an analysis of variance with vehicle type and sound level as repeated-measures factors, there were no significant effects. This finding was expected because both vehicle signals provide good information for spatial hearing, and both sound levels are comfortably audible. Mean threshold path length across conditions was 5.91 m (SD = 0.69). One can see this path length in Figure 1 by counting up 6 m from the nominal start position and in Figure 3 at the doubleended arrow. At this path length, the vehicle was positioned about 75° to the listener’s left, which was true for both the straight and turning paths. The finding that threshold path lengths were the same for the lower and higher sound levels provides replication of the basic


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finding and suggests that the range from 52 to 65 dB-A has little impact on distinguishing straight from turning paths in a quiet background.


The threshold path length of 5.91 m implies that listeners used sound level, rather than direction, as the primary basis for distinguishing straight from turning paths, and the salience of sound level is what participants reported in debriefing. At this path length, the straight and turning paths differed in sound level by 4 to 5 dB but in direction by only 2° to 3°. In terms of the dynamic cue values, at threshold path length, the sound level kept increasing on turn paths because the sound source got closer to the listener, whereas the sound level tapered off on straight paths as the source moved away from the listener (see Figure 3). Listeners might have attended to both the overall differences in sound level on straight and turning paths and to the pattern of change. Pilot work with one participant showed that thresholds worsened by about 25% when the sound level cue was removed. Although sound level appeared to play a key role, directional change probably contributed to judgments. For example, participants may have waited until the vehicle reached about 90° to their left before attending closely to sound level. Also, some participants had path lengths approaching 7 m, for which straight and turning paths differed by about 30°, so these individuals may have relied on directional information.


The threshold path lengths relate to practical decisions pedestrians make about whether to start crossing the street when a vehicle is approaching. At the average threshold path length, the vehicle would arrive directly in front of the pedestrian (in the crosswalk) in another 2 to 3 m. At the vehicle speeds used for the turns, 10.5 to 18.5 km/h, it would take less than 1 s for the vehicle to travel that far. This time is not enough for the pedestrian to safely cross in front of a turning vehicle. However, it is short enough so that, if the pedestrian started to cross, he or she could hear the vehicle come through the crosswalk and pause or step back. Of course, in a real traffic setting, a pedestrian might have additional information to use in deciding whether a vehicle was going straight or turning, such as tire noise or activity by other pedestrians.

EXPERIMENT 2 The second experiment assessed the effect of background noise on performance of the straight-or-turning task. Experiment 1 was conducted in an extremely quiet background, not typical of pedestrian road-crossing settings. For Experiment 2, we obtained roadside recordings of “live” traffic in two settings, a fairly quiet residential and light business area, and a busy roadway near a four-way intersection. These recordings were played as background noise (Figure 6 shows the spectrum).


The target vehicle signals were the same electric and gasoline ones used in Experiment 1, played at a reference level of 52 dB-A measured from 2 m away. This sound level is typical for a contemporary car with a fairly quiet internal combustion engine. The practical question addressed by this experiment is how well the motion paths of cars with typical sound level are perceived in different levels of background traffic noise. Method The participants were eight young adults with normal hearing according to screening as in Experiment 1 (two participants were in that experiment). Both the electric and gasoline vehicle signals were played at a reference level of 52 dB-A (at 2 m distance), with level adjustments to simulate target distance. There were four background sound conditions: quiet (like Experiment 1), residential-like noise at 54 dB-A, moderate traffic noise at 60 dB-A, and busy traffic noise at 68 dB-A. These noise levels were based on sampling in various traffic locales and are consistent with levels reported by Lawson and Wiener (2010). For the


