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Vehicle Characteristics and Car Following

Vehicle Characteristics and Car Following. George J. Andersen Department of Psychology University of California, Riverside. Funded by NIH AG13419-06 PATH Project MOU 4220. Perceptual Tasks in Driving. Collision Detection Obstacle avoidance Longitudinal control (car following)

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Vehicle Characteristics and Car Following

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  1. Vehicle Characteristics and Car Following George J. Andersen Department of Psychology University of California, Riverside Funded by NIH AG13419-06PATH Project MOU 4220

  2. Perceptual Tasks in Driving • Collision Detection • Obstacle avoidance • Longitudinal control (car following) • Lateral control (steering)

  3. Perceptual Tasks in Driving • Collision Detection • Obstacle avoidance • Longitudinal control (car following) • Lateral control (steering)

  4. Driving is a skill dependent on visual information Use of simulators requires accurate presentation of visual information used by drivers

  5. Complexity of Collision Detection: Event Specification • Vehicle motion • Speed • Constant or varying (accelerating/decelerating) • Path • Straight or curved • Object motion • Speed • Constant or varying (accelerating/decelerating) • Path • Straight or curved

  6. Complexity of Collision Detection: • Model (Andersen & Sauer, 2004) based on analysis of visual information available to driver • Use of 5 parameters t dt/dt a da/dt ddiff

  7. TopView FrontView t=0 t=1 q t=q/Dq t specifies the time to contact during constant velocity collisions

  8. t =dt/dt dt/dt used during deceleration (braking control) When dt/dt = -0.5 vehicle will reach zero velocity at obstacle

  9. a is the position of object in visual field When a = 0 object is on a collision path Useful when path of motion is linear

  10. da/dt is the change in position of object in visual field When da/dt = k object is on a collision path Useful when path of motion is curvilinear

  11. ddiff is comparison of two distance estimates: dv – distance vehicle will traverse before reaching zero velocity ds – distance of collision object ddiff = dv – ds dv = 1.5v2/a v = edge rate (number of texture elements that pass position in visual field) a = change in number of texture Elements that pass position in visual field ds = (s)tan-1 q s = size of object q = visual angle of object

  12. Size information and safe deceleration to a stop

  13. Edge rate information and safe deceleration to a stop

  14. Vehicle Motion No F/S V/S F/C V/C No F/S Object Motion V/S F/C V/C F = Fixed Speed V = Variable Speed S = Straight Path C = Curved Path

  15. Optical Information for Car Following • Information for specifying distance and change in distance • Information for specifying speed and change in speed

  16. t=0 t=1 a FrontView TopView Da associated with change in distance due to change in speed

  17. Parameters of Car Following Model a’ • Initial visual angle of lead vehicle a • Current visual angle da/dt • Instantaneous change in visual angle J, k • Weighting scalar constants

  18. acceleration Acceleration (km/hr2)

  19. a’ Desired time gap = 1.1s W = width of lead vehicle FVv = following vehicle (driver) speed 2 m Lead Car Distance headway α Driver

  20. Human Factors Experiments • Maintain distance behind lead vehicle that varied speed - sine function - ramp function - sum of sines function

  21. 006

  22. Do drivers use visual information other than visual angle?

  23. Edge Rate Information: Used for Perceived Driver (following vehicle) speed

  24. a t = da/dt Edge Rate and Collision Detection: Moving Objects

  25. t =dt/dt Edge Rate and Collision detection during braking: Static objects

  26. Car Following and edge rate Experiment Task: Car following to sine wave function Independent Variables: Presence or absence of scene Frequency and amplitude of lead vehicle speed Prediction: If edge rate used then more accurate tracking performance when scene present as compared to scene absent

  27. Ongoing Research: Car Following in Traffic

  28. Edge Rate and Moving Vehicles Dual task performance car following Detect Light Change Edge Rate Information Presence of other moving vehicles

  29. Edge Rate and Reduced Visibility Dual task performance car following Detect Light Change Edge Rate Information Presence of Fog

  30. Simulation Design Issues and Recommendations • Simulation displays should be designed to optimize use of visual information • Understanding how best to do this requires understanding what are the sources of information

  31. Simulation Design Issues and Recommendations • Factors that directly affect availability of information sources • Display characteristics (e.g., frame rate, spatial resolution, monitor update and flicker) • 3D model characteristics (e.g., complexity of world model, lighting, and texturing) • Viewing characteristics • (e.g., conflicting accommodation, eye vergence) • Viewing from design eye of simulation

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