Virtual vehicle prototyping - Institute for Transport Studies

University of Leeds Driving Simulator
Institute for Transport Studies
Virtual vehicle prototyping
Can driving simulation support Jaguar Land Rover’s
vehicle design process?
Hamish Jamson
Erwin Boer
Sunjoo Advani
Spencer Salter
Programme for
Simulation Innovation
Programme for Simulation Innovation
• Project objective
• To develop capabilities that will deliver robust design through
simulation within the product development process
• Virtual Vehicle Prototyping
• Funding
• £10m strategic partnership between EPSRC and JLR
• ITS funding £1.3m over five years (Mar 2013 – Mar 2018)
• Five core themes
• Two new themes started Mar 2014
Programme for
Simulation Innovation
Programme for Simulation Innovation
• Flawless Launch (University of Sheffield)
• Lifecycle analysis (University of Manchester)
Theme 3 – Driving Simulation
• Academic challenges
• Development of driving simulator utility standards
• Improving driving simulator validity and benchmarking techniques
• Empirical assessment and ranking of driving simulator design
• Industrial benefits
• Cost effective virtual prototyping
• Improved compatibility of predicative testing
• More robust vehicle design
• Objective Functionality Matrix
Programme for
Simulation Innovation
What is a driving simulator?
Vehicle manufacturers
• 1985– first of the two Daimler-Benz facilities
• First driving sim with interchangable cabs
Vehicle manufacturers
• 2009– Toyota Higashi-Fuji Technical Centre
• 25m lateral x 35m longitudinal sled, 7m dome, infinite yaw
But so is this ……
Key components of a driving sim
Objective Functionality Matrix
• Based on Design Verification Methods (DVM)
• Functionality Matrix defines the appropriate level of simulation
required to achieve a specific DVM
• Task driven approach
• What is the role of human perception in performing the driving tasks in
the real situation?
• How can the available resources and the simulator’s characteristics
(hardware and software) be best optimised to re-create these
Programme for
Simulation Innovation
Common Experimental Approach
• Task-based approach, c.f. ICAO 9625/AN938
Common Experimental Approach
• Basis functions
• Any complex driving manoeuvre can be described as a composition of
a set of basic driving manoeuvres
• Basis driving tasks (vDMC)
• Vehicle dynamics
• Controlled Stopping, Elk Test, Slalom, Negotiation into a Sharp Curve
• Human Machine Interaction
• Car Following, Gentle Curve Negotiation, Lane change task
• Simulator Assessment
• Speed production, Line tracking
Common Experimental Approach
• Test-cases
• Project 3.0: Driving Simulator Foundation
• Virtual Gaydon with simple vDMC
• Project 3.1: Virtual Global Test
• Low µ / split µ environmental conditions
• Project 3.2: Stability Control Systems
• Modelling perceptual / handling changes with HWIL SCS
• Project 3.3: Human Machine Interface
• Modelling basis HMI tasks and primary/secondary task interference
• Project 3.4: Driver modelling
• Modelling higher order driving tasks (Gosport circuit)
Theme 3 projects
Quantifying Simulator Utility (1/2)
• Simulator cue fidelity (open-loop transfer function)
• DC Gain
• Cut-off Frequency
• Pure Delay
• Lag at Cut-off frequency
• Q-factor
Quantifying Simulator Utility (2/2)
• Behavioural fidelity (closed-loop transfer function)
• Aggregate Performance
• Performance in relation to the task specification. Focus is placed on accuracy or the
degree to which the task was performed to specification.
• Time Series Comparison
• Focus on vehicle response rather than control actions
• Transfer function
• Focus on the perception-action control rather than vehicle movements
Classic cybernetic driver models
• Driver model for lateral control
• Crossover model (McRuer and Weir, 1969)
• STI driver model (McRuer, Allen, Weir and Klein, 1977)
PSI driver model
• Fundamentally a cascade controller (cross-over model)
• Perception of absolute vehicle state
fundamentally influenced by cue rendering and cue perception.
• Perception of relative vehicle state
based primarily on visual environmental cues including preview.
• Feed-forward open loop control
representing a driver’s internal model relating vehicle control actions to vehicle dynamics.
• Prediction
based on look-ahead time to equalise lags and delays in human/vehicle system.
• Proprioceptive feedback
modelled as the coupling between the neuromuscular part of the driver and the manipulator dynamics
• Non-linear control
Cybernetic Driver Model
PSI driver model
Relative Vehicle State
Absolute Vehicle State
Feed Forward Contro
Predicted Vehicle State
Cascade Control
PSI driver model (lateral)
Cascade Control
Relative Vehicle State
Predicted Vehicle State
Feed Forward Control
PSI driver model (lateral)
Relative Vehicle State
Absolute Vehicle State
Feed Forward Contro
Predicted Vehicle State
Cascade Control
Preliminary model analysis
• Road curvature to vehicle curvature for four
manipulations of simulator characteristic
• Base (20m preview with full motion and steering torque)
• Far (Base + infinite preview)
• NoMot (Base – motion cueing)
• NoSteer (Base – steering torque)
• Sum of sinusoids forcing function for road curvature
Preliminary model analysis
Next few months….
• Second round of RA recruitment
• Working predominantly on project 3.1 (VGT) and 3.2 (SCS)
• PhD recruitment
• Working on projects 3.3 (HMI), 3.4 (Driver Modelling) and 3.5 (Future
Simulator Design)
• Preliminary UoLDS utility assessment using JLR
chicane data for July Review Group and Deep-Dive
• Kick-off meetings for 3.1-3.5 in May 2015
Next four years….
• Establishing objective Functionality Matrix and
standards for driving simulator utility assessment
• interactions between the different sensory cue rendering fidelities
• how one sensory channel can compensate for degradation in another
• Establishing the balance between the visual, haptic and motion contributions
to the control-level driving task.
• Developing future driving simulation designs for in
virtual vehicle prototyping
• Considering the role of emerging technologies in the creation of visual,
vestibular, haptic and auditory perception.
• Estimating cost-benefit analyses of proposed designs.

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