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three conditions with background traffic noise, a 30-s “loop” of noise was played repeatedly. Copies of the loop were played continuously through two loudspeakers, with a 15-s offset between them, producing two separate streams of traffic noise. The two loudspeakers (JBL 8110, 4-in. cones) playing the background noise were located 5° and 175° to the participant’s left. This setup simulated an intersection scenario with most of the moving vehicles to the pedestrian’s left and parallel to the intended walking direction. In pilot work, listeners had difficulty performing the task at the moderate and high levels of traffic noise. This was not surprising, given the signal-to-noise ratios of −8 and −16 dB, respectively. However, it was important to include these conditions, because they correspond to the practical problem of listening to individual vehicle paths in a traffic setting. It was necessary to devise a stopping rule in parallel with the staircase psychophysical procedure to end the threshold search if the participant could not do the task regardless of how far the motion path went. The rule was that if the path length reached the maximum allowable value of 10 m five times during a run, then the run ended with a “ceiling-reached” designation. As shown in Figure 3, if one counts 10 m along the motion paths, both the level and direction differences between turn paths and straight paths are large for a 10-m path length. In a quiet background, this discrimination would be extremely easy. When participants reached ceiling, they reported that they could not “hear out” the target vehicle from the background noise.


Results and Discussion At the higher levels of background noise, participants had great difficulty performing the task. The proportions of threshold runs successfully completed were calculated on the basis of three runs that each participant performed in each of the eight conditions (four background noise levels by two vehicle signals). Figure 7 shows the mean proportions. In an analysis of variance with vehicle and background noise level as repeated-measures factors, there was a significant effect of noise level, F(3, 21) = 35.445, p < .001, η2 = .835. The results were similar across vehicle signals, and the interaction was not significant. For the moderate and high traffic noise backgrounds, these findings suggest that the sound level of the target vehicle was too low, relative to the background traffic noise, for participants to perform the task. In fact, they were often unaware that a target vehicle had come by—even when it passed directly in front of them—until lights appeared on the response box, indicating it was time to vote.


For statistical analyses, a participant’s threshold for each condition (vehicle type by background sound level) was calculated from any of the three threshold runs that were not discontinued or was assigned a value of 10 if all three runs were discontinued. An analysis of variance was conducted with vehicle signal (gasoline, electric) and background sound level (quiet, residential, moderate traffic, heavy traffic) as repeated-measures factors. Only the main effect of background sound level was significant, F(3, 21) = 18.995, p < .001, η2 = .731. This analysis is problematic because 13 of the 64 data points were assigned the ceiling level of 10 m, all in the moderate and busy background traffic noise levels. When those points were excluded as missing data, the background sound level was again the only significant factor, F(3, 15) = 4.794, p < .016, η2 = .978. Considering only the findings from complete threshold runs, and averaging across gasoline and electric vehicles, the mean path lengths were quiet, 6.78 m; residential, 7.54 m; moderate, 7.77 m; and busy, 7.75 m. Although this finding confirms that performance worsened with increasing background sound level, the more salient finding was that in the moderate and busy traffic conditions, participants were often reduced to guessing. Only in the quiet and residential background noise settings could all participants perform the task Hum Factors. Author manuscript; available in PMC 2012 September 21.

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consistently and with clear awareness of the target vehicle. Even in the residential noise condition, however, the mean path length (averaged across electric and gasoline vehicle sounds) was significantly longer than in the quiet condition, F(1, 7) = 14.235, p < .007, η2 = .670. These findings indicate that the acoustic signature of individual vehicles is too weak, in moderately busy traffic settings, to enable listeners to track individual motion paths of vehicles. In fact, even the noise in a quiet residential setting has a substantial impact on the ability to make judgments about whether vehicles are turning or going straight. The findings were very similar for the electric and gasoline vehicle signals, suggesting that the general problem is that moderate to high levels of background traffic noise mask the sound coming from individual vehicles, even those traveling near the pedestrian’s location.

