Paper Number

Driver Distraction Monitoring and Adaptive Safety Warning Systems Riad I. Hammoud

Matthew R. Smith Robert Dufour

Deborah Bakowski Gerald Witt

Delphi E&S, Kokomo, Indiana, USA, [email protected], [email protected] Copyright © 2008 SAE International

ABSTRACT This paper addresses the issue of driving while distracted and presents a frontal/non-frontal head posebased driver distraction alert system along with its integration with conventional Lane Departure Warning and Forward Collision Warning systems. It overviews the core algorithmic building blocks of these systems while reporting the experimental results obtained on a diverse and challenging set of subjects and environmental driving conditions.

the coming years because increasingly elaborate nomadic devices (e.g., cell phones, navigation systems, wireless Internet and email devices) are being brought into vehicles that may increasingly compromise safety. From the aforementioned issues, a good system will regulate safety warnings based upon the drivers’ visual distraction state. With such intelligence, both driver acceptance and system effectiveness will be substantially boosted. This paper will focus on these two aspects.

2. ON DRIVER DISTRACTION MEASURES 1. INTRODUCTION Nowadays, vehicles are equipped with active safety systems including Forward Collision Warning (FCW) and Lane Departure Warning (LDW) systems. Typical Forward Collision Warning system utilizes radar to sense obstacles in front of the host vehicle and alerts the driver when there is an imminent threat of collision, and Lane Departure Warning system utilizes vision processing to alert the driver when the host vehicle strays across a lane boundary. These active safety systems provide warnings to vehicles drivers regardless of the drivers’ visual distraction state. Yet, these warnings benefit the driver only when they are distracted. Otherwise, they represent a source of annoyance to the driver. While driving, drivers get engaged into different sorts of behaviors including glancing on a peripheral display or on an object away from the driving forward region (visual distraction), releasing the steering wheel to adjust the radio (manual distraction), and using auditory e-mail systems or holding hand-free cell phone conversations (mental/ cognitive distraction) [7, 9, 10]. Whether the distraction is manual, visual and/or cognitive, the driving performance degrades in terms of slower reaction time to unpredictable and potentially dangerous events including traffic signals and brake events [4, 9, 6]. These distractions are classified as the cause of many, possibly avoidable accidents. A 2-second window has been identified as being long enough for an attentive driver to avoid a collision [5]. The 100-Car study estimated that distraction may contribute to more than three quarters of all crashes [2], and visual inattention alone may have contributed to 93% of rear-end-striking crashes. The issue of driver distraction may become more critical in

Although many research-intent driver monitoring technologies are able to monitor eye gaze, detecting eye-gaze is a difficult task using affordable automotivegrade technology. From a technology perspective monitoring the head pose is more feasible than monitoring eye gazes. Figure 1 demonstrates how head pose and eye-gaze measures correlate with the driver performance measures: accelerator release time (ART) and standard deviation of lane position (SDLP) [4]. Although the eye-gaze measures are able to capture slightly more of the variance than head-pose measures, head pose still provides a good estimate of driver distraction, especially for SDLP.

Figure 1. Correlations between head pose or eye gaze and performance measures: Accelerator Release Time (ART) and Standard Deviation of Lane Position (SDLP).

In section 4, we present our non-contact video-based Frontal/Non-Frontal Driver Face Forward detection system, along with the experimental setup and testing results.

3. TOWARD ADAPTIVE SAFETY WARNING SYSTEMS The adaptive active safety warning systems differ from their conventional counterpart in that they utilize information about the driver's head pose in order to tailor the warnings to the driver's visual attention. For instance, the National Highway Transportation Safety Administration (NHTSA) funded the SAfety VEhicle(s) using adaptive Interface Technology (SAVE-IT) program [9]. The SAVE-IT program was a five-year research and development program that developed and tested two branches of countermeasures, distraction mitigation countermeasures that seek to directly reduce the amount of distraction, and adaptive warnings that seek to reduce the negative impact of distraction. The research done in the NTHSA sponsored SAVE-IT program demonstrated that tailoring alerts to the driver's visual distraction can help alleviate the tradeoff between providing sufficient warning during distracted episodes and annoying drivers when they do not need the warnings. By avoiding this tradeoff, the collisionreduction effectiveness of forward collision warning was increased and the acceptance of both FCW and lane departure warning was improved by reducing the number of alerts during periods of visually attentive driving.

