Search Results

Objective tools that can assess the demands associated with in-vehicle human machine interfaces (HMIs) could assist automotive engineers designing safer interaction. This paper presents empirical evidence supporting one objective assessment approach, which compares the demand associated with in-vehicle tasks to the demand associated with “benchmarking” or “comparison tasks”. In the presented study, there were two types of benchmarking tasks-a modified surrogate reference task (SuRT) and a delayed digit recall task (n-back task) - representing different levels of visual demand and cognitive demand respectively. Twenty-four participants performed these two types of benchmarking tasks as well as two radio tasks while driving a vehicle on a closed-loop test track. Response measures included physiological (heart rate), glance metrics, driving performance (steering entropy) and subjective workload ratings.

An analysis of the first 35 back-over crashes reported by NHTSA's Special Crash Investigations unit was undertaken with two objectives: (1) to test a hypothesized classification of backing crashes into types, and (2) to characterize scenario-specific conditions that may drive countermeasure development requirements and/or objective test development requirements. Backing crash cases were sorted by type, and then analyzed in terms of key features. Subsequent modeling of these SCI cases was done using an adaptation of the Driving Reliability and Error Analysis Methodology (DREAM) and Cognitive Reliability and Error Analysis Methodology (CREAM) (similar to previous applications, for instance, by Ljung and Sandin to lane departure crashes [10]), which is felt to provide a useful tool for crash avoidance technology development.