EXPERIMENT 3


The findings from Experiment 2 suggest that pedestrians have difficulty tracking individual vehicle motion paths in background traffic noise. In Experiment 3, we turned the question around by setting a fixed background noise level and asking how high the individual vehicle signal needs to be for participants to distinguish straight from turning paths. The path length was fixed to a range between 7 and 8 m (on each trial, the path length varied randomly in that range). Instead of varying path length psychophysically across trials, we varied the sound level of the vehicle signal. The background traffic noise was the same as in Experiment 2, played at 60 dB-A, typical of a moderately busy street. An additional question posed in this experiment was whether there is a difference in the sound level needed to just detect a vehicle, compared with telling whether it is on a straight or turning path. This question matters because setting sound levels ample for detection might still leave pedestrians unable to perform subtle, yet critical, listening decisions. Method


The participants were eight young adults with normal hearing according to screening as in Experiment 1. Of the total participants, five were in Experiment 2 and were included because they were well practiced on the task. The experimental conditions were the combinations of two vehicle sounds (electric, gasoline) and two tasks (detection, discrimination between straight and turn path). Each participant performed in all four conditions in a block of separate threshold runs, and this procedure was followed four times, for a total of 16 threshold runs. Orders within blocks were counterbalanced. The procedure was like that in Experiments 1 and 2, except that the path length was fixed within a narrow range, and the sound level in decibels of the target vehicle sound was varied psychophysically (decreases by a factor of 0.833, increases by 1.2). On each trial, the path length was chosen randomly in the range of 7 to 8 m, a length at which straight and turning paths are readily distinguished in a quiet background. The vehicle sound level was initially 67 dB-A, for a signal-to-noise ratio of +7 dB over the background of 60 dB-A. On each trial in the detection task, the signal (i.e., vehicle sound) was randomly assigned as present or absent, and the listener judged whether the signal was present or absent. When the signal was present, the motion path was randomly straight or turn, but the listener reported just whether there was a vehicle. In the straight-or-turn discrimination task, there was a vehicle on each trial, and the listener judged whether its path was straight or turning. Results and Discussion Figure 8 shows the average threshold signal-to-noise values.


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In a two-factor, repeated-measures analysis of variance, there was a significant effect of task, F(1, 7) = 52.104, p < .0002, η2 = .882, with the signal-to-noise ratio 11.1 dB lower for the detection task than the straight-or-turn discrimination task. Thus, listeners required an additional 11 dB above their detection thresholds to judge reliably whether the vehicle was on a straight or turning path. Even for the detection task, signal-to-noise ratio was high, about −2 dB for the gasoline vehicle signal, suggesting that in moderate background traffic noise, some gasoline vehicles would not be detectable. There was also a significant effect of vehicle, F(1, 7) = 24.743, p < .002, η2 = .779. Averaged across tasks, signal-to-noise thresholds were 3.2 dB lower for the electric sound than for the gasoline sound. This finding may be because the electric vehicle sound was more distinct spectrally from the background traffic noise, which was overwhelmingly based on gasoline-powered vehicles. The interaction between task and vehicle was not significant.


Figure 8 shows that the task of distinguishing between straight and turn paths requires a signal-to-noise ratio of +6 to +8 dB. The background traffic noise was at 60 dB-A, typical for many nonresidential settings. This finding implies that judgments about vehicle paths made in moderately busy traffic settings require individual vehicles to emit very high sound levels. Specifically, the sound level of the individual vehicle needed to be 65 to 70 dB-A measured from a distance of 2 meters. This level is far higher than contemporary vehicles emit, even internal combustion engine cars. It is unlikely that such a high sound level would be acceptable to drivers or in terms of community noise levels. If a large portion of the vehicle fleet had such a high sound level, then the background traffic noise level would be higher, leading to an untenable upward spiral of vehicle sound. The discrepancy between the signal-to-noise ratios needed to detect a vehicle, as opposed to making a judgment about the vehicle’s motion path, was large, about 11 dB. This result agrees with a classic finding that speech detection signal-to-noise thresholds are about 8 dB lower than speech recognition thresholds (Cambron, Wilson, & Shanks, 1991). This discrepancy is mirrored in clinical audiology practice: Someone can tell that a person is talking but cannot understand what he or she saying. Similarly, just detecting that a vehicle is in the area does not ensure that a listener can reliably perceive the vehicle’s motion path. This distinction is important in that much of the research literature on quiet cars has focused on the rather simple scenario of detecting an approaching vehicle in a quiet setting. Detection by itself has limited value in situations in which pedestrians must make road crossings while there are moving vehicles in the immediate area.