Data from the 100-Car Study backs up the fact that drivers who are attentive to driving do not benefit from active safety warnings, and that crashes usually result when the sudden emergence of an unexpected situation that coincides with a driver's distraction from the forward scene [2, 3]. This result indicates that inattention to the forward roadway is an important contributing factor in lead-vehicle and single-vehicle crashes, perhaps even to a greater extent than conventional crash statistics had implied [1]. The visual distraction acts as a catalyst for a crash by interfering with the driver's response, converting a mere incident, or near-miss, into a collision. In sections 5 and 6 we describe in details these adaptive safety countermeasures along with the experimental setup and testing results. Finally, this paper is concluded in section 5. Figure 2 displays a conceptual overview of the SAVE-IT system in which two branches of countermeasures, Adaptive Warnings and Distraction Mitigation, accept input from the two sources of adaptive inputs, Head Pose, and Driving Task Demand. Whereas the Adaptive Warnings attempt to reduce the negative effects of driver distraction, the Distraction Mitigation countermeasures attempt to directly reduce driver distraction. The Adaptive Warnings include Adaptive Forward Collision Warning (AFCW) and Adaptive Lane Departure Warning (ALDW), which are like their non-adaptive counterparts, except that they also respond to the driver's Head Pose. Although production AFCW and ALDW systems would also need to adapt to driver drowsiness, drowsiness was defined as beyond the scope of the SAVE-IT program and was therefore not investigated. Forward Collision Warning (FCW) and Lane Departure Warning (LDW) were selected for adaptation because an analysis of collisions suggested that distraction most impacted the events that these systems attempt to prevent. The Distraction Mitigation countermeasures include Trip Report, which provides post-hoc feedback at the end of each trip regarding safety-relevant events and the driver's Head Pose, Adaptive Infotainment Availability and Advisory, which either lock out or advise against infotainment features when the Driving Task Demand is too high for the given feature to be performed safely, and Adaptive Phone Management, which when placed in auto-screen mode, screens calls to voicemail when the Driving Task Demand is too high for the phone to be answered safely.

4. HEAD POSE-BASED DISTRACTION ALERT SYSTEM

Figure 2. Countermeasures as a function of adaptation inputs.

The proposed head pose detection algorithm here takes as input either the locations of eyes of the face box. Sub-routines have been developed to locate and track the eye positions in the raw video images when the eyes are visible (i.e. the case of no dark sunglasses). Otherwise, an alternative algorithm attempts to locate the head/face regardless the eye visibility. Neither approach requires any calibrations. Below (figure 3) some examples of eye and face detection in presence of no-glasses, prescription glasses and NOIR sun-glasses.

A training set has been collected offline and used to build a face-forward head model and face-non-forward head pose. The face forward region is defined between +/- 20 degrees as shown in figure 3.

Figure 3. Illustration of results - featureless head posebased driver alert detection.

Figure 4. Example of one user interface used during testing and evaluation.

controlled. Most testing occurred throughout the day between eight am and six pm from December 2006 through December 2007. The same vehicle, a Volvo S60, was used for all testing. Seventy-eight participants from central Indiana (46% females) were tested in accordance to the procedures described below. The average height for the population tested was sixty-six inches (SD = 3.7 inches). The sample was ethnically diverse (52% Asian, 37% White, 6% African-American and Hispanic and 5% Indian). The subjects were asked about their eye wear status. Those subjects who needed prescription glasses in order to drive were asked to wear them during the tests. Those who preferred to wear sunglasses or those who needed to wear sunglasses given the time of the day at test were asked to wear them. Prior to the start of testing, the vehicle was parked and markers were installed at key, measured location in the forward periphery from the driver-side window to the passenger-side window. Each subject was asked to face the center position (marked as zero on the windshield, illustrated at the top of figure 4) and then to turn their head to the marker specified by the experimenter. Subjects were asked to complete the turn with their head, using their nose as a pointer so as to face the called-out marker in the same fashion as they faced the center marker. At the end of the turn, the subjects were asked to hold that position for approximately five seconds and then to return to the center position. Each driver performed twelve head turns and this procedure was repeated twice. In this analysis, all markers within |17°| of center were considered forward markers (bottom left picture of figure 4). Markers outside of |25°| of center were considered non-forward markers (bottom right picture in figure 4). The results are presented in Table 1. Table 1. Driver Distraction Status Head Turn test results in stationary vehicle.

