Driver Head Movements in Left Outside Mirror Viewing

Bhise, Vivek D.; Meldrum, James F.; Jack, Daniel D.; Troell, George M.; Hoffmeister, David H.; Forbes, Lyman M.

doi:10.4271/810761

Cited by 9 publications

(9 citation statements)

References 1 publication

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“…With a limited data set, it is difficult to train a classifier to adapt to each driver's own style of eye glancing prior to lane changes. Head pose movements, however, occur in a more telling manner across the population; this pattern extends from the general results of Bhise et al [16]. This property makes head dynamics a more reliable metric for inferring driver intent.…”

Section: A Discussionsupporting

confidence: 59%

“…Bhise et al [16] studied a series of naturalistic lane changes in real-world settings and discovered that most visual searches prior to lane changes involve head motions and relatively very few (5.4% in their study) involve eye glance alone. Furthermore, Robinson et al [17] found a remarkably stable relationship between eye glance behavior and head movement behavior: In an experiment where a visual fixation was placed at 60…”

Section: A Related Research In Lane Change Behavior Analysismentioning

confidence: 99%

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On the Roles of Eye Gaze and Head Dynamics in Predicting Driver's Intent to Change Lanes

Doshi

Trivedi

2009

IEEE Trans. Intell. Transport. Syst.

244

124

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Abstract-Driver behavioral cues may present a rich source of information and feedback for future intelligent advanced driver-assistance systems (ADASs). With the design of a simple and robust ADAS in mind, we are interested in determining the most important driver cues for distinguishing driver intent. Eye gaze may provide a more accurate proxy than head movement for determining driver attention, whereas the measurement of head motion is less cumbersome and more reliable in harsh driving conditions. We use a lane-change intent-prediction system Index Terms-Driver-assistance systems, driver behavior, driver intent inference, intelligent vehicles, machine vision, sparse Bayesian learning.

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Section: A Discussionsupporting

confidence: 59%

Section: A Related Research In Lane Change Behavior Analysismentioning

confidence: 99%

On the Roles of Eye Gaze and Head Dynamics in Predicting Driver's Intent to Change Lanes

Doshi

Trivedi

2009

IEEE Trans. Intell. Transport. Syst.

244

124

View full text Add to dashboard Cite

show abstract

“…The use of obtrusive equipment, such as eye-markers, helmets, and large visible video cameras (Burger, Mulholland, Smith, Sharkey & Bardales, 1980;Godhelp, 1985;Mourant, Rockwell & Rackoff, 1969;Robinson, Erickson, Thurston & Clark, 1972), may constrict driver behavior by making the driving task unnatural. In most studies an experimenter accompanied the driver and gave driving instructions during data collection (Robinson et al;Bhise et al, 1981;Hetrick, 1997;Tijerina, Garrott, Glecker, Stoltzfus & Parmer, 1997). It is believed that the presence of an experimenter may influence driver behavior, resulting in a lack of natural driving behavior.…”

Section: Limitations Of Previous Lane Change Effortsmentioning

confidence: 99%

A Comprehensive Examination of Naturalistic Lane-Changes

Lee¹,

Olsen²,

Wierwille³

2004

PsycEXTRA Dataset

169

125

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Michael Goodman was the NHTSA Task Order Manager for this project. 16. Abstract This research effort provided valuable insight into the nature and severity of lane changes in a naturalistic driving environment. Sixteen commuters who normally drove more than 25 miles (40 km) in each direction participated. The two research vehicles were a sedan and an SUV; each participant drove each vehicle for ten days. Data gathering was automatic, and no experimenter was present in the vehicle. There were 8,667 lane changes observed over 23,949 miles of driving, making this the largest known data collection effort for the study of lane changes. Analysis of the full data set resulted in many interesting findings regarding the frequency, duration, urgency, and severity of lane changes in regard to maneuver type, direction, and other classification variables. A subset of the full data set (500 lane changes) was then analyzed in greater depth using the sensor data collected by the instrumented vehicle. The sampled lane changes were generally of the more severe and urgent types since these are the cases in which a lane change collision avoidance system is likely to be of greatest help.Variables analyzed for the sampled lane changes included turn signal use, braking behavior, steering behavior, eye glance patterns, and forward and rearward area analysis. The concept of a safety envelope for lane changes was then developed using the forward and rearward area analyses. Finally, the data were used to provide recommendations for designers of lane change CAS in terms of display location and activation criteria. Overall, the research described in this report provides insight into the behaviors and parameters associated with lane changes, while the naturalistic data archive has the potential to address other questions related to driving behavior.17.

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“…Furthermore, the amount of eye movement could vary between drivers. Head pose movements have been shown to occur in a more telling manner across the population [17]. Finally, the timing of eye glances could happen at different moments prior to the lane change compared to the head motions.…”

Section: A Discussionmentioning

confidence: 99%

“…Bhise et al [17] studied a series of naturalistic lane changes in real world settings, and discovered that most visual searches prior to lane changes involve head motions; only 5.4% of the searches involve eye glance alone. However Robinson also found a remarkably stable relationship between eye glance behavior and head movement behavior:…”

Section: A Lane Changes and Driver Visual Searchmentioning

confidence: 99%

A comparative exploration of eye gaze and head motion cues for lane change intent prediction

Doshi

Trivedi

2008

2008 IEEE Intelligent Vehicles Symposium

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Abstract-Driver behavioral cues may present a rich source of information and feedback for future Intelligent Driver Assistance Systems (IDAS). Two of the most useful cues might be eye gaze and head motion. Eye gaze provides a more accurate proxy than head motion for determining driver attention, whereas the measurement of head motion head motion as a derivative of pose is less cumbersome and more reliable in harsh driving conditions. With the design of a simple and robust IDAS in mind, we are interested in determining the most important driver cues for distinguishing Driver Intent. We use a lane change intent prediction system [1] to determine the relative usefulness of each cue for determining intent. Various combinations of input data are presented to a discriminative classifier, which is trained to output a prediction of probable lane change maneuver at a particular point in the future. Quantitative results using realworld data are presented and show that head motion, when combined with lane position and vehicle dynamics, is a reliable cue for lane change intent prediction. The addition of eye gaze does not improve performance as much as simpler head posebased cues.

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Driver Head Movements in Left Outside Mirror Viewing

Cited by 9 publications

References 1 publication

On the Roles of Eye Gaze and Head Dynamics in Predicting Driver's Intent to Change Lanes

On the Roles of Eye Gaze and Head Dynamics in Predicting Driver's Intent to Change Lanes

A Comprehensive Examination of Naturalistic Lane-Changes

A comparative exploration of eye gaze and head motion cues for lane change intent prediction

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