Effects of Behavior-Based Driver Feedback Systems on Commercial Long Haul Operator Safety

There are large economic and societal costs to commercial motor vehicle crashes. A majority of crashes are precipitated due to driver-related factors. Behavior-based systems that influence drivers with feedback from safety managers can help reduce driver-related risk factors. These systems harness the experience and knowledge of managers along with advanced driver telematics that monitor and record driver behaviors to positively influence driver safety. Safety solutions that focus on modifying driver behaviors thus hold promise for improving the safety record of commercial trucking. In this study, one such feedback system was examined by analyzing data from a commercial trucking fleet, treating the system deployment as a natural experiment. This made it possible, without experimental intervention, to compare drivers before and after system introduction, and to compare drivers that were subject to this system with those that drove with no supervisor feedback. Adverse event data were obtained for drivers in the fleet and weekly event rates were calculated taking into account driving exposure (in miles). Results show that drivers improved after receiving safety feedback and significantly more so than drivers that did not receive feedback

Novel roles for well-known players: from tobacco mosaic virus pests to enzymatically active assemblies

The rod-shaped nanoparticles of the widespread plant pathogen tobacco mosaic virus (TMV) have been a matter of intense debates and cutting-edge research for more than a hundred years. During the late 19th century, their behavior in filtration tests applied to the agent causing the \u27plant mosaic disease\u27 eventually led to the discrimination of viruses from bacteria. Thereafter, they promoted the development of biophysical cornerstone techniques such as electron microscopy and ultracentrifugation. Since the 1950s, the robust, helically arranged nucleoprotein complexes consisting of a single RNA and more than 2100 identical coat protein subunits have enabled molecular studies which have pioneered the understanding of viral replication and self-assembly, and elucidated major aspects of virus–host interplay, which can lead to agronomically relevant diseases. However, during the last decades, TMV has acquired a new reputation as a well-defined high-yield nanotemplate with multivalent protein surfaces, allowing for an ordered high-density presentation of multiple active molecules or synthetic compounds. Amino acid side chains exposed on the viral coat may be tailored genetically or biochemically to meet the demands for selective conjugation reactions, or to directly engineer novel functionality on TMV-derived nanosticks. The natural TMV size (length: 300 nm) in combination with functional ligands such as peptides, enzymes, dyes, drugs or inorganic materials is advantageous for applications ranging from biomedical imaging and therapy approaches over surface enlargement of battery electrodes to the immobilization of enzymes. TMV building blocks are also amenable to external control of in vitro assembly and re-organization into technically expedient new shapes or arrays, which bears a unique potential for the development of \u27smart\u27 functional 3D structures. Among those, materials designed for enzyme-based biodetection layouts, which are routinely applied, e.g., for monitoring blood sugar concentrations, might profit particularly from the presence of TMV rods: Their surfaces were recently shown to stabilize enzymatic activities upon repeated consecutive uses and over several weeks. This review gives the reader a ride through strikingly diverse achievements obtained with TMV-based particles, compares them to the progress with related viruses, and focuses on latest results revealing special advantages for enzyme-based biosensing formats, which might be of high interest for diagnostics employing \u27systems-on-a-chip\u27

Reproducible safety and efficacy of atezolizumab plus bevacizumab for HCC in clinical practice: Results of the AB-real study

IMbrave150 has established the superiority of atezolizumab plus bevacizumab over sorafenib in patients with unresectable hepatocellular carcinoma (HCC)

Adaptive Eyes: Driver Distraction and Inattention PreventionThrough Advanced Driver Assistance Systems and Behaviour-Based Safety

