Investigation of AV Operational Issues Using Driving Simulator Equipment

Investigation of AV Operational Issues Using Driving Simulator Equipment

Headshot of Brandon Pitts. The link directs to their bio page.
Brandon Pitts
Samuel Labi Headshot. The link directs to their bio page.
Samuel Labi
Purdue University Logo. The link directs to the funded research led by this institution.

Principal Investigator(s):

Brandon Pitts, Assistant Professor of Industrial Engineering – Purdue University
Faculty Associate – Center on Aging and the Life Course (CALC)
Director – Next-generation Human-systems and Cognitive Engineering (NHanCE) Research Lab
Samuel Labi, Professor of Civil Engineering – Purdue University
Director – NEXTRANS
Associate Director – Center for Connected and Automated Transportation (CCAT)

Project Abstract:
Autonomous vehicle (AV) stakeholders continue to seek assurance of the safety performance of this new technology through ways that include AV testing on in-service roads, AV-dedicated road networks and AV test tracks. Driving simulation can be used to test AV scenarios in a safe environment. This project proposes the use of a driving simulator to address specific issues associated with autonomous vehicles. The proposed research addresses five aspects associated with human take over from AV: (1) characterizing the level of risk in the driving environment, as a function of the traffic conditions, roadway design features, road environment conditions, and AV passenger attributes, (2) establishment of take-over warrants, that is, the given combinations of risk factors that will require take over, (3) take-over alerts, specifically, evaluating the efficacy of various alert alternatives: visual, auditory, tactile, and any combination of these, (4) assessing the propensity of an AV operator to take over the vehicle control, as a function of the nature of perceived risk and the prevailing levels of the risk factors (attributes of the driver, vehicle, the road design, and the road environment), (5) measuring and modeling the effective response time, in other words, the time taken for the operator to take over the vehicle (from the time of receipt of the AV’s alert to take over or the operator’s self-recognition of driving hazard without receiving alert) to the time the operator is in full control.

Institution(s): Purdue University

Award Year: 2019

Research Thrust(s): Control & Operations, Human FactorsModeling & Implementation, Policy & Planning

Project Form(s):