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Assisting Drivers with Ambient Take Over Requests in Highly Automated Driving
- 1. Assisting Drivers with Ambient Take-Over Requests in Highly Automated Driving Shadan Sadeghian Borojeni Lewis Chuang Wilko Heuten Susanne Boll
- 2. Automated Driving General Motors Motorama Exhibit 1956 04.11.2016 Sadeghianborojeni et al., Auto-UI 2016, Ann Arbor, MI, USA 3
- 5. Levels of Automation (NHTSA) 6 Level 0: No vehicle autonomy, driver has control Level 1: vehicle provides driver info/warnings, driver has informed control Level 2: vehicle integrates detection/response, driver ready to take control Level 3: vehicle fully autonomous, driver takes control in emergency Level 4: vehicle fully autonomous, occupants do not need ability to drive Sadeghianborojeni et al., Auto-UI 2016, Ann Arbor, MI, USA04.11.2016
- 6. Human vs Machine Battle of the sensors 7Sadeghianborojeni et al., Auto-UI 2016, Ann Arbor, MI, USA04.11.2016
- 8. vigilance: monitor for unexpected events concentrate: driving, monitor busy traffic switching: different information locations share: other tasks suppress: inhibit unnecessary actions preparation: initiate action procedures goal-setting: maintenance of objective(s) Software of Attention? e.g. on the highway looking for an exit 9 Stuss, Shallice, Alexander, & Picton (1995). A multi-disciplinary approach to anterior attentional functions. In Grafman, Holyoak, Boller (Eds. Structure and function of the human prefrontal cortex, Annals of New York Academy of Sciences, 279, 191--211 Sadeghianborojeni et al., Auto-UI 2016, Ann Arbor, MI, USA
- 9. vigilance: right lateral mid-frontal regions concentrate: anterior cingulate switching: dorsolateral prefrontal cortex share: orbitofrontal and anterior cingulate suppress: bilateral orbitofrontal areas preparation: pre-motor cortex goal-setting: dorsolateral prefrontal cortex Software of Attention? e.g. on the highway looking for an exit 10 Stuss, Shallice, Alexander, & Picton (1995). A multi-disciplinary approach to anterior attentional functions. In Grafman, Holyoak, Boller (Eds. Structure and function of the human prefrontal cortex, Annals of New York Academy of Sciences, 279, 191--211 Sadeghianborojeni et al., Auto-UI 2016, Ann Arbor, MI, USA
- 10. Assisting Takeover Situations 11 How can we support a driver’s ability to switch from engaging with a non-vehicle-handling task to monitor and/or resume the complex maneuvers that constitute effective vehicle handling? Attention disengagement from non-driving task Shifting attention to manual driving task switching: different information locations preparation: initiate action procedures suppress: inhibit unnecessary actions Sadeghianborojeni et al., Auto-UI 2016, Ann Arbor, MI, USA04.11.2016
- 11. Experimen t
- 12. Audio-Visual Take-over Request Goal • Shift attention from the secondary task to the driving task • Communicate the driving environment and upcoming task • Prepare appropriate maneuver Attention disengagement from non-driving task Shifting attention to manual driving task Shift driver‘s attention Shift driver‘s attention and provide context
- 13. Design: Take-over Requests 14 • Ambient light displays can be effective to prime a take-over situation. • Presenting contextual information as TORs to drivers, affects their performance • The presentation pattern of the light cues affects drivers’ performance Sadeghianborojeni et al., Auto-UI 2016, Ann Arbor, MI, USA04.11.2016
- 14. RGB LED Strip Tablet PC Eyetracker 20 participants
- 16. 17 Primary task: Visuospatial 1-back Sadeghianborojeni et al., Auto-UI 2016, Ann Arbor, MI, USA04.11.2016
- 17. 18 Baseline All LEDs turn on Sadeghianborojeni et al., Auto-UI 2016, Ann Arbor, MI, USA04.11.2016
- 18. 19 Half of the LEDs turn on for steering direction Static Sadeghianborojeni et al., Auto-UI 2016, Ann Arbor, MI, USA04.11.2016
- 19. 20 Moving Half of the LEDs turn on sequentially for steering direction Sadeghianborojeni et al., Auto-UI 2016, Ann Arbor, MI, USA04.11.2016
- 21. Measurements 22 Reaction time (RT) the time between presentation of the TOR and first steering action Time to collision (TTC) to obstacle the time between the lane change maneuver and collision to the road block Workload (NASA-RTLX) self-reported workload ratings Gaze behavior number and duration of glances at the light display when the TORs were presented Sadeghianborojeni et al., Auto-UI 2016, Ann Arbor, MI, USA04.11.2016
