Ergonomics of intelligent vehicle braking systems

The present thesis examines the quantitative characteristics of driver braking and pedal operation and discusses the implications for the design of braking support systems for vehicles. After the current status of the relevant research is presented through a literature review, three different methods are employed to examine driver braking microscopically, supplemented by a fourth method challenging the potential to apply the results in an adaptive brake assist system. First, thirty drivers drove an instrumented vehicle for a day each. Pedal inputs were constantly monitored through force, position sensors and a video camera. Results suggested a range of normal braking inputs in terms of brake-pedal force, initial brake-pedal displacement and throttle-release (throttle-off) rate. The inter-personal and intra-personal variability on the main variables was also prominent. [Continues.

Draft guidelines concerning E&D issues: The TELSCAN handbook of design guidelines for usability of systems by elderly and disabled drivers and travellers. Version 2

Draft guidelines concerning E&D issues: The TELSCAN handbook of design guidelines for usability of systems by elderly and disabled drivers and travellers. Version

Psychological Analysis of Degree of Safety in Traffic Environment Design

See this work in the Center for Transportation Research Library catalog: https://library.ctr.utexas.edu/Presto/catalogid=5773Recent on-site accident investigation studies have estimated that between 10 and 25 percent of automobile accidents involve distraction as a principal causative factor. This report presents the findings of a research project designed to study the relationship between visual dis tractors in the roadside environment, such as advertising signs, neon lights, and gaudy billboards, and traffic safety. This project has involved the definition, operationalization, and measurement of visual distractibility in the traffic environment, including an analysis of distractions attributable to private signs and lights in the Vicinity of public signs and signals and of distractions caused by an overload or improper placement of public signs.Texas Office of Traffic SafetyCenter for Transportation ResearchSee this work in the Center for Transportation Research Library catalog

The development of improvements to drivers' direct and indirect vision from vehicles - phase 1

This research project concerning "The development of improvements to drivers' direct and indirect vision from vehicles" has been designed to be conducted in two phases: . Phase 1 whose aim is to scope the existing knowledge base in order to prioritise and direct activities within Phase 2; . Phase 2 whose aim is to investigate specific driver vision problems prioritised in Phase 1 and determine solutions to them. This report details the activities, findings and conclusions resulting from the Phase 1 tasks undertaken

An Evaluation of the Utilization of Psychological Knowledge Concerning Potential Roadside Distractors

See this work in the Center for Transportation Research Library catalog: https://library.ctr.utexas.edu/Presto/catalogid=5775This report presents the findings of a project designed: (1) to evaluate the effect of nighttime distractors on accidents at signalized intersections, and (2) to facilitate the implementation at the local level of traffic safety standards. The findings of Part I, based on a study of nighttime accidents occurring at 30 metropolitan intersections, offer tentative data that both the color and the proximity of lights may be important in determining their distractibility with respect to the traffic signal. Part II describes the procedures followed by the project team to disseminate psychological knowledge concerning environmental distractors in the traffic environment to appropriate local agencies and includes an evaluation of the utilization of this information by these agencies.Texas Office of Traffic SafetyCenter for Transportation ResearchSee this work in the Center for Transportation Research Library catalog

Integrating automobile multiple intelligent warning systems : performance and policy implications