Technology pervades our daily living, and is increasingly integrated into the vehicle – directly affecting driving. On the one hand technology such as cell phones provoke driver distraction and inattention, whereas, on the other hand, Advanced Driver Assistance Systems (ADAS) support the driver in the driving task. The question is, can a driver successfully adapt to the ever growing technological advancements? Thus, this thesis aimed at improving safe driver behaviour by understanding the underlying psychological mechanisms that influence behavioural change. Previous research on ADAS and human attention was reviewed in the context of driver behavioural adaptation. Empirical data from multiple data sources such as driving performance data, visual behaviour data, video footage, and subjective data were analyzed to evaluate two ADAS (a brake-capacity forward collision warning system, B-FCW, and a Visual Distraction Alert System, VDA-System). Results from a field operational test (EuroFOT) showed that brake-capacity forward collision warnings lead to immediate attention allocation toward the roadway and drivers hit the brake, yet change their initial response later on by directing their eyes toward the warning source in the instrument cluster. A similar phenomenon of drivers changing initial behaviour was found in a driving simulator study assessing a Visual Distraction Alert System. Analysis showed that a Visual Distraction Alert System successfully assists drivers in redirecting attention to the relevant aspects of the driving task and significantly improves driving performance. The effects are discussed with regard to behavioural adaptation, calibration and system acceptance. Based on these findings a novel assessment for human-machine-interaction (HMI) of ADAS was introduced. Based on the contribution of this thesis and previous best-practices, a holistic safety management model on accident prevention strategies (before, during and after driving) was developed. The DO-IT BEST Feedback Model is a comprehensive feedback strategy including driver feedback at various time scales and therefore is expected to provide an added benefit for distraction and inattention prevention. The central contributions of this work are to advance research in the field of traffic psychology in the context of attention allocation strategies, and to improve the ability to design future safety systems with the human factor in focus. The thesis consists of the introduction of the conducted research, six publications in full text and a comprehensive conclusion of the publications. In brief this thesis intends to improve safe driver behaviour by understanding the underlying psychological mechanisms that influence behavioral change, thereby resulting in more attention allocation to the forward roadway, and improved vehicle control.:Abstract i Zusammenfassung iii List of included publications v Acknowledgements vii Previously published work ix Table of contents xi Preface xii 1 Chapter 1 Introduction 1 1.1 Outline 1 1.2 Objectives 2 1.3 Background 8 1.3.1 Behavioural adaption to ADAS 8 1.3.2 Driver distraction and inattention 9 2 Chapter 2 Paper I 23 3 Chapter 3 Paper II 47 4 Chapter 4 Paper III 61 5 Chapter 5 Paper IV 91 6 Chapter 6 Paper V 117 7 Chapter 7 Paper VI 143 8 Chapter 8 Conclusions and discussion 161 8.1. Contributions 161 8.2. Implications 171 8.3. Limitations and research needs 173 9 References 177 Curriculum Vitae 199 Eidesstattliche Erklärung 201Technologie durchdringt unser tägliches Leben und ist zunehmend integriert in Fahrzeuge – das Resultat sind veränderte Anforderungen an Fahrzeugführer. Einerseits besteht die Gefahr, dass er durch die Bedienung innovativer Technologien (z.B. Mobiltelefone) unachtsam wird und visuell abgelenkt ist, andererseits kann die Nutzung von Fahrerassistenzsystemen die den Fahrer bei der Fahraufgabe unterstützten einen wertvollen Beitrag zur Fahrsicherheit bieten. Die steigende Aktualität beider Problematiken wirft die Frage auf: "Kann der Fahrer sich erfolgreich dem ständig wachsenden technologischen Fortschritt anpassen?" Das Ziel der vorliegenden Arbeit ist der Erkenntnisgewinn zur Verbesserung des Fahrverhaltens indem der Verhaltensänderungen zugrunde liegende psychologische Mechanismen untersucht werden. Eine Vielzahl an Literatur zu Fahrerassistenzsystemen und Aufmerksamkeitsverteilung wurde vor dem Hintergrund von Verhaltensanpassung der Fahrer recherchiert. Daten mehrerer empirischer Quellen, z. B. Fahrverhalten, Blickbewegungen, Videomitschnitte und subjektive Daten dienten zur Datenauswertung zweier Fahrerassistenzsysteme. Im Rahmen einer Feldstudie zeigte sich, dass Bremskapazitäts-Kollisionswarnungen zur sofortigen visuellen Aufmerksamkeitsverteilung zur Fahrbahn und zum Bremsen führen, Fahrer allerdings ihre Reaktion anpassen indem sie zur Warnanzeige im Kombinationsinstrument schauen. Ein anderes Phänomen der Verhaltensanpassung wurde in einer Fahrsimulatorstudie zur Untersuchung eines Ablenkungswarnsystems, das dabei hilft die Blicke von Autofahrern stets auf die Straße zu lenken, gefunden. Diese Ergebnisse weisen nach, dass solch ein System unterstützt achtsamer zu sein und sicherer zu fahren. Die vorliegenden Befunde wurden im Zusammenhang zu Vorbefunden zur Verhaltensanpassung zu Fahrerassistenzsystemen, Fahrerkalibrierung und Akzeptanz von Technik diskutiert. Basierend auf den gewonnenen Erkenntnissen wurde ein neues Vorgehen zur Untersuchung von Mensch- Maschine-Interaktion eingeführt. Aufbauend auf den Resultaten der vorliegenden Arbeit wurde ein ganzheitliches Modell zur Fahrsicherheit und -management, das DO-IT BEST Feedback Modell, entwickelt. Das Modell bezieht sich auf multitemporale Fahrer-Feedbackstrategien und soll somit einen entscheidenen Beitrag zur Verkehrssicherheit und dem Umgang mit Fahrerunaufmerksamkeit leisten. Die zentralen Beiträge dieser Arbeit sind die Gewinnung neuer Erkenntnisse in den Bereichen der Angewandten Psychologie und der Verkehrspsychologie in den Kontexten der Aufmerksamkeitsverteilung und der Verbesserung der Gestaltung von Fahrerassistenzsystemen fokusierend auf den Bediener. Die Dissertation besteht aus einem Einleitungsteil, drei empirischen Beiträgen sowie drei Buchkapiteln und einer abschliessenden Zusammenfassung.:Abstract i Zusammenfassung iii List of included publications v Acknowledgements vii Previously published work ix Table of contents xi Preface xii 1 Chapter 1 Introduction 1 1.1 Outline 1 1.2 Objectives 2 1.3 Background 8 1.3.1 Behavioural adaption to ADAS 8 1.3.2 Driver distraction and inattention 9 2 Chapter 2 Paper I 23 3 Chapter 3 Paper II 47 4 Chapter 4 Paper III 61 5 Chapter 5 Paper IV 91 6 Chapter 6 Paper V 117 7 Chapter 7 Paper VI 143 8 Chapter 8 Conclusions and discussion 161 8.1. Contributions 161 8.2. Implications 171 8.3. Limitations and research needs 173 9 References 177 Curriculum Vitae 199 Eidesstattliche Erklärung 20

