Evaluation and Validation of Distraction Detection Algorithms on Multiple Data Sources

This study aims to evaluate algorithms designed to detect distracted driving. This includes the comparison of how efficiently they detect the state of distraction and likelihood of a crash. Four algorithms that utilize measures of cumulative glance, past glance behavior, and glance eccentricity were used to understand the distracted state of the driver and were validated on two separate data sources (i.e., simulator and naturalistic data). Additionally, an independent method for distraction detection was designed using data mining methods. This approach utilized measures like steering degree, lane offset, lateral and longitudinal velocity, and acceleration. The results showed a higher likelihood of distracted events when cumulative glances were considered. However, the state of distraction was observed to be higher when glance eccentricity was added. Additionally, it was observed that glance behavior using the four legacy algorithms were better detectors of the state of distraction as compared to the data mining method that used vehicular measures. This research has implications in understanding the state of distraction, predicting the power of different methods, and comparing approaches in different contexts (naturalistic vs simulator). These findings provide the fundamental building blocks towards designing advanced mitigation systems that give drivers feedback in instances of high crash likelihood

Ambient hues and audible cues: An approach to automotive user interface design using multi-modal feedback

The use of touchscreen interfaces for in-vehicle information, entertainment, and for the control of comfort settings is proliferating. Moreover, using these interfaces requires the same visual and manual resources needed for safe driving. Guided by much of the prevalent research in the areas of the human visual system, attention, and multimodal redundancy the Hues and Cues design paradigm was developed to make touchscreen automotive user interfaces more suitable to use while driving. This paradigm was applied to a prototype of an automotive user interface and evaluated with respects to driver performance using the dual-task, Lane Change Test (LCT). Each level of the design paradigm was evaluated in light of possible gender differences. The results of the repeated measures experiment suggests that when compared to interfaces without both the Hues and the Cues paradigm applied, the Hues and Cues interface requires less mental effort to operate, is more usable, and is more preferred. However, the results differ in the degradation in driver performance with interfaces that only have visual feedback resulting in better task times and significant gender differences in the driving task with interfaces that only have auditory feedback. Overall, the results reported show that the presentation of multimodal feedback can be useful in design automotive interfaces, but must be flexible enough to account for individual differences

From Manual Driving to Automated Driving: A Review of 10 Years of AutoUI

This paper gives an overview of the ten-year devel- opment of the papers presented at the International ACM Conference on Automotive User Interfaces and Interactive Vehicular Applications (AutoUI) from 2009 to 2018. We categorize the topics into two main groups, namely, manual driving-related research and automated driving-related re- search. Within manual driving, we mainly focus on studies on user interfaces (UIs), driver states, augmented reality and head-up displays, and methodology; Within automated driv- ing, we discuss topics, such as takeover, acceptance and trust, interacting with road users, UIs, and methodology. We also discuss the main challenges and future directions for AutoUI and offer a roadmap for the research in this area.https://deepblue.lib.umich.edu/bitstream/2027.42/153959/1/From Manual Driving to Automated Driving: A Review of 10 Years of AutoUI.pdfDescription of From Manual Driving to Automated Driving: A Review of 10 Years of AutoUI.pdf : Main articl

Policy interventions for safer, healthier people and communities

This report examines the effects of transportation policies on public health in three key areas, environment and environmental public health, community design and active transportation, and motor vehicle-related injuries and fatalities.Authored by a team of individuals headed by David R. Ragland, PhD, MPH Director of the Safe Transportation Research and Education Center (SafeTREC) at UC Berkeley and Phyllis Orrick, BA, SafeTREC Communications Director. -- p. iThis publication was made possible by cooperative agreement 3U58HM000216-05W1 between the Centers for Disease Control and Prevention and Partnership for Prevention and through contracts with Booz Allen Hamilton and the Safe Transportation Research and Education Center (SafeTREC) at UC Berkeley.1. Policies that improve the environment and environmental public health -- 2. Policies that enhance community design and promote active transportation -- 3. Policies that reduce motor vehicle-related injuries and fatalities.2011Other689

The Effects of Concurrent Driving and In-Vehicle Tasks: A Multivariate Statistical Analysis of Driver Distraction in a High-Fidelity Driving Simulator

Distracted driving continues to remain a cause of concern for a number of bodies, including government agencies, traffic safety advocacy groups and law enforcement agencies, because of its traffic safety risks. The driving simulator continues to be popular with researchers in collecting data on performance variables that provide scientific knowledge of the effects of distracted driving. Several of these performance variables can be used to quantify a single distracting effect, resulting in a multivariate dataset. A literature review of related studies revealed that researchers overwhelmingly use univariate (single and multiple) tests to analyze the resulting dataset. Performing multiple univariate tests on a multivariate dataset results in inflated Type-I error rates, and could result in inaccurately concluding that there is a distracting effect when there may not be. Researchers also provided very little or no justification for the selection of variables that were used for the univariate analysis. Being able to correctly identify a set of variables to be used to research a single distracting effect is critical in that different variables may lead to different conclusions of significant findings or not. The primary objective of this dissertation was to develop a sound statistical basis for correctly identifying a set of variables and also to demonstrate the benefits of adopting a multivariate gate-keeper test in distracted driving studies. This was demonstrated with an experiment where 67 drivers participated in a repeated measures driving simulator experiment. 14 commonly used performance variables were used as the multivariate response variables. The corresponding data were analyzed using univariate tests, and multivariate gate-keeper tests. The results indicate that ignoring the multivariate structure and performing multiple univariate tests, as has been found to be prevalent in past studies, will lead to inflated Type-I error rates and potentially misleading conclusions. The procedure developed in this study also led to the development of sound statistical basis for the selection of variables that can be best used to account for the distracting effect of the texting and phone call activities that were investigated. The findings of this study have significant educational value to the body of knowledge on distracted driving studies and any other studies that analyze multiple dependent variables for a single factor