General Discussion NIH-PA Author Manuscript

Auditory Motion Perception Our experiments were not designed to exhaustively analyze what perceptual cues were used for discriminating straight from turning paths, but the findings suggest that listeners attended to a sound level cue. Figure 3 shows sound level and source direction plotted for straight and turn paths. At the threshold path length, the direction of the sound source had not yet diverged much for the straight and turn paths. In contrast, the sound level was still increasing in the turn path, whereas it had peaked and started to decrease for the straight path. The vehicle was still getting closer to the listener in the turn path, whereas it was moving farther away in the straight path. Indeed, participants reported that they attended to sound level. In Experiment 1, with a quiet background, participants could have attended to the peak sound level, which was higher by several decibels for the turn path than for the straight path starting at a nominal path length of about 5 m (see Figure 3). However, this peak level cue was probably less noticeable in Experiments 2 and 3 because of natural fluctuations in the background noise level, and in a real street setting, it would not be useful


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because of variability in vehicle sound levels. For these reasons we favor the idea that participants made judgments on the basis of the pattern of sound level change across time.


Lutfi and Wang (1999) compared various cues for perception of displacement, velocity, and acceleration of sound sources moving on headphone-simulated straight transverse paths, similar to the straight path in the present study. They reported that in their lower speed range, which was most similar to our speeds, listeners were influenced strongly by sound level. However, the weight given to sound level, interaural time differences, and Doppler shift varied with overall velocity range, perceptual task, predictability of the fundamental frequency of the harmonic tone stimulus, and individual preference. They emphasized that auditory motion perception has multiple perceptual components, with no single stimulus dimension predominating. It will be valuable in future work to explore these factors in the context of pedestrian decision making. We tentatively suggest that instructional strategies for distinguishing between straight and turning paths might focus on pedestrians listening more to the pattern of change in overall sound level than to direction per se. It should be noted that the acoustic simulation was unrealistic in certain respects, such as the absence of reflected sound and lack of spatial directivity of the vehicle signals. More realistic simulations might reveal variations in the ability to distinguish straight from turning paths.


Effect of Background Noise Although listeners could distinguish between straight and turn paths in quiet and low noise backgrounds, this ability was greatly compromised by traffic noise levels typical of those in which pedestrians travel. These findings add to previous reports that auditory motion perception is of limited use for some of the decisions that pedestrians, especially those with visual impairments, must make. At modern roundabouts, when crossing the exit lane(s) from the roundabout, pedestrians often need to decide whether a vehicle has turned into the exit lane or continued in the circulatory roadway. Even with visual support, it is difficult to make this decision quickly, before the next vehicles approach. Without visual guidance, it is very difficult to hear individual vehicles and decide whether they will exit the roundabout (Ashmead et al., 2005; Guth et al., 2005).


