The effects of eight in-vehicle tasks on driver distraction were measured in a large, moving-base driving simulator. Forty-eight adults, ranging in age from 35 to 66, and 15 teenagers participated in the simulated drive. Hand-held and hands-free versions of phone dialing, voicemail retrieval, and incoming calls represented six of the eight tasks. Manual radio tuning and climate control adjustment were also included to allow comparison with tasks that have traditionally been present in vehicles. During the drive the participants were asked to respond to sudden movements in surrounding traffic. The driver’s ability to detect these sudden movements or events changed with the nature of the in-vehicle tasks that were being performed. Driving performance measures such as lane violations and heading error were also computed. The performance of the adult group was compared with the performance of the teenage drivers. Compared with the adults, the teens were found to choose unsafe following distances, have poor vehicle control skills, and be more prone to distraction from hand-held phone tasks.

Lane departure warning (LDW) is a driver warning system designed to reduce the number of unintended lane departures. We addressed warning effectiveness and customer acceptance when the unintended lane departures are the result of drowsy driving. Thirty-two adults who were sleep deprived for 23 hours participated in the study and drove Ford's VIRTTEX driving simulator. Four Human Machine Interfaces (HMI) for LDW were evaluated: Steering Wheel Torque, Rumble Strip Sound, Steering Wheel Vibration and Head Up Display. A yaw deviation technique was used to produce controlled lane departures in the first two hours of the drive while for the last 20 minutes driver-initiated lane departures were analyzed. The Steering Wheel Vibration HMI, accompanied by Steering Wheel Torque, was found to be the most effective HMI for LDW in a group of drowsy drivers, with faster reaction times and smaller lane excursions. The Vibration HMI was also perceived by the drowsy drivers to be acceptable and helpful.

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The ease of entering a car is one of the important ergonomic factors that car manufacturers consider during the process of car design. This has motivated many researchers to investigate factors that affect discomfort during ingress. The patterns of motion during ingress may be related to discomfort, but the analysis of motion is challenging. In this paper, a modeling framework is proposed to use the motions of body landmarks to predict subjectively reported discomfort during ingress. Foot trajectories are used to identify a set of trials with a consistent right-leg-first strategy. The trajectories from 20 landmarks on the limbs and torso are parameterized using B-spline basis functions. Two group selection methods, group non-negative garrote (GNNG) and stepwise group selection (SGS), are used to filter and identify the trajectories that are important for prediction. Finally, a classification and prediction model is built using support vector machine (SVM). The performance of the proposed framework is then evaluated against simpler, more common prediction models.

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