- 22. Hypotheses 1: Performance RTBaseline < RTStatic < RTMoving TTCmoving > TTCstatic > TTCbaseline 2: Workload NASA-RTLXmoving < NASA-RTLXstatic < NASA-RTLXbaseline 3: Glance Behavior on Cue G_FreqBaseline ≤ G_Freq Moving < G_FreqStatic G_DurBaseline ≤ G_Dur Moving < G_DurStatic 23Sadeghianborojeni et al., Auto-UI 2016, Ann Arbor, MI, USA04.11.2016
- 23. Results
- 24. Qualitative Feedback • “Having light in the periphery together with the auditory cue, attracts attention faster to the handover task. ” • “ It saves time scanning the road and seeing what is wrong and what I have to do.” • 85% of the participants preferred conditions with contextual cuing (static and moving) to the baseline. • Between the static and moving lights, the moving light was preferred (71%) 25Sadeghianborojeni et al., Auto-UI 2016, Ann Arbor, MI, USA04.11.2016
- 25. Reaction Times (msecs) F2,38 = 7.46, p < 0.01, ω2 = 0.24 Bayes Factor ≈ p(H0):p(H1) =3.58 26 * * Baseline Static Moving Sadeghianborojeni et al., Auto-UI 2016, Ann Arbor, MI, USA04.11.2016 Kass, R. E., & Raftery, A. E. (1995). Bayes factors. Journal of the american statistical association, 90(430), 773-795.
- 26. Reaction Times (msecs) F2,38 = 7.46, p < 0.01, ω2 = 0.24 H0 is 3.58 times more likely than H1 (Static≠Moving) 27 * * Baseline Static Moving Sadeghianborojeni et al., Auto-UI 2016, Ann Arbor, MI, USA04.11.2016 Kass, R. E., & Raftery, A. E. (1995). Bayes factors. Journal of the american statistical association, 90(430), 773-795.
- 27. Time to Collision to Obstacle (secs) 28 F2,38 = 7.70, p < 0.01, ω2 = 0.25 H0 is 4.3 times more likely than H1 (Static≠Moving) * * Baseline Static Moving Sadeghianborojeni et al., Auto-UI 2016, Ann Arbor, MI, USA04.11.2016
- 28. NASA RTLX • Overall Workload (F1.89, 37.95 = 2.16 , p = 0.13) 29 28.57 ±16.03 33.21 ±12.19 27.14 ±16.32 Moving < Static < Baseline Sadeghianborojeni et al., Auto-UI 2016, Ann Arbor, MI, USA04.11.2016
- 29. F2,38 = 3.09, p = 0.06, ω2 = 0.09 H0 is 1.3 times less likely than H1 (Static≠Baseline) H0 is 1.7 times more likely than H1 (Moving≠Baseline) Number of glances Baseline Static Moving
- 30. Glance duration F2,38 = 2.24, p = 0.12, ω2 = 0.06 Baseline Static Moving H0 is 5 times less likely than H1 (Static≠Baseline) H0 is 2 times less likely than H1 (Moving≠Baseline)
- 31. Wrap up
- 32. Findings • indicating appropriate maneuver • reduces response times • increase the safety margin for time to collision • self-reports indicate less mental demands for moving cue • moving cue is not more likely than the baseline to capture gaze • Users prefer the moving cue 33Sadeghianborojeni et al., Auto-UI 2016, Ann Arbor, MI, USA04.11.2016
- 33. Conclusions 34 • Ambient light displays can be effective in shifting attention to a take-over situation. • The presentation pattern of the light cues does not necessarily impair driving performance. • Presenting contextual information in TORs can result in more desirable behavior. Sadeghianborojeni et al., Auto-UI 2016, Ann Arbor, MI, USA04.11.2016
- 34. Thank you for your attention shadan.sadeghian@offis.de lewis@humanmachinesystems.org
Editor's Notes
- ford
- 1. when driving, the driver has to be vigilant to respond for unexpected events that occur rarely, such as the appearance of the pot-hole 2. in addition, he has to concentrate on his primary task, which is driving 3. for driving itself, he might need to switch attention between the traffic ahead and looking at the mirrors for the traffic behind. 4. he might also be talking to a passenger and has to manage how resources are shared between talking and driving 5. naturally, he will want to look at the passenger and extra resources are required to prevent this unhelpful behavior during driving 6. when he notices the sign for the highway exit, he will need resources to prepare the actions for an exiting-highway manoeuvre. this requires resources also. 7. Throughout all of this, he has set himself a goal, namely getting to a specific place, and will need to remind himself of this goal constantly. This requires resources also.