In Experiment 2, with several levels of background noise and a typical sound level for the target vehicle, most of the test sessions in moderate to heavy noise were terminated early because listeners could not discriminate better than chance level (see Figure 7). In Experiment 3, with background traffic noise set at a moderate level of 60 dB-A, the signalto-noise ratio needed for the straight-or-turn discrimination was +6 to +8 dB (see Figure 8). With that ratio, vehicle sound output would be about 10 to 15 dB higher than the level of most contemporary gasoline vehicles. Even if this sound level were acceptable, the necessary signal-to-noise ratio could not be achieved because the background traffic sound level would rise. A caveat is that our traffic noise was from a 30-s recorded loop selected to represent average levels. Across a longer term, traffic noise has highs and lows not fully represented in our loops, and pedestrians might take advantage of lulls. In all three experiments, comparisons were made between an idling gasoline engine signal and a signal being considered by an automobile manufacturer as a sound to be played through a loudspeaker on an electric car. There were few differences between these signals, the main one being in Experiment 3, wherein the signal-to-noise ratio needed for the electric signal to be detected was lower than for the gasoline signal (Figure 8). This difference probably occurred because the frequency spectrum (see Figure 4) of the gasoline engine sound was more similar to the background traffic noise than was that of the electric car


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signal. If a substantial portion of the vehicle fleet were to emit a standardized add-on sound, this advantage would likely disappear.


Policy Implications Most research related to the quiet-car issue has focused on relative differences between hybrid (or electric) and gasoline vehicles, with respect to how much sound they emit and at what approaching distance they can be detected. This work, reviewed in the Introduction, has shown that vehicles operating in electric mode at low speed tend to be quieter and therefore detectable only from closer distances than vehicles on similar body platforms operating in internal combustion mode. However, findings from the present article suggest that in moderately busy traffic situations, it is very difficult to listen for the motion trajectory of a single vehicle and even to detect the presence of one. Increasing the sound level from individual vehicles is unlikely to solve this problem, because the overall traffic sound level would increase commensurately.


The participants in these experiments were young adults with no visual or auditory impairments. Possibly persons with visual impairments would perform differently. For example, they might distinguish straight from turning paths better than sighted persons because of experience using hearing in traffic settings. In one relevant study by Kim et al. (2011), blind participants with experience as pedestrians judged whether real vehicles, after starting from a stop, went straight or turned in front of them. Given the latency to make this judgment, the turning vehicles tended to be directly in front of the participants when the judgment was made. In contrast, participants in our experiments made the turn-or-straight distinction when the simulated turning vehicles were much earlier in the turn, about 70° to the side. Although direct comparison across studies is complicated by differences in method, the findings from Kim et al. (2011) do not support the idea that blind participants would do better than sighted participants. Similarly, Guth et al. (1989) reported that blind and sighted participants did not differ in their accuracy of alignment for a street crossing on the basis of traffic sounds. Also, with respect to our finding that performance was adversely affected by even moderate background traffic noise, it seems unlikely that blind and sighted persons would differ in basic signal-to-noise requirements.


Another consideration is that in real traffic settings, pedestrians may benefit from factors such as traffic surges in response to signals, sound reflections off of signs and other objects, and lulls in overall traffic noise. On the other hand, real-world settings also introduce competing factors, such as ambient music and voices, wind noise, and cognitive demands associated with actual crossing of the street. In summary, one need in future work is to compare performance of sighted and blind persons, ideally in both simulated and real traffic settings, such as reporting on turning vehicles at intersections. The signal-to-noise ratio required to detect whether a target vehicle was present was lower than that required for discriminating turn paths, by a difference of about 11 dB. This finding suggests that a policy of increasing vehicle sound levels to achieve detectability might provide little benefit with respect to decisions that pedestrians must make about patterns of vehicle movement. There might even be an unintended adverse impact if a requirement for a minimum vehicle sound level resulted in an increase in overall traffic noise. Public policy should be informed by consideration of a wider range of pedestrian decision making than just detecting a single vehicle approaching in a quiet background. Making vehicles more detectable in quiet settings will not necessarily translate into providing pedestrians with useful acoustic information in situations in which many vehicles are operating.


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Any recommendation to establish a standard for vehicle sound should be based on a demonstrable benefit to pedestrian safety. Current proposals to match electric vehicle sound level to that of gasoline vehicles have intuitive appeal but rest on the assumption that such a sound level is very useful. The present findings, as well as interpretation of studies covered in the Introduction to this article, suggest this assumption is questionable. Nevertheless, the concept of adding sound to vehicles could provide opportunities to provide information not readily available from gasoline vehicles.