Executive summary of work package reports of the project E-Frame: Evaluation Framework for Commercial Vehicle Safety Systems and Services

The objective of the EFrame FFI project was to develop a structured framework for traffic safety evaluation in an industrial (commercial vehicle manufacturer) context. The resulting framework facilitates more efficient development of crash/injury countermeasures by identifying and focusing on the most important safety (crash) problems, providing a toolset for analyzing crashes and estimating the potential and actual effectiveness of safety systems and services and, finally, identifying the data sources needed to perform these analyses.The project was divided into several work packages whereas all the work packages produced individual reports.A general overview of the project and its results can be found in the Final VINNOVA and FFI Report (Chalmers Publication Library, Pubid. 247448) and in the final framework specification report which are equivalent to the WP1 reports (Chalmers Publication Library, Pubid. 247449). Both reports are publically available and can be found in full text either on Chalmers Publication Library or the VINNOVA FFI website.The summary reports of each work package can be found in this report in full text. The individual work package reports can be accessed upon request from the project leader and/or the author of the report

Final framework specification for Evaluation Framework for Commercial Vehicle Safety Systems and Services (EFrame)

The objective of the EFrame FFI project was to develop a structured framework for traffic safety evaluation in an industrial (commercial vehicle manufacturer) context. The resulting framework facilitates more efficient development of crash/injury countermeasures by identifying and focusing on the most important safety (crash) problems, providing a toolset for analyzing crashes and estimating the potential and actual effectiveness of safety systems and services and, finally, identifying the data sources needed to perform these analyses. A general overview of the project and its results can be found in the Final Report (Engstr\uf6m and Wege, 2016)The project started with identification of the general types of safety evaluation needed from an industrial development perspective (the Evaluation Use Cases, EUCs). The EUCs helped to keep the project focused, in spite of its broad general scope, and constituted the basis for all remaining work in the project

Evaluation Framework for Commercial Vehicle Safety Systems and Services (EFrame). Final Report

The main project aim was to develop a structured framework for traffic safety evaluation in an industrial (commercial vehicle manufacturer) context. The resulting framework facilitates more efficient development of crash/injury countermeasures by (1) identifying and focusing on the most important safety problems, (2) estimating the potential and actual safety benefits of safety systems and services and (3) identifying the data sources needed to perform these analyses.The project started with identification of the general types of safety evaluation analyses needed from an industrial development perspective (the Evaluation Use Cases, EUCs). The EUCs helped to keep the project focused, in spite of its broad general scope, and constituted the basis for all remaining work in the project (WP1). Next, an initial sketch of the framework, in terms of the data sources and analysis needed to address the EUCs were developed (WP1). This was followed by a comprehensive state-of-the-art review of existing data sources and road safety analysis methodologies that could potentially be used as components in the framework (WP2). Based on this, existing methods were adapted, or novel methods developed, to address the Evaluation Use Cases (WP3). Finally, the methods adapted/developed in WP3 were applied to a set of concrete evaluation test cases in order to demonstrate the framework and identify needs for further improvement (WP4). Based on this, the final framework was defined (WP4). Thus, the project objectives have generally been met, although further development and testing is needed on other concrete test cases beyond than those addressed in WP4.The framework has the potential to reduce the number of killed and injured in traffic by focusing industrial development and academic research on the most effective safety systems and services and increases AB Volvo’s international competitiveness by further strengthening its safety system/services offering. The project has also, thanks to its broad scope, fostered increased collaboration between different sub-fields of traffic safety analysis (e.g., passive safety, active safety and road user behavior analysis) and thus contributed to the development of a critical mass of competence at SAFER/Chalmers/Volvo in this area