Thesis (S.M.)--Massachusetts Institute of Technology, Engineering Systems Division, Technology and Policy Program, 2006.Includes bibliographical references (p. 160-167).Intelligent driver warning systems can be found in many high-end vehicles on the road today, which will likely rapidly increase as they become standard equipment. However, introducing multiple warning systems into vehicles could potentially add to the complexity of the driving task, and there are many critical human factors issues that should be considered, such as how the interaction between alarm alerting schemes, system reliabilities, and distractions combine to affect driving performance and situation awareness. In addition, there are also questions with respect to whether there should be any minimum safety standards set to ensure both functional and usage safety of these systems, and what these standards should be. An experiment was conducted to study how a single master alert versus multiple individual alerts of different reliabilities affected drivers' responses to different imminent collision situations while distracted. A master alert may have advantages since it reduces the total number of alerts, which could be advantageous especially with the proliferation of intelligent warning systems. However, a master alert may also confuse drivers, since it does not warn of a specific hazard, unlike a specific alert for each warning systems.(cont.) Auditory alerts were used to warn of imminent frontal and rear collisions, as well as unintentional left and right lane departures. Low and high warning reliabilities were also tested. The different warning systems and reliability factors produced significantly different reaction times and response accuracies. The warning systems with low reliability caused accuracy rates to fall more than 40% across the four warning systems. In addition, low reliability systems also induced negative emotions in participants. Thus, reliability is one of the most crucial determinants of driving performance and the safety outcome, and it is imperative that warning systems are reliable. For the master versus distinct alarms factor, drivers responded statistically no different to the various collision warnings for both reaction times and accuracy of responses. However, in a subjective post-experiment assessment, participants preferred distinct alarms for different driver warning systems, even though their objective performance showed no difference to the different alerting schemes. This study showed that it was essential to design robust and reliable intelligent warning systems. However, there are no existing safety standards today to ensure that these systems are safe before they are introduced into vehicles, even though such systems are already available in high-end cars.(cont.) Even though there are tradeoffs in having standards, such as increased time-to-market and possible loss of innovation, I recommend that safety standards be set nonetheless, since standards will ensure the safety performance of warning systems, to an extent. In terms of functional safety, safety standards should be performance-based, and should specify a minimum level of reliability. In terms of usage safety, the standards should also be performance-based, where driving performance can be indicated by measures such as reaction time, lane position, heading distance and accuracy of responses. In addition, multiple threat scenarios should also be tested. In terms of design guidelines, the various human factors guidelines from different countries should be harmonized internationally to ensure that manufacturers have access to a consistent set of guidelines. Finally, it is also important that these standards, especially for usage safety, specify tests with not just the average driver, but also with peripheral driving populations including novice and elderly drivers.by Angela Wei Ling Ho.S.M

Towards perceptual intelligence : statistical modeling of human individual and interactive behaviors

Thesis (Ph.D.)--Massachusetts Institute of Technology, Dept. of Architecture, 2000.Includes bibliographical references (p. 279-297).This thesis presents a computational framework for the automatic recognition and prediction of different kinds of human behaviors from video cameras and other sensors, via perceptually intelligent systems that automatically sense and correctly classify human behaviors, by means of Machine Perception and Machine Learning techniques. In the thesis I develop the statistical machine learning algorithms (dynamic graphical models) necessary for detecting and recognizing individual and interactive behaviors. In the case of the interactions two Hidden Markov Models (HMMs) are coupled in a novel architecture called Coupled Hidden Markov Models (CHMMs) that explicitly captures the interactions between them. The algorithms for learning the parameters from data as well as for doing inference with those models are developed and described. Four systems that experimentally evaluate the proposed paradigm are presented: (1) LAFTER, an automatic face detection and tracking system with facial expression recognition; (2) a Tai-Chi gesture recognition system; (3) a pedestrian surveillance system that recognizes typical human to human interactions; (4) and a SmartCar for driver maneuver recognition. These systems capture human behaviors of different nature and increasing complexity: first, isolated, single-user facial expressions, then, two-hand gestures and human-to-human interactions, and finally complex behaviors where human performance is mediated by a machine, more specifically, a car. The metric that is used for quantifying the quality of the behavior models is their accuracy: how well they are able to recognize the behaviors on testing data. Statistical machine learning usually suffers from lack of data for estimating all the parameters in the models. In order to alleviate this problem, synthetically generated data are used to bootstrap the models creating 'prior models' that are further trained using much less real data than otherwise it would be required. The Bayesian nature of the approach let us do so. The predictive power of these models lets us categorize human actions very soon after the beginning of the action. Because of the generic nature of the typical behaviors of each of the implemented systems there is a reason to believe that this approach to modeling human behavior would generalize to other dynamic human-machine systems. This would allow us to recognize automatically people's intended action, and thus build control systems that dynamically adapt to suit the human's purposes better.by Nuria M. Oliver.Ph.D

