Methods for Automated Identification of Informative Behaviors in Natural Bioptic Driving

Luo, Gang; Peli, Eli

doi:10.1109/tbme.2012.2191406

Cited by 5 publications

(4 citation statements)

References 22 publications

(19 reference statements)

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“…Video clips were selected from a driving video inventory composed of 765 five-second driving video clips. The inventory was built from naturalistic driving videos recorded in the Boston, MA area using a digital video recorder (DVR) [ 19 – 20 ]. Vehicle speed was recorded by the DVR.…”

Section: Methodsmentioning

confidence: 99%

An investigation of perceived vehicle speed from a driver's perspective

Doherty

et al. 2017

PLoS ONE

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PurposeSpeed estimation of drivers’ own vehicles and other vehicles on the road is an important task for drivers and is also crucial to the roadway safety. The objective of the study was to examine the effects of multiple factors such as image scale, speed, road type, driving experience, and gender on the speed perception of drivers’ own vehicles.MethodsThirty participants consisted of 17 males and 13 females, including 13 without driving experience. All participants estimated the driving speed of 192 5-second video clips, which were selected from naturalistic driving recordings. The recorded driving speeds were evenly distributed across the entire range from 5mph to 65mph. Half of the selected video clips were recorded on wide roads and another half were recorded on comparatively narrow roads. Video clips were played on a large screen, with each clip shown in one of 4 image scales (100%, 75%, 50%, and 38% of the actual field of view in the real world).ResultsSpeed estimates were most accurate for the smallest image size (38% of the actual field of view). As the image size increased, the driving speed was increasingly underestimated. Participants with driving experience accurately estimated the driving speed on both wide and narrow roads whereas those without driving experience had greater underestimates on wider roads. Speeds were most accurately estimated within the range 25-35mph, but the speeds slower than the range tend to be overestimated and the speeds faster than the range are more likely to be underestimated. While males and females showed the same pattern across speed groups, females have greater estimation errors at the highest and lowest speed groups. Participants without driving experience showed increasing underestimation of speed as driving speed increased whereas participants with driving experience primarily underestimated the highest speeds.ConclusionsThe present study shows the effect of multidimensional influential factors on perceived vehicle speed from drivers’ perspective. The results also have implications for driving simulation scenario design, driving simulator setup, and the assessment of speed control in simulated and naturalistic environments.

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Section: Methodsmentioning

confidence: 99%

An investigation of perceived vehicle speed from a driver's perspective

Doherty

et al. 2017

PLoS ONE

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“…To address the limitations of conventional paradigms, naturalistic driving studies have been increasingly conducted. Since the first naturalistic driving study was launched, 31 naturalistic recordings have been used to document daily driving of different cohorts, including younger or older drivers, 8,11,43 visually or cognitively impaired drivers, 11,25 and passenger car or truck drivers. 32,30,38 …”

Section: Introductionmentioning

confidence: 99%

“…12,30 In West et al ’s study, 43 software was used to mark the video segments recorded at intersections. To study the driving of visually impaired people using bioptic telescopes, Luo et al 25 developed automated identification techniques for driving events such as the use of the telescope or unsteady steering. To be effective, automated detection methods must have a high sensitivity for important events and a high data reduction rate to facilitate manual offline review of relevant segments only.…”

Section: Introductionmentioning

confidence: 99%

“…weaving), accelerometer-based methods may be easy to implement and work well in some cases. 10,18,25 However, these methods are not suitable for lane-change detection, because most lane changes are smooth making the signature signal within the acceleration data weak. Advanced Driver Assistance Systems (ADAS) usually use vision-based lane-change or lane-departure detection methods, which have been applied in commercial vehicles 4,16,17,23,45 However, these methods require high-resolution lane markers, typically in good road conditions.…”

Section: Introductionmentioning

confidence: 99%

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Detection of Lane-Change Events in Naturalistic Driving Videos

Wang

Ott

Luo

2018

Int. J. Patt. Recogn. Artif. Intell.

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Lane changes are important behaviors to study in driving research. Automated detection of lane-change events is required to address the need for data reduction of a vast amount of naturalistic driving videos. This paper presents a method to deal with weak lane-marker patterns as small as a couple of pixels wide. The proposed method is novel in its approach to detecting lane-change events by accumulating lane-marker candidates over time. Since the proposed method tracks lane markers in temporal domain, it is robust to low resolution and many different kinds of interferences. The proposed technique was tested using 490 h of naturalistic driving videos collected from 63 drivers. The lane-change events in a 10-h video set were first manually coded and compared with the outcome of the automated method. The method’s sensitivity was 94.8% and the data reduction rate was 93.6%. The automated procedure was further evaluated using the remaining 480-h driving videos. The data reduction rate was 97.4%. All 4971 detected events were manually reviewed and classified as either true or false lane-change events. Bootstrapping showed that the false discovery rate from the larger data set was not significantly different from that of the 10-h manually coded data set. This study demonstrated that the temporal processing of lane markers is an effcient strategy for detecting lane-change events involving weak lane-marker patterns in naturalistic driving.

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Evaluation of the Driving Safety of Visually Impaired Bioptic Drivers Based on Critical Events in Naturalistic Driving

Moharrer

Wang

Dougherty

et al. 2020

Trans. Vis. Sci. Tech.

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Visually impaired people may be allowed to drive if they wear bioptic telescopes. Bioptic driving safety is debatable, especially given that the telescopes are seldom used by most bioptic drivers. This preliminary study examined bioptic safety based on critical events that occurred in naturalistic daily driving. Methods: Daily driving activities were recorded using in-car video recorders in 20 bioptic drivers (median age 55, visual acuity, 20/60-160) and 19 control subjects (median age 74) for two to eight weeks. In a secondary analysis, these subjects were compared with 44 cognitively impaired drivers with normal vision (median age 75). Results: In 292 hours of driving by bioptic drivers and 169 hours by control drivers, seven bioptic drivers and three control drivers had eight and four near-collisions, respectively. Near-collision survival times were not significantly different between the two groups (hazard ratio [HR] = 1.93, P = 0.591) according to Cox hazards regression. Even without compensation for bioptic drivers' longer driving exposure, their odds ratio (OR) was not statistically significant (OR = 2.88, P = 0.18). When including cognitively impaired drivers with normal vision, cognition was a significant predictor of near collisions (HR = 3.86, P = 0.036), but vision loss was not (HR = 0.47, P = 0.317). Conclusions: This preliminary study failed to find any evidence suggesting that bioptic drivers were more prone to near-collision than healthy drivers. Vision might be a lesssignificant factor than cognition. Translational Relevance: Given that bioptic drivers use the telescope for less than 2% of the driving time, this study suggests that driving safety might not be substantially affected even when visual acuity is in the low vision range.

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Methods for Automated Identification of Informative Behaviors in Natural Bioptic Driving

Cited by 5 publications

References 22 publications

An investigation of perceived vehicle speed from a driver's perspective

An investigation of perceived vehicle speed from a driver's perspective

Detection of Lane-Change Events in Naturalistic Driving Videos

Evaluation of the Driving Safety of Visually Impaired Bioptic Drivers Based on Critical Events in Naturalistic Driving

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