1. Programme for Simulation Innovation Hamish Jamson Erwin Boer Sunjoo Advani Spencer Salter University of Leeds Driving Simulator Institute for Transport Studies FACULTY OF ENVIRONMENT Virtual vehicle prototyping Can driving simulation support Jaguar Land Rover’s vehicle design process?

2. Programme for Simulation Innovation PSI 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

3. PSI Programme for Simulation Innovation Flawless Launch (University of Sheffield) Lifecycle analysis (University of Manchester)

4. Programme for Simulation Innovation 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

5. What is a driving simulator?

6. Vehicle manufacturers 1985– first of the two Daimler-Benz facilities First driving sim with interchangable cabs

7. Vehicle manufacturers 2009– Toyota Higashi-Fuji Technical Centre 25m lateral x 35m longitudinal sled, 7m dome, infinite yaw

8. But so is this ……

9. Key components of a driving sim

10. Programme for Simulation Innovation 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 perceptions?

11. Common Experimental Approach Task-based approach, c.f. ICAO 9625/AN938

12. 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

13. 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)

14. 5 Theme 3 projects

15. 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

16. 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

17. Classic cybernetic driver models Driver model for lateral control Crossover model (McRuer and Weir, 1969) STI driver model (McRuer, Allen, Weir and Klein, 1977)

18. 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

19. PSI driver model Cybernetic Driver Model Absolute Vehicle State Relative Vehicle State Feed Forward Control Cascade Control System Predicted Vehicle State LATERAL LONGITUDINAL

20. PSI driver model (lateral) Relative Vehicle State Feed Forward Control Cascade Control Predicted Vehicle State LATERAL

21. PSI driver model (lateral) Absolute Vehicle State Relative Vehicle State Feed Forward Control Cascade Control System Predicted Vehicle State

22. 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

23. Preliminary model analysis

24. 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

25. 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.