Human Perception of Transient Longitudinal Vehicle Motion
Human perception of discomfort during transient longitudinal motion of a vehicle is little understood. Optimizing vehicle motion to maximize occupant comfort is of great interest to vehicle manufacturers. The imminent wide-scale deployment of autonomous control has further increased the attention on the ride comfort problem, since user acceptance significantly impacts adoption. Correlations of passenger discomfort and vehicle motion have been studied for decades using regression analysis, but there is increasing doubt about the relevance of these results given rapid developments in vehicle performance and autonomy. It is therefore desirable to develop a mechanistic model of the vehicle occupant for the prediction of objective and subjective responses to transient longitudinal motion. A measurement system capable of providing the data required for identification of a mechanistic model of the passenger is needed. Accordingly, the first contribution of this work is the development of passenger instrumentation based on probabilistic state estimation of the occupant head and torso motion in an accelerating vehicle. Probabilistic visual-inertial sensor fusion is used to overcome the limitations of existing approaches and estimate body segment dimensions by appending biomechanical kinematic constraints. A mechanistic model of the passenger is developed including details of biomechanics, sensory perception, cognition, and muscular action. In particular, this work contributes with a novel model of the passenger's ability to temporally anticipate the vehicle motion utilizing sensory cues. The model is used to predict the objective responses including internal latent signals which are hypothesized to be associated with subjective emotions. Measurements and published datasets are used to identify subject-specific model parameter sets. The model predictions are found to reasonably fit the measurements during a wide range of motion parameter combinations. Subjective-objective correlations are studied to predict the passenger discomfort from observed and latent features providing unique insights which were not possible to infer from existing data-driven studies. Results show that discomfort is likely the result of the vehicle acceleration magnitude and passenger head motion magnitude and predictability. The developed insights are used to devise guidelines for vehicle motion design.
human motion perception, longitudinal vehicle motion, occupant body motion estimation, passenger model, ride comfort
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