Potential safety applications of advanced technology. Final Report

Notes: Report covers the period Sept 1990 - June 1993. Originally dated June 1993Federal Highway Administration, Office of Safety and Traffic Operations Research and Development, McLean, Va.http://deepblue.lib.umich.edu/bitstream/2027.42/1045/2/85136.0001.001.pd

A Context Aware Classification System for Monitoring Driver’s Distraction Levels

Understanding the safety measures regarding developing self-driving futuristic cars is a concern for decision-makers, civil society, consumer groups, and manufacturers. The researchers are trying to thoroughly test and simulate various driving contexts to make these cars fully secure for road users. Including the vehicle’ surroundings offer an ideal way to monitor context-aware situations and incorporate the various hazards. In this regard, different studies have analysed drivers’ behaviour under different case scenarios and scrutinised the external environment to obtain a holistic view of vehicles and the environment. Studies showed that the primary cause of road accidents is driver distraction, and there is a thin line that separates the transition from careless to dangerous. While there has been a significant improvement in advanced driver assistance systems, the current measures neither detect the severity of the distraction levels nor the context-aware, which can aid in preventing accidents. Also, no compact study provides a complete model for transitioning control from the driver to the vehicle when a high degree of distraction is detected. The current study proposes a context-aware severity model to detect safety issues related to driver’s distractions, considering the physiological attributes, the activities, and context-aware situations such as environment and vehicle. Thereby, a novel three-phase Fast Recurrent Convolutional Neural Network (Fast-RCNN) architecture addresses the physiological attributes. Secondly, a novel two-tier FRCNN-LSTM framework is devised to classify the severity of driver distraction. Thirdly, a Dynamic Bayesian Network (DBN) for the prediction of driver distraction. The study further proposes the Multiclass Driver Distraction Risk Assessment (MDDRA) model, which can be adopted in a context-aware driving distraction scenario. Finally, a 3-way hybrid CNN-DBN-LSTM multiclass degree of driver distraction according to severity level is developed. In addition, a Hidden Markov Driver Distraction Severity Model (HMDDSM) for the transitioning of control from the driver to the vehicle when a high degree of distraction is detected. This work tests and evaluates the proposed models using the multi-view TeleFOT naturalistic driving study data and the American University of Cairo dataset (AUCD). The evaluation of the developed models was performed using cross-correlation, hybrid cross-correlations, K-Folds validation. The results show that the technique effectively learns and adopts safety measures related to the severity of driver distraction. In addition, the results also show that while a driver is in a dangerous distraction state, the control can be shifted from driver to vehicle in a systematic manner

Accident causation and pre-accidental driving situations: Part 1. Overview and general statistics

WP2 of the European Project TRACE is concerned with “Types of Situations” to analyse the causation of road traffic accidents from the pre-accidental driving situation point of view. Four complementary situations were defined: stabilized situations, intersection, specific manoeuvre and degradation scenario. To reach this objective, the analysis is based on a common methodology composed on 3 steps: the “descriptive analysis” which from general statistics will allow to identify among the studied situations those them relevant and to give their characteristics, the “in-depth analysis” allowing to obtain accident causes from the generic description of the problems identified in the previous step and the risk analysis identifying the risk of being involved in an accident taking into account the results obtained from the ‘in–depth’ level. This report is dedicated to the descriptive analysis with the identification of the most relevant scenario regarding the situation in which the driver is involved just prior the accident. The results are based on the literature review, general statistics and the analysis of the national databases available in TRACE via WP8. Because the information level differ from databases to another, the available scenario presented here for the 4 predefined types of situations are generics and some specific situations could not have be distinguished. For each situation some key indicators are given, such as prevalence, severity, KSI (killed x severely injured), etc. When it is possible, these indicators are estimated at the EU27 level