1 / 31

Driving behaviour effects of the Chauffeur Assistant

Jeroen Hogema. Driving behaviour effects of the Chauffeur Assistant . Overview. Background Method TNO driving simulator Simulating the CA Experimental design Results Conclusions consequences for traffic simulation model. Dutch Evaluation of the Chauffeur Assistant (DECA).

bree
Download Presentation

Driving behaviour effects of the Chauffeur Assistant

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Jeroen Hogema Driving behaviour effects of the Chauffeur Assistant

  2. Overview • Background • Method • TNO driving simulator • Simulating the CA • Experimental design • Results • Conclusions • consequences for traffic simulation model

  3. Dutch Evaluation of the Chauffeur Assistant (DECA) • Chauffeur Assistant • Adaptive Cruise Control • Lane Keeping System • Follow-up of Lane Departure Warning Assistant FOT • Transport Research Centre (TRC) • Ministry of Transport, Public Works, and Water Management

  4. Chauffeur Assistant: Questions Individual driver level • driving behaviour • workload • acceptance traffic flow level • traffic performance • safety indicators

  5. DECA MIXIC DRIVING SIMULATOR behaviour driver CA CA workload MIXIC simulations acceptance interpretation report TNO TRC

  6. Method – Driving simulator • visual • audio • steering force • motion

  7. Method – Driving simulator • DAF 95XF lorry • Mass 20500 kg (fully loaded) • Maximum engine power: 350 kW • Parameter set from DAF trucks

  8. Method – Simulating the CA • Adaptive Cruise Control DC specifications • Distance law for car-following • Dref = 6.0 + 1.3 * v • Dref = ACC's intended following distance (m) • v=current speed (m/s) • Braking: max. -3 m/s2

  9. Method – Simulating the CA • ACC controller • structure from earlier work • parameters from recent ACC work by TNO Automotive

  10. Method – Simulating the CA ACC reference scenarios • approaching • braking lead car • accelerating lead car • cut-in Dynamic behaviour of • reference model • driving simulator CA • MIXIC CA

  11. Method – Simulating the CA LKS • noise added to obtain realistic servo performance • SDLP about 10 cm

  12. Method - Experimental design (1) • with vs without CA • traffic volume • low (3400/u) • high (6000/u) • 3-lane motorway, 3.5 m wide lanes ACC headway • Dref = 6.0 + tk * v • tk = 1.0 – 1.3 – 1.6 s • 1 preferred setting selected by each driver prior to experiment

  13. Method - Experimental design (2) Scenarios • car-following (overtaking possible) • braking lead car • 3 m/s2 • 4 m/s2 Subjects • 18, professional truck drivers • at least 5 years 'groot rijbewijs' • age between 25-55 • paid for their participation

  14. Human Machine Interface • driver turns CA turns on/off • switches • brake pedal • driver sets ACC speed • buzzer at maximum braking display • ACC set speed on speedometer • symbol: headway control or speed control

  15. Results – preferred CA time headway 1.0 s 1 x 1.3 s 8 x 1.6 s 9 x Total 18 x

  16. Results – SD lateral position • effect of CA

  17. Results – Time to Line Crossing • effect of CA

  18. Results – close following • effect of CA

  19. Results – lane change frequency • effect of CA on edge of marginal significance [p<.11]

  20. Braking lead car: lane change response lane change reaction of subject Fewer lane changes with CA

  21. Braking lead car: braking response braking reaction of subject lower deceleration levels with CA

  22. Results – mental effort • Rating Scale of Mental Effort • effect of CA

  23. Acceptance (1) • -2..2 scales:

  24. Acceptance (2) Underlying variables • USEFULNESS: + 0.93 • SATISFACTION: + 1.10

  25. Summary of results (1) With Chauffeur Assistant… • reduced SD of lateral position • higher Time to Line Crossings • less short time headways • reduced Mental Effort • (fewer changes with CA?)

  26. Summary of results (2) • Acceptance: positive • except “sleep-inducing” Lane changes • fewer changes with CA? Braking lead car • fewer lane changes with CA • less critical behaviour with CA (maximum deceleration, minimum TTC) No effects on: • mean, s.d. speed • lane use (% right lane) • mean lateral position • mean time headway

  27. Chauffeur Assistant in MIXIC driver vehicle CA

  28. Chauffeur Assistant in MIXIC DRIVER LONGITUDINAL car following VEHICLE free driving LATERAL CA lane change model CA settings transitions

  29. MIXIC driver model Driver – CA • CA settings • CA reference speed = driver’s intended speed • CA reference headway: 50% 1.3 s; 50% 1.6 s • CA off when: • CA is braking hard AND driver would brake harder • starting lane-change manoeuvre • CA on when: • “possible”

  30. MIXIC driver model Lane change behaviour • small effects • nature of effects unknown • tactical level: avoid getting 'stuck' in car-following in a 'slow' lane • driver-state related: reduced alertness, complacency, less 'active' driving • => no changes in lane change model

  31. Conclusions Chauffeur Assistant – ACC + LKS: • Behaviour • Workload effects in line with ACC orLKS research • Acceptance } contribution of ACC and LKS unknown Minor modifications to MIXIC->driver->ACC model

More Related