Figure 5. Forward/non-forward head pose definition. The test was designed to provide controlled and baseline measurements of the system’s capacity to track driver head pose. Given these goals, the test reported here was conducted while the vehicle was parked. Time of day and meteorological conditions were not

The analyses of variance looking at eyewear status and type (3 levels, described above), age (4 levels: 20s, 30s, 40s and 50s), height (2 levels: short and tall) and sex (2 levels) for forward and non-forward markers failed to yield any significant differences (all p>.10). The results indicate that the variation in performance reported across the three eyewear status and type groups is not statistically significant. Statistically, the system works uniformly across eye wear status. Furthermore, an analysis of forward markers restricted to markers within |15°| of the center marker shows that performance increases past 95% for all three groups. In summary, as a baseline measure and in a stationary vehicle, the head pose system can reliably distinguish

between forward and non-forward pose. This information can then be used to inform alert system as to the position of the driver’s head during the driving task.

5. Adaptive Forward Collision Warning . Like traditional Forward Collision Warning (FCW) systems, the proposed Adaptive Forward Collision Warning (AFCW) system utilizes the Delphi forwardlooking radar to assess the imminent threat of colliding with the lead vehicle. However, unlike FCW, AFCW utilized instantaneous head pose to adapt the timing of the alerts to the driver's attentiveness to the forward scene. Table 2 displays the final system configuration for Adaptive FCW that was tested in the SAVE-IT evaluation. After investigating the different adaptation alternatives, the Differential Alert Timing strategy appeared to be the most promising. This implementation was selected in order to relieve the usual tradeoff faced by FCW systems between providing distracted drivers with sufficient time to respond while not annoying drivers with unnecessary alerts during attentive periods of driving.

Table 2. Adaptive and Non-Adaptive mode Forward Collision Warning as a Function of Driver Head Pose. ADAPTIVE MODE Dependent on DSM Head Pose Forward Host Vehicle Not Braking

Delayed Alert

Host Vehicle Braking

No Alert

Not Forward

Not Forward Extended

Early Alert

Very Early Alert Delayed Alert

NONADAPTIVE MODE

6. Adaptive Lane Departure Warning Like traditional Lane Departure Warning (LDW) systems, the proposed Adaptive Lane Departure Warning (ALDW) system utilizes the Delphi forward-looking camera and vision processing to detect lane position and alert the driver when the lane was crossed. In the system, alerts were completely suppressed when the driver’s head pose was forward (the “Alert Suppression” strategy of Table 3). Because of the prevalence of rapid head pose movements just prior to a lane crossing resulting from mirror checks, ALDW required a distinction between brief head pose movements and extended periods of away head pose. Only when the driver’s head pose had been away for an extended period of time, were alerts presented to the driver. For an ALDW alert to be generated, the driver’s head pose needed to be away for a minimum of 2s, and at least 1s of the head-pose away needed to occur prior to the lane crossing. This was because if drivers looked away just as they were about to cross the lane, it could be assumed that they were aware of the lane crossing.

Table 3. Adaptive and Non-Adaptive mode Lane Departure Warning as a Function of Driver Head Pose. ADAPTIVE MODE Dependent on DSM Head Pose Forward

Not Forward

Not Forward Extended

No Alert

No Alert

Allow Alert

NONADAPTIVE MODE

Allow Alert

Nominal Alert Delayed Alert

In the non-adaptive mode, the algorithm assumed a brake reaction time of 2.5s to the alert. In order to improve driver acceptance, when the driver's head pose was forward the AFCW algorithm would assume a brake reaction time of 0.5s. This late timing issued warnings very late and usually resulted in the suppression of unnecessary alerts. However, in order to provide distracted drivers with sufficient time to respond, when the driver's head pose was not forward, AFCW would produce very early alerts. When the driver's head pose was first directed away from the roadway, the AFCW brake reaction time would begin at 1.5s and then linearly increase towards 3.5 s, which would be reached at a 2-s extended away pose. The SAVE-IT FCW algorithm was tuned to provide warnings earlier than most production systems. This would allow drivers to experience FCW alerts in the limited on-road testing that was planned for the evaluation phase.

