Passing behavior on two-lane roads in real and simulated environments

Passing maneuvers allow faster drivers to continue driving at their desired speeds without being delayed behind impeding vehicles. On two-lane rural roads, such maneuvers require the passing driver to occupy the opposing lane; this condition has tremendous implications for the safety and operation of two-lane roads. Several studies have investigated the passing behavior of drivers, and some studies have used driving simulators to analyze drivers' behavior during following and passing maneuvers. However, the validity of simulators has not been ensured, because their results have rarely been compared with real data. The objective of this study was to compare drivers' passing behavior as observed in the field with passing behavior in a driving simulator. For this purpose, data on passing performance and passing gap acceptance decisions were required. The study carried out a comparative analysis of the most significant variables related to passing behavior. The results showed similarities between passing time and passing distance of completed maneuvers (during the occupation of the opposing lane). However, drivers passed faster in the driving simulator and maintained greater clearances. Gap acceptance decisions were found to be similar, as the distributions of accepted and rejected gaps were similar, although critical gaps were found to be lower in the driving simulator. This finding might be explained by the absence of objective risks. The applicability of driving simulation seems reasonable, although some improvements are still possible, to account for sight distance limitations, replicate age and gender distributions, and reproduce the opposing traffic flow.Transport and Plannin

Development of An Adaptive Staircase System Actuated by Facial-, Object-, and Voice-Recognition

This paper details a proof-of-concept development of an adaptive staircase system-type capable of user-specific mechanical reconfigurations actuated by facial-, object-, and voice-recognition. The system is described via two variation-prototypes - developed at Technology Readiness Level 4 - as instances of the same system-type. Accordingly, each prototype is informed by the same use-case considerations and requirements. Nevertheless, by means of their mechanical particulars, advantages and disadvantages specific to each variation are identified and explored. The present adaptive staircase system-type consists of two main components, one computational and the other mechanical. The computational component is built upon an inherited System Architecture previously developed and implemented by the authors. More specifically, the computational component uses Google's TensorFlow for facial-recognition; BerryNet for multi-object detection; and VoiceIt for voice-recognition. These three cloud-compatible, -based, or -dependent recognition mechanisms are used to ascertain the identity three user-types: (1) a person without perceivable physical disabilities; (2) a person reliant on a walking-cane; and (3) a person on a wheelchair. With the exception of the first case, the computational component proceeds to actuate mechanical transformations pertinent to each variety of disabilities depending on which user-type is identified. The objective of this implementations is to present an intuitive and automated vertical mobility solution capable of supporting users with varying degrees of reduced mobility.Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.Architectural Engineerin