- 1. when driving, the driver has to be vigilant to respond for unexpected events that occur rarely, such as the appearance of the pot-hole 2. in addition, he has to concentrate on his primary task, which is driving 3. for driving itself, he might need to switch attention between the traffic ahead and looking at the mirrors for the traffic behind. 4. he might also be talking to a passenger and has to manage how resources are shared between talking and driving 5. naturally, he will want to look at the passenger and extra resources are required to prevent this unhelpful behavior during driving 6. when he notices the sign for the highway exit, he will need resources to prepare the actions for an exiting-highway manoeuvre. this requires resources also. 7. Throughout all of this, he has set himself a goal, namely getting to a specific place, and will need to remind himself of this goal constantly. This requires resources also.
- first, we show that while having audio cues to prime users with the urgency of take-over situation, locating the visual cue in the periphery (namely, a peripheral light display) can reduce mental workload and assist safe maneuvers second, our designed light display can convey contextual information to assist steering at take-over situations third, using different light patterns for presenting contextual information can have an effect on driving behavior.
- A fixed-based right-hand traffic driving simulator with a field of vision of 150 was used. The simulation was created with SILAB 1 . Auditory cues were also played simultaneously from speakers built in the driving simulator, located behind the driver on both sides. In this study, an Adafruit NeoPixel Digital RGB LED strip with a resolution of 144 LEDs per meter was used. To reduce the intensity of the light display, theLED strip was placed in a matte white acrylic LED profile. The frame was located on the dashboard of the driving simulator behind the steering wheel, 65 degrees from fixation on the tablet pc presenting the 1-back task, which was on the drivers’ laps. To detect the eye gaze of the participants during the experiment, they were asked to wear Dikablis Glasses by Ergoneers 2 . The eye-tracker was calibrated before each trial to ensure constant track of eye-gaze behavior. Two physical markers on the front panel and two virtual ones on the simulator displays were used. The calibration procedure took between 30 seconds to one minute for each trial. We used the standard eyetracker software for calibration, video recording and analysis of participants’ eye-gaze.
- In 30-40 second intervals a light and an audio cue was presented as TOR, informing them of a road block (a truck with road construction signs and alerts parked on the road) on either left or the right lane. The TORs were presented at 5 seconds TTC to the road block,
- N-back task Add tablet pic
- Measures
- Condition (baseline)
- Condition (baseline)
- Second trial only
- Measures
- Hypothesis
- Post-hoc Tukey HSD tests on both measures revealed that both static and moving cue conditions were significantly different from the baseline cue condition but not from each other.
- Post-hoc Tukey HSD tests on both measures revealed that both static and moving cue conditions were significantly different from the baseline cue condition but not from each other.
- Post-hoc Tukey HSD tests on both measures revealed that both static and moving cue conditions were significantly different from the baseline cue condition but not from each other.
- results
- We performed a JZS Bayesian t-tests in order to understand how the manipulated cues of static and moving compared to the baseline. In terms of number of glances, the static cue (BF01=0.21) was more likely, than the moving cue (BF01=0.49), to be different from the baseline. In addition, the mean duration of these glances were more likely to be different for the static cue (BF01=0.8), than the moving cue (BF01=1.66), to the baseline. Using the labels provided by [22], we have ’substantial’ evidence that static cues attract more glances than the baseline but only ’anecdotal’ evidence for moving cues. Furthermore, we have ’anecdotal’ evidence that moving cues result in glances that have similar duration lengths as our baseline cues, and ’anecdotal’ evidence that static cues result in longer glances.
- We performed a JZS Bayesian t-tests in order to understand how the manipulated cues of static and moving compared to the baseline. In terms of number of glances, the static cue (BF01=0.21) was more likely, than the moving cue (BF01=0.49), to be different from the baseline. In addition, the mean duration of these glances were more likely to be different for the static cue (BF01=0.8), than the moving cue (BF01=1.66), to the baseline. Using the labels provided by [22], we have ’substantial’ evidence that static cues attract more glances than the baseline but only ’anecdotal’ evidence for moving cues. Furthermore, we have ’anecdotal’ evidence that moving cues result in glances that have similar duration lengths as our baseline cues, and ’anecdotal’ evidence that static cues result in longer glances.
- first, we show that while having audio cues to prime users with the urgency of take-over situation, locating the visual cue in the periphery (namely, a peripheral light display) can reduce mental workload and assist safe maneuvers second, our designed light display can convey contextual information to assist steering at take-over situations third, using different light patterns for presenting contextual information can have an effect on driving behavior.