For more detailed information on how the adaptive and non-adaptive systems operated, refer to the Adaptive Safety Warning Countermeasures report [8].

7. EXPERIMENTS Test Track Results. The drivers who experienced the lead vehicle braking events on the test track were exposed to the concept of adaptation for both AFCW and ALDW. For LDW, drivers experienced scripted events that included: a non-adaptive nuisance LDW alert from a lane change, the ALDW suppression of nuisance alerts generated from lane changes, and ALDW alerts resulting from an extended non-forward head pose prior to crossing the lane marker. For FCW, in addition to experiencing two surprise braking events (one AFCW and one FCW) drivers experienced scripted events that included: non-adaptive alerts with the lead vehicle braking, non-adaptive nuisance alerts resulting from a lead vehicle changing lanes, and adaptive suppression of both lead vehicle braking and lead vehicle changing

lane events. After these and other test track trials were completed, drivers were asked to rate the usefulness of the adaptive and non-adaptive systems. Figure 6 displays the Van der Laan usefulness ratings for the adaptive and non-adaptive versions of both FCW and LDW. Although the LDW trend did not reach statistical significance, the trend did reach statistical significance for FCW, where AFCW was rated as significantly more useful than FCW.

Figure 6. Usefulness Van der Laan ratings of Adaptive and Non-adaptive FCW and LDW.

Indiana On-road Results On-road exposures were carried out in Indiana. In Indiana, LDW and FCW exposures were conducted separately using different subjects in order to simplify the testing from the subject’s point of view. The pool of naïve participants included 14 drivers who experienced AFCW and FCW and 14 drivers who experienced ALDW and LDW. The FCW drives spanned approximately 120 mi per driver, and covered a route that favored conflict-rich small-city regions while avoiding long delays at intersections. The LDW drives spanned approximately 150 mi per driver, and covered a route that spanned four-lane rural highway, six-lane interstate, and four and six lane divided highway. In both cases, drivers spent half the drive in adaptive mode and half the drive in non-adaptive mode, with an order that was counterbalanced across subjects. The drives revealed reductions in alert-frequency for the adaptive versions of both FCW and LDW. Drivers received significantly fewer alerts in the adaptive mode of both the FCW and LDW studies. The FCW and LDW warning systems demonstrated high adaptive suppression rates, approximately 70% (reducing from 7.5 to 2.2 alerts per 100 mi) and 95% (reducing from 7.8 to 0.4 alerts per 100 mi) respectively. Significantly more drivers expressed a preference for the adaptive mode in both studies (see Figure 7), and when they did so, the most common reason provided was the lower alert-rate. Ten out of the 14 FCW drivers preferred AFCW over FCW, with three drivers preferring the non-adaptive mode and one driver who was not sure. Whereas the 13

drivers who selected either AFCW or FCW as their preference all experienced fewer alerts in AFCW mode than FCW mode, the driver who did not make a choice received equal alerts during both segments of the drive. All 14 drivers in the LDW mode received more alerts in during the LDW portions that the ALDW portions, and 12 preferred ALDW and 2 preferred LDW. It appears that the difference in alert frequency between modes was the most salient aspect of both systems, and influenced several subjective ratings. The trend across both warning systems was for drivers to report that the adaptive warning system was more acceptable, had fewer nuisance alerts, and was less distracting.

Figure 7. Preferences for Adaptive vs. Non-adaptive FCW and LDW systems. Figures 8 and 9 plot the results of the questionnaires for FCW and LDW respectively and reveal a consistent trend of both adaptive and non-adaptive systems behaving as the driver’s expected, the adaptive versions improving the perception of whether the nuisance alert rate was acceptable, the adaptive mode improving their likelihood to recommend the system and the adaptive mode revealing no negative effect on the perception of whether the system enhanced safety.

Figure 8. Subjective Adaptive vs. Non-Adaptive responses for FCW.

3.

Figure 9. Subjective Adaptive vs. Non-Adaptive responses for LDW. 4.