Dream 3.0. Documentation of references supporting the links in the classification scheme

Both the Driving Reliability and Error Analysis Method (DREAM; Ljung, 2002) and the SafetyNet Accident Causation System (SNACS; Ljung, 2006) have been successfully used as tools for accident analysis in Sweden as well as in other European countries. While the drivervehicle/ traffic environment-organisation triad are used as frames of reference and the Contextual Control Model (COCOM; Hollnagel, 1998) is used to organise human cognition, the links in the classification schemes have not been established by referring to literature. The aim of this literature review is therefore to investigate the empirical support for the links in the classification scheme of DREAM 3.0 (an updated version of DREAM/SNACS)

Mobile Application to support fuel-efficient driving through situation awareness

Abstract. Situation awareness is usually conceptualized as design and implementation principles for safety critical industries like aviation or military. Finland was one of the first countries in the world to establish an intelligent transport systems (ITS) strategy in 2009. Increasing the situation awareness in traffic is regarded as one of the means to implement the strategy. In the theoretical part of this thesis, we explore the use of situation awareness and context awareness in intelligent transport systems. Particularly, the thesis focuses on summarizing proper design and evaluation principles to provide situation awareness support for fuel efficient driving. These guidelines were exploited in implementing a mobile application, called Driving Coach Mobile Application in the practical part of the thesis. The purpose of the application is to provide awareness to the drivers about how they can save fuel. Driving Coach Mobile Application’s accordance of design and implementation principles to situation awareness support is validated by user study with simulated data focused on usability, usefulness and fuel efficiency awareness support. The results of this thesis can be used in fleet management planning, city planning as well as in personal driving, for example.Tilannetietoinen mobiilisovellus polttoainetaloudellisen ajamisen tueksi. Tiivistelmä. Turvallisuuskriittisissä teollisuuden osa-alueissa kuten ilmailussa tai sotilaallisessa toiminnassa, eri toimijoiden tilannetietoisuuden parantamiseen tähtäävät suunnittelu- sekä toteutusperiaatteet ovat olleet merkittävässä roolissa jo pitkään. Suomi oli maailman ensimmäisiä maita, jotka julkistivat älykkään liikenteen strategian jo vuonna 2009. Tilannetietoisuuden parantaminen liikenteessä on edelleen eräs tämän strategian toimeenpanomuoto. Tämän työn teoreettisessa osassa tutkitaan avulla tilannetietoisuuden sekä toimintatilanteesta tietoisuuden soveltamista älyliikenteessä. Erityisesti tarkastellaan suunnittelu- sekä evaluointiperiaatteita polttoainetalouden tehokkuuden lisäämiselle tilannetietoisuuden avulla. Työn käytännön osuudessa sovellettiin näitä periaatteita mobiilisovelluksen toteuttamiseksi. Mobiilisovellus tukee kuljettajien polttoainetehokkaampaa ajamista. Sovellus testattiin käytettävyyden, hyödyllisyyden sekä polttoainetehokkaan ajamisen tuen suhteen. Sovellusta voidaan käyttää esimerkiksi kaupunkisuunnittelussa, autokannan toiminnan tarkkailemisessa tai vaikka henkilökohtaisen ajotavan arvioinnissa

Classification of Drivers' Workload Using Physiological Signals in Conditional Automation

The use of automation in cars is increasing. In future vehicles, drivers will no longer be in charge of the main driving task and may be allowed to perform a secondary task. However, they might be requested to regain control of the car if a hazardous situation occurs (i.e., conditionally automated driving). Performing a secondary task might increase drivers' mental workload and consequently decrease the takeover performance if the workload level exceeds a certain threshold. Knowledge about the driver's mental state might hence be useful for increasing safety in conditionally automated vehicles. Measuring drivers' workload continuously is essential to support the driver and hence limit the number of accidents in takeover situations. This goal can be achieved using machine learning techniques to evaluate and classify the drivers' workload in real-time. To evaluate the usefulness of physiological data as an indicator for workload in conditionally automated driving, three physiological signals from 90 subjects were collected during 25 min of automated driving in a fixed-base simulator. Half of the participants performed a verbal cognitive task to induce mental workload while the other half only had to monitor the environment of the car. Three classifiers, sensor fusion and levels of data segmentation were compared. Results show that the best model was able to successfully classify the condition of the driver with an accuracy of 95%. In some cases, the model benefited from sensors' fusion. Increasing the segmentation level (e.g., size of the time window to compute physiological indicators) increased the performance of the model for windows smaller than 4 min, but decreased for windows larger than 4 min. In conclusion, the study showed that a high level of drivers' mental workload can be accurately detected while driving in conditional automation based on 4-min recordings of respiration and skin conductance

On driver behavior recognition for increased safety:A roadmap

Advanced Driver-Assistance Systems (ADASs) are used for increasing safety in the automotive domain, yet current ADASs notably operate without taking into account drivers’ states, e.g., whether she/he is emotionally apt to drive. In this paper, we first review the state-of-the-art of emotional and cognitive analysis for ADAS: we consider psychological models, the sensors needed for capturing physiological signals, and the typical algorithms used for human emotion classification. Our investigation highlights a lack of advanced Driver Monitoring Systems (DMSs) for ADASs, which could increase driving quality and security for both drivers and passengers. We then provide our view on a novel perception architecture for driver monitoring, built around the concept of Driver Complex State (DCS). DCS relies on multiple non-obtrusive sensors and Artificial Intelligence (AI) for uncovering the driver state and uses it to implement innovative Human–Machine Interface (HMI) functionalities. This concept will be implemented and validated in the recently EU-funded NextPerception project, which is briefly introduced