For example, proposals for add-on sound include a provision for “pitch bending,” such that the overall frequency composition of the emitted sound covaries with vehicle speed. This feature could not be included in our simulation for proprietary reasons, but it might serve as a more reliable cue to vehicle speed than does gasoline engine sound. Likewise, if there were a unique sound when a vehicle is stationary, it could help cue that a driver is yielding for a pedestrian or that a traffic surge has begun (as the stationary signals switch to motion signals). Still another possibility is that an identifiable sound event would occur when the steering mechanism reaches a certain deviation from straight ahead. These kinds of proposals would need to be standardized, would necessitate education about what the various sounds mean, and would require research to evaluate the potential to enhance pedestrian safety. A formidable challenge for any of these features is the signal-to-noise issue. That is, in a traffic setting with many vehicles present, it is very difficult to hear what is going on with a single vehicle. There are alternative technological approaches to the quiet-car problem besides adding sound to vehicles. The principle of universal design suggests that technology should be available to the widest possible range of users, without the need for individuals to engage in atypical maneuvers or carry specialized devices (Barlow, Bentzen, & Franck, 2010). Traffic engineers use inductive ground loops and camera-based image processing to monitor vehicle activity. Vehicle-to-vehicle short-range wireless communication (Biswas, Tatchikou, & Dion, 2006) is already in partial use and will be adapted to provide information for traffic control, possibly including information for pedestrians. The widespread use of smartphones and a culture for using applications on them will provide a way to communicate information to pedestrians at their request. Efforts based on these kinds of technology face two kinds of challenges. One is to provide pedestrians with precise information needed to make decisions in busy traffic settings, which requires high spatial accuracy and rapid updating, in a context in which many vehicles must be monitored. The second challenge is the imperative to provide the full range of pedestrians with information they need, in a form they can use, to help them make safe decisions.


Acknowledgments This investigation was supported by Grant No. R01- EY12894 of the National Institutes of Health, National Eye Institute. Support also came from a research contract, “Children’s Perception of Moving Sound Sources,” from Nissan Technical Center North America. The contents are solely the responsibility of the authors and do not necessarily represent the official views of the National Institutes of Health or Nissan. Thanks to Heather Konet for discussions throughout the project and to the editor and three anonymous reviewers for helpful suggestions.

REFERENCES Ashmead DH, Guth D, Wall RS, Long RG, Ponchillia PE. Street crossing by sighted and blind pedestrians at a modern roundabout. Journal of Transportation Engineering. 2005; 131:812–821. Bach DR, Neuhoff JG, Perrig W, Seifritz E. Looming sounds as warning signals: The function of motion cues. International Journal of Psychophysiology. 2009; 74:28–33. [PubMed: 19615414]


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NIH-PA Author Manuscript NIH-PA Author Manuscript NIH-PA Author Manuscript

