A discrete-event simulation model for driver performance assessment: application to autonomous vehicle cockpit design optimization
This work addresses safety and interaction issues in autonomous vehicle design for automotive engineers, but it appears incremental as it builds on existing task analysis methods.
The researchers tackled the challenge of optimizing autonomous vehicle cockpit designs by integrating task analysis into a discrete-event simulation tool, which they applied in an industrial project to assess driver performance metrics like cognitive workload and eyes-off-the-road time.
The latest advances in the design of vehicles with the adaptive level of automation pose new challenges in the vehicle-driver interaction. Safety requirements underline the need to explore optimal cockpit architectures with regard to driver cognitive and perceptual workload, eyes-off-the-road time and situation awareness. We propose to integrate existing task analysis approaches into system architecture evaluation for the early-stage design optimization. We built the discrete-event simulation tool and applied it within the multi-sensory (sight, sound, touch) cockpit design industrial project.