9. CONCLUSION This paper presented four complementary active safety systems for commercial vehicles: (1) free-calibration vision based driver forward/non-forward head pose estimation, visual driver distraction alert, Adaptive Lane Departure Warning and Adaptive Forward Collision Warning systems. Reported results in this paper demonstrated that tailoring alerts to the driver’s visual distraction can help alleviate the tradeoff between providing sufficient warning during distracted episodes and annoying drivers when they do not need the warnings. By avoiding this tradeoff, the collisionreduction effectiveness of FCW was increased and the acceptance of both FCW and LDW was improved by reducing the number of alerts during periods of visuallyattentive driving. A range of distraction mitigation countermeasures were investigated in the SAVE-IT program in terms of collision-reduction effectiveness and driver acceptance. Of the distraction mitigation countermeasures that were tested in the SAVE-IT program, the trip report offers the greatest potential in terms of both collision-reduction effectiveness and driver acceptance.

REFERENCES 1. Campbell, B. N., Smith, J. D., & Najm, W. G. (2003). Examination of Crash Contributing Factors Using National Crash Databases. National Highway Transportation Safety Administration Report, Washington DC. DOT HS 809 664. 2. Dingus, T. A., Klauer, S. G., Neale, V. L., Petersen, A., Lee, S. E., Sudweeks, J., Perez, M. A., Hankey,

5.

6.

J., Ramsey, D., Gupta, S., Bucher, C., Doerzaph, Z. R., Jermeland, J., & Knipling, R. R. (2006). The 100Car Naturalistic Driving Study: Phase 1I - Results of the 100-Car Field Experiment, NHTSA DTNH22-00C-07007. Klauer, S. G., Dingus, T. A., Neale, V. L., Sudweeks, J., and Ramsey, D. J. (2006). The Impact of Driver Inattention on Near-Crash/Crash Risk: An Analysis Using the 100-Car Naturalistic Driving Study Data. Virginia Tech Transportation Institute. National Highway Traffic Safety Administration. DOT HS 810 594. Washington, DC. Retrieved from: http://wwwnrd.nhtsa.dot.gov/departments/nrd-13/driver distraction/PDF/DriverInattention.pdf Donmez, B., Boyle, L., & Lee, J. D. (2007). Safety Vehicle(s) using adaptive Interface Technology (SAVE-IT) Task 4 Final Report: Distraction Mitigation Evaluation. http://www.volpe.dot.gov/hf/roadway/saveit/docs/pha se2/distraction-mitigation-evaluation-final.doc Klauer, S. G., Dingus, T. A., Neale, V. L., Sudweeks, J., and Ramsey, D. J. (2006). The Impact of Driver Inattention on Near-Crash/Crash Risk: An Analysis Using the 100-Car Naturalistic Driving Study Data. Virginia Tech Transportation Institute. National Highway Traffic Safety Administration. DOT HS 810 594. Washington, DC. Retrieved from: http://wwwnrd.nhtsa.dot.gov/departments/nrd-13/driver distraction/PDF/DriverInattention.pdf D. Lamble, T. Kauranen, M. Laakso, and H. Summala. Cognitive load and detection thresholds in car following situations: Safety implications for using mobile (cellular) telephones while driving. Accident Analysis and Prevention, 31:617.623, 1999.

7. M. A. Regan, J. D. Lee, and K. D. Young. Driver Distraction Theory, Effects and Mitigation. CRC Press.

8. Smith, M. R. H., Bakowski, D. L., & Witt, G. J. (2008). Safety Vehicle(s) using adaptive Interface Technology (SAVE-IT) Task 9 Final Report: Safety Warning Countermeasures. http://www.volpe.dot.gov/hf/roadway/saveit/docs/dec 04/finalrep_9b.pdf 9. D. L. Strayer and W. A. Johnston. Driven to distraction: Dualtask studies of simulated driving and conversing on a cellular telephone. Psychological Science,12(4):462.466,2001. 10. Zhang, H., Smith, M. R. H., & Dufour, R. (2008). Safety Vehicle(s) using adaptive Interface Technology (SAVE-IT) Task 7 Phase 1 Report: Visual Distraction.

Driver Distraction Monitoring and Adaptive Safety Warning Systems

... Release Time. (ART) and Standard Deviation of Lane Position (SDLP). ... Illustration of results - featureless head pose- based driver alert detection. Figure 4.

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