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Mauchly JW. Significance test for sphericity of a normal n-variate distribution. Annals of Mathematical Statistics. 1940; 11:204–209. McBeath MK, Neuhoff JG. The Doppler effect is not what you think it is: Dramatic pitch change due to dynamic intensity change. Psychonomic Bulletin Review. 2002; 9:306–313. [PubMed: 12120793] Miedema HME. Annoyance caused by environmental noise: Elements for evidence-based noise policies. Journal of Social Issues. 2007; 63:41–57. Mills AW. On the minimum audible angle. Journal of the Acoustical Society of America. 1958; 30:237–246. Moore DB. The revised ISO 362 standard for vehicle exterior noise measurement. Sound and Vibration. 2006 Oct.:19–23. National Federation of the Blind. Committee on Automobile and Pedestrian Safety/Quiet Cars. 2010. Retrieved from http://quietcars.nfb.org/ Pedestrian Safety Enhancement Act of 2010, Pub. L. 2010:111–373. Perrott DR, Tucker J. Minimum audible movement angle as a function of signal frequency and the velocity of the source. Journal of the Acoustical Society of America. 1988; 83:1522–1527. [PubMed: 3372867] Pulkki V, Karjalainen M. Localization of amplitude-panned virtual sources: 1. Stereophonic panning. Journal of the Audio Engineering Society. 2001; 49:739–752. Robart RL, Rosenblum LD. Are hybrid cars too quiet? Journal of the Acoustical Society of America. 2009; 125:2774. Rosenblum LD, Wuestefeld AP, Saldana HM. Auditory looming perception: Influences on anticipatory judgments. Perception. 1993; 22:1467–1482. [PubMed: 8090622] Roudsari B, Kaufman R, Koepsell T. Turning at intersections and pedestrian injuries. Traffic Injury Prevention. 2006; 7:283–289. [PubMed: 16990243] Sandberg, U.; Ejsmont, JA. Tyre/Road noise reference book. Kisa, Sweden: Informex; 2002. Szeto AY, Valerio NC, Novak RE. Audible pedestrian signals: Part 3. Detectability. Journal of Rehabilitation Research and Development. 1991; 28:71–78. [PubMed: 1829762] U.S. Noise Pollution and Abatement Act of 1972, Pub. L. 86 Stat. 1234, 42 U.S.C. § 4901–42 U.S.C. § 4918. 1972 Wall RS, Ashmead DH, Bentzen BL, Barlow J. Directional guidance from audible pedestrian signals for street crossing. Ergonomics. 2004; 47:1318–1338. [PubMed: 15370850] Wall Emerson R, Naghshineh K, Hapeman J, Wiener W. A pilot study of pedestrians with visual impairments detecting traffic gaps and surges containing hybrid vehicles. Transportation Research Part F. 2011; 14:117–127. Wall Emerson R, Sauerburger D. Detecting approaching vehicles at streets with no traffic control. Journal of Visual Impairment & Blindness. 2008; 102:747–760. [PubMed: 20126414] Wiener WR, Lawson G, Naghshineh K, Brown J, Bischoff A, Toth A. The use of traffic sounds to make street crossings by persons who are visually impaired. Journal of Visual Impairment & Blindness. 1997; 91:435–445. Wiener WR, Naghshineh K, Salisbury B, Rozema J. The impact of hybrid vehicles on street crossings. Journal of RE:view: Rehabilitation and Education for Blindness and Visual Impairment. 2006; 38(2):65–78. Wiener, WR.; Walsh, RL.; Blasch, BB., editors. Foundations of orientation and mobility: Vol. 2, Instructional strategies and practical applications. 3rd ed. New York, NY: AFB Press; 2010.

Biographies Daniel H. Ashmead is a professor in the Department of Hearing and Speech Sciences at Vanderbilt University School of Medicine. He received a PhD in developmental psychology from the University of Minnesota in 1983.


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D. Wesley Grantham is a professor in the Department of Hearing and Speech Sciences at Vanderbilt University School of Medicine. He received a PhD in psychology from Indiana University in 1975. Erin S. Maloff is a clinical applications specialist at Cochlear Americas. She received a PhD in audiology from Vanderbilt University in 2010. Benjamin Hornsby is an assistant professor in the Department of Hearing and Speech Sciences at Vanderbilt University School of Medicine. He received a PhD in hearing science from Vanderbilt University in 2002. Takabun Nakamura is an associate professor in the Department of Welfare System and Health Science, Faculty of Health and Welfare Science, at Okayama Prefectural University, Japan. He received a PhD in electronic engineering from Shizuoka University, Japan, in 1981. Timothy J. Davis is an audiology graduate student at Vanderbilt University School of Medicine. He received a BA in communication sciences and disorders from Western Washington University in 2009.


