Search

A large percentage of single-vehicle automobile crashes involve a situation called run-off-road (ROR) where the vehicle leaves the roadway and travels on the surfaces adjacent to the road. Present solutions such as roadway infrastructure modifications and vehicle safety systems have helped to mitigate some ROR events but remain limited in their approach. A complete solution must also directly address the primary factor contributing to ROR crashes, which is driver performance errors. In this paper, four vehicle safety control systems, based on sliding (SL) control, linear quadratic (LQ), state flow, and classical theories, were developed to autonomously recover a vehicle from ROR without driver intervention. The vehicle response was simulated for each controller under a variety of common road departure and return scenarios. The results showed that the LQ and SL control methodologies outperformed the other controllers in terms of overall stability. However, the LQ controller was the only design to safely recover the vehicle in all of the simulation conditions examined. On average, it performed the recovery almost 50% faster and with 40% less lateral error than the SL controller at the expense of higher yaw rates. [ABSTRACT FROM PUBLISHER]

Detecting deception in interpersonal dialog is challenging since deceivers take advantage of the give-and-take of interaction to adapt to any sign of skepticism in an interlocutor’s verbal and nonverbal feedback. Human detection accuracy is poor, often with no better than chance performance. In this investigation, we consider whether automated methods can produce better results and if emphasizing the possible disruption in interactional synchrony can signal whether an interactant is truthful or deceptive. We propose a data-driven and unobtrusive framework using visual cues that consists of face tracking, head movement detection, facial expression recognition, and interactional synchrony estimation. Analysis were conducted on 242 video samples from an experiment in which deceivers and truth-tellers interacted with professional interviewers either face-to-face or through computer mediation. Results revealed that the framework is able to automatically track head movements and expressions of both interlocutors to extract normalized meaningful synchrony features and to learn classification models for deception recognition. Further experiments show that these features reliably capture interactional synchrony and efficiently discriminate deception from truth. [ABSTRACT FROM PUBLISHER]

Increased global use of motorized vehicles has led to a higher number of automotive crashes involving human injuries and fatalities. Greater driver awareness through haptic feedback can provide vital information from the vehicle and surroundings to the driver while minimizing driver distraction. In this paper, steering wheel haptic feedback will be provided to increase the drivers' knowledge of the roadway and driving conditions. This increased knowledge should enable the driver to avoid obstacles presented in the roadway earlier than without any feedback. A high-fidelity customizable driving simulator was utilized to provide nine different levels of sinusoidal haptic feedback via the steering wheel. Mathematical models for the vehicle steering system, with adjustable controller gains, were developed to create torsional feedback. The testing included 25 human subjects ranging in age and driving experience. Driver preferences for feedback amplitude and frequency were gathered, whereas driver/vehicle performance was used as the measure of feedback effectiveness. The laboratory results demonstrate that haptic steering feedback improved driver performance as measured by a 62% reduction in obstacle hit rates, small reductions in peak steering wheel angle and peak vehicle yaw rate, as well as a nearly 10 m (32.8 ft) increase in reaction distance to the obstacles. The research contribution is the successful application of an advanced steering simulator with a steering control strategy to introduce supplemental haptic feedback to the driver through the steering system for roadway avoidance notification. [ABSTRACT FROM PUBLISHER]

The article provides information about deception detection through automatic, unobtrusive analysis of nonverbal behavior cues. The system can infer deception or truthfulness from a set of features extracted from head and hands movements in a video. A validated and reliable behaviorally-based deception analysis system could potentially have great impacts in augmenting humans' abilities to assess credibility. In this regard, researchers have focused their studies in micro-momentary expressions whereby involuntary fleeting facial movements reliably suggest deception. By extensively deconstructing deceptive acts, researchers have found that deceivers in an attempt to retain credibility and deflect suspicion, express patterns of atypical behavior.