Executive summary of work package reports of the project E-Frame: Evaluation Framework for Commercial Vehicle Safety Systems and Services

The objective of the EFrame FFI project was to develop a structured framework for traffic safety evaluation in an industrial (commercial vehicle manufacturer) context. The resulting framework facilitates more efficient development of crash/injury countermeasures by identifying and focusing on the most important safety (crash) problems, providing a toolset for analyzing crashes and estimating the potential and actual effectiveness of safety systems and services and, finally, identifying the data sources needed to perform these analyses.The project was divided into several work packages whereas all the work packages produced individual reports.A general overview of the project and its results can be found in the Final VINNOVA and FFI Report (Chalmers Publication Library, Pubid. 247448) and in the final framework specification report which are equivalent to the WP1 reports (Chalmers Publication Library, Pubid. 247449). Both reports are publically available and can be found in full text either on Chalmers Publication Library or the VINNOVA FFI website.The summary reports of each work package can be found in this report in full text. The individual work package reports can be accessed upon request from the project leader and/or the author of the report

Processing of Eye/Head-Tracking Data in Large-Scale Naturalistic Driving Data Sets

Driver distraction and driver inattention are frequently recognized as leading causes of crashes and incidents. Despite this fact, there are few methods available for the automatic detection of driver distraction. Eye tracking has come forward as the most promising detection technology, but the technique suffers from quality issues when used in the field over an extended period of time. Eye-tracking data acquired in the field clearly differs from what is acquired in a laboratory setting or a driving simulator, and algorithms that have been developed in these settings are often unable to operate on noisy field data. The aim of this paper is to develop algorithms for quality handling and signal enhancement of naturalistic eye- and head-tracking data within the setting of visual driver distraction. In particular, practical issues are highlighted. Developed algorithms are evaluated on large-scale field operational test data acquired in the Sweden-Michigan Field Operational Test (SeMiFOT) project, including data from 44 unique drivers and more than 10 000 trips from 13 eye-tracker-equipped vehicles. Results indicate that, by applying advanced data-processing methods, sensitivity and specificity of eyes-off-road glance detection can be increased by about 10%. In conclusion, postenhancement and quality handling is critical when analyzing large databases with naturalistic eye-tracking data. The presented algorithms provide the first holistic approach to accomplish this task

On-Scene Injury Severity Prediction (OSISP) Algorithm for Truck Occupants

The aim of this study is to develop an on-scene injury severity prediction (OSISP) algorithm for truck occupants using only accident characteristics that are feasible to assess at the scene of the accident. The purpose of developing this algorithm is to use it as a basis for a field triage tool used in traffic accidents involving trucks. In addition, the model can be valuable for recognizing important factors for improving triage protocols used in Sweden and possibly in other countries with similar traffic environments and prehospital procedures. Methods: The scope is adult truck occupants involved in traffic accidents on Swedish public roads registered in the Swedish Traffic Accident Data Acquisition (STRADA) database for calendar years 2003 to 2013. STRADA contains information reported by the police and medical data on injured road users treated at emergency hospitals. Using data from STRADA, 2 OSISP multivariate logistic regression models for deriving the probability of severe injury (defined here as having an Injury Severity Score [ISS]>15) were implemented for light and heavy trucks; that is, trucks with weight up to 3,500 kg and ≥16,500 kg, respectively. A 10-fold cross-validation procedure was used to estimate the performance of the OSISP algorithm in terms of the area under the receiver operating characteristic curve (AUC). Results: The rate of belt use was low, especially for heavy truck occupants. The OSISP models developed for light and heavy trucks achieved cross-validation AUC of 0.81 and 0.74, respectively. The AUC values obtained when the models were evaluated on all data without cross-validation were 0.87 for both light and heavy trucks. The difference in the AUC values with and without use of cross-validation indicates overfitting of the model, which may be a consequence of relatively small data sets. Belt use stands out as the most valuable predictor in both types of trucks; accident type and age are important predictors for light trucks. Conclusions: The OSISP models achieve good discriminating capability for light truck occupants and a reasonable performance for heavy truck occupants. The prediction accuracy may be increased by acquiring more data. Belt use was the strongest predictor of severe injury for both light and heavy truck occupants. There is a need for behavior-based safety programs and/or other means to encourage truck occupants to always wear a seat belt