Faith Pampel is a graduate student in the Department of Hearing and Speech Sciences at Vanderbilt University School of Medicine. She received a BA in linguistics from Swarthmore College in 2009. Erin G. Rushing is a graduate student in the audiology program in the Department of Communication Disorders, School of Allied Health, at Louisiana State University Health Sciences Center. She received an MS in biology from the University of Louisiana at Lafayette in 2007.

NIH-PA Author Manuscript Hum Factors. Author manuscript; available in PMC 2012 September 21.

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KEY POINTS


•

Quiet cars include vehicles of all engine types, and concern has been expressed that low sound emissions may make it difficult for pedestrians to make safe road-crossing decisions.

•

In listening situations typical of moderately busy traffic settings, it is very difficult to perceive whether an individual vehicle is traveling straight or turning, even for internal combustion engine vehicles.

•

The signal-to-noise ratio required to make decisions about a vehicle’s travel path is substantially higher than the ratio needed just to detect the vehicle.

•

Increasing vehicle sound output to a minimum standard would not necessarily enhance pedestrian safety in situations in which multiple vehicles are present, because doing so would tend to increase the overall traffic noise level.

NIH-PA Author Manuscript NIH-PA Author Manuscript Hum Factors. Author manuscript; available in PMC 2012 September 21.

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Figure 1.

Illustration of straight and turn paths. Paths started at a variable position, proceeding northward along the north-south street. Solid lines show vehicle centerline travel paths along the streets. Path length was measured from a nominal position at (0,−8). Open circles show 1-m increments along the paths. The turn path has a circular radius of 6 m, with the turn starting at (0,−6). Listener position is at (3,−3), with listener facing north, as though waiting to cross the east-west street. Dashed circle indicates where the laboratory loudspeakers were located.


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Figure 2.

Examples of velocity profiles for the turn (dashed lines) and straight (solid lines) motion paths at lower and higher velocity ranges. The paths had variable starting positions and lengths and are plotted with zero along the x-axis (dotted vertical line), corresponding to halfway through the turn on a turn path or the intersection of the north-south and east-west traffic lanes on a straight path. Although these examples do not show the full diversity of the velocity profiles that were possible, they do convey that there was generally more deceleration (and subsequently acceleration) on turn paths than on straight paths, as is true of real traffic.


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Figure 3.

Plot of sound level (relative to level at nominal start of path) and direction for straight and turn paths, each 15 m long. Left ends of functions correspond to nominal start of path at point (0,−8) in Figure 1. Direction is with respect to listener’s straight-ahead direction, with negative values leftward. Circles mark 1-m increments along each path. The double-ended arrow shows the average threshold path length of 5.91 m from Experiment 1. At this point, the straight and turn paths differed by 4.2 dB in sound level but only 1.8° in direction.


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NIH-PA Author Manuscript NIH-PA Author Manuscript

Figure 4.

Spectra of the digital signals of the electric and gasoline vehicle sounds.


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Figure 5.

Experiment 1 mean threshold path lengths for discriminating between straight and turning vehicle paths in a quiet background. Signal conditions were the electric or gasoline vehicle sounds, played at levels of 52 or 65 dB, A-scale (sound levels are offset slightly to separate plot symbols for vehicle types). Error bars show 95% confidence intervals based on the approach of Hollands and Jarmasz (2010).


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NIH-PA Author Manuscript NIH-PA Author Manuscript

Figure 6.

Spectrum of digital signal of background traffic noise.


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Figure 7.

Experiment 2 proportion of threshold runs completed for gasoline and electric car signals in four background noise conditions. Runs not completed were terminated because participants consistently failed to discriminate straight from turn paths even at the upper limit on path length, 10 m.


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Figure 8.

Mean signal-to-noise thresholds by type of vehicle sound and task. Error bars show 95% confidence intervals.


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