Simulation of an automobile under automatic steering control, from the occupant’s point of view

1. Fault Detection in Lateral Vehicle Control • Simulation of an automobile under automatic steering control, from the occupant’s point of view • Evaluation of an occupant’s ability to detect and correct for controller malfunctions that will cause the vehicle to drift out of its lane • Evaluation of an occupant’s ability to compensate for a controller fault and keep the car safely in its lane

2. Test Setup PC Monitor Steering Wheel HO Controller Noise

3. Unique Properties of the Human Controller The human controller brings certain properties as yet unduplicated in machine controllers. These include: 1) Superior image processing 2) Superior adaptability based upon cognitive skills 3) Superior ability to anticipate But slower responses, poorer control performance, divided attention, fatigability, diversity of skills, and distractibility are distinctly human traits as well. What, then, is the right strategy to combine human and machine fault detection capabilities?

4. Roles of the Human Operator We view the range of possible roles as lying on a continuum. At one end (H), we have the human fully in charge, at the other end, the controller (C) is in charge. We have focused our attention on three intermediate points or cooperative strategies (CS), between these extremes: H C 1) as helper 2) as peer 3) as crisis handler We have developed a Scenario Evaluation System (SES) in which to explore both the fault detection and the fault handling performance of each of these three scenarios.

5. Purpose of the Scenario Evaluation System The SES is essentially a pre-simulator. It is a system in which we isolate many variables to concentrate on just a few. Our main interest is the role of vision in the performance of the combination formed by the human operator and the controller. We thus exclude important variables from consideration. The advantage is specificity concerning the role of vision. Disadvantages are overcome by migrating the study to either a real simulator or to the test track.

6. Detection Test Series • How accurately and quickly can the HO detect a controller fault? • # faults • # “hits” • # “misses” • # “false alarms” • Lateral and angular deviation at detection • Point on track where fault occurred/was detected • With and without system noise • As a function of road geometry (straight-a-way, curve, S-curve, transition point) • Physical attributes, behavior, and detection criteria used will vary from subject to subject • How do detections (hits) and false alarms trade off with respect to • The various scenarios • Road geometry

7. Quality of Reaction Test Series • Comparison of HO’s and Controller’s guidance performance • Comparison of Lateral and Angular Deviation • How well can the HO compensate for a controller fault and keep the car safely in the lane? • Lateral and angular deviation • As a function of CS (helper, peer, crisis handler) • As a function of road geometry • How does performance change with task familiarity? • How long does it take to learn a given CS?

8. Fault & Curvature Change Locations

9. Detection Test Series: Summary

10. Lateral Deviation

11. Learning Curve Effect

12. Placement of False Alarm Criterion

13. Hit Rate vs. False Alarm Rate

14. Quality of Reaction Test Series: Part I

15. Additional Questions the SES Can Answer • 1. How is the HO’s detection/quality of reaction performance affected by: • - Cooperative Strategy • - Road Geometry • - Atmospheric Conditions • - Vision Defects • - Presence of other vehicles (in front, to the sides) • 2. How well can the HO “drive” the PC-model of the vehicle compared to the automatic controller? • - What is the HO’s strategy for negotiating various road geometry's situations (curves, overtaking and passing slower vehicles)? • - What visual cues does the HO use in determining how to steer the vehicle? How does he use these? • - Can this be used to improve the operation of the controller?

16. Additional Questions (Continued) • 3. How well can the HO compensate for a controller fault after taking (partial or full) control of the vehicle after detecting a fault? • - What is the distribution of normal human performance • 4. How best can an HO share the driving duties with an automatic controller? • - What type of controller/cooperative strategy works best in a shared arrangement? • - How would such a scheme compare with vehicle performance under full automatic control?

17. Additional Questions (Continued) • 5. How would such a combination (Item 4) be modeled? • - HO likely to be nonstationary, nonhomogeneous, nonlinear, etc? • - Develop cognitive model of HO? • - Interface between HO and controller is critical, not only for model accuracy, but also for proper coordination and communication between the two. • - How will controller design be affected?

18. Additional Uses • Simulate new designs and driving procedures • Develop specifications for • Operator sight and skill requirements • Road and vehicle/controller performance standards • Customize Cooperative Strategies for individual drivers • Performance comparisons

19. Additional Uses (continued) • Re-creation of actual driving scenarios for further analysis • Identify and quantify the important results that more elaborate and expensive test procedures must provide • First step towards development of a cognitive model combining the HO and controller in a “complete” system • Develop best cooperative strategy • Incorporate full range of human capabilities into model • Tailor CS to individual drivers

20. Cooperative Strategies