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Johan Engström

Bio: Johan Engström is an academic researcher from Virginia Tech. The author has contributed to research in topics: Poison control & Active safety. The author has an hindex of 25, co-authored 65 publications receiving 3183 citations. Previous affiliations of Johan Engström include Volvo & Chalmers University of Technology.


Papers
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Journal ArticleDOI
TL;DR: In this article, the effects of visual and cognitive load on driving performance and driver state were systematically investigated by means of artificial, or surrogate, In-vehicle Information Systems (S-IVIS).
Abstract: As part of the HASTE European Project, effects of visual and cognitive demand on driving performance and driver state were systematically investigated by means of artificial, or surrogate, In-vehicle Information Systems (S-IVIS). The present paper reports results from simulated and real motorway driving. Data were collected in a fixed base simulator, a moving base simulator and an instrumented vehicle driven in real traffic. The data collected included speed, lane keeping performance, steering wheel movements, eye movements, physiological signals and self-reported driving performance. The results show that the effects of visual and cognitive load affect driving performance in qualitatively different ways. Visual demand led to reduced speed and increased lane keeping variation. By contrast, cognitive load did not affect speed and resulted in reduced lane keeping variation. Moreover, the cognitive load resulted in increased gaze concentration towards the road centre. Both S-IVIS had an effect on physiological signals and the drivers’ assessment of their own driving performance. The study also investigated differences between the three experimental settings (static simulator, moving base simulator and field). The results are discussed with respect to the development of a generic safety test regime for In-vehicle Information Systems.

756 citations

Journal ArticleDOI
TL;DR: In this article, the eye-movement measures were found to be highly sensitive to the demands of visual and auditory in-vehicle tasks as well as driving task demands, and two new measures, Percent road centre and Standard deviation of gaze, were found more sensitive, more robust, more reliable, and easier to calculate than established glance-based measures.
Abstract: Eye-movement measures were found to be highly sensitive to the demands of visual and auditory in-vehicle tasks as well as driving task demands. Two newer measures, Percent road centre and Standard deviation of gaze, were found to be more sensitive, more robust, more reliable, and easier to calculate than established glance-based measures. The eye-movement measures were collected by two partners within the EU project HASTE to determine their sensitivity to increasingly demanding in-vehicle tasks by means of artificial, or surrogate, In-vehicle Information Systems (S-IVIS). Data from 119 subjects were collected from four routes: a motorway in real traffic with an instrumented vehicle, a motorway in a fixed base simulator, and from rural roads in two different fixed base simulators. As the visual task became more difficult, drivers looked less at the road centre area ahead, and looked at the display more often, for longer periods, and for more varied durations. The auditory task led to an increasing gaze concentration to road centre. Gaze concentration to the road centre area was also found as driving task complexity increased, as shown in differences between rural curved- and straight sections, between rural and motorway road types, and between simulator and field motorways.

487 citations

Journal ArticleDOI
TL;DR: There is an acute need for a unifying conceptual framework in order to synthesize these results and make useful generalizations on driving styles, and there is a considerable potential for increasing road safety by means of behavior modification.
Abstract: Objective:The aim of this study was to outline a conceptual framework for understanding driving style and, on this basis, review the state-of-the-art research on driving styles in relation to road safety.Background:Previous research has indicated a relationship between the driving styles adopted by drivers and their crash involvement. However, a comprehensive literature review of driving style research is lacking.Method:A systematic literature search was conducted, including empirical, theoretical, and methodological research, on driving styles related to road safety.Results:A conceptual framework was proposed whereby driving styles are viewed in terms of driving habits established as a result of individual dispositions as well as social norms and cultural values. Moreover, a general scheme for categorizing and operationalizing driving styles was suggested. On this basis, existing literature on driving styles and indicators was reviewed. Links between driving styles and road safety were identified and ind...

265 citations

BookDOI
19 Mar 2014
TL;DR: The SAFER Vehicle and Traffic Safety Centre at Chalmers, Gothenburg, Sweden as discussed by the authors is a joint research unit where 25 partners from the Swedish automotive industry, academia and authorities cooperate to make a center of excellence within the field of vehicle and traffic safety (see www.chalmers.se/safer ).
Abstract: This work was sponsored by the second Strategic Highway Research Program (SHRP 2), which is administered by the Transportation Research Board of the National Academies. This project was managed by Ken Campbell, Chief Program Officer for SHRP 2 Safety , and Jim Hedlund, SHRP 2 Safety Coordinator . The research reported on herein was performed by the main contractor SAFER Vehicle and Traffic Safety Centre at Chalmers, Gothenburg, Sweden. SAFER is a joint research unit where 25 partners from the Swedish automotive industry, academia and authorities cooperate to make a center of excellence within the field of vehicle and traffic safety (see www.chalmers.se/safer ). The host and legal entity SAFER is Chalmers University of Technology. Principle Investigator Tr ent Victor is Adjunct Professor at Chalmers and worked on the project as borrowed personnel to Chalmers but his main employer is Volvo Cars. The other authors of this report are Co - PI Marco Dozza, Jonas Bargman, and Christian - Nils Boda of Chalmers Universi ty of Technology (as a SAFER partner) ; Johan Engstrom and Gustav Markkula of Volvo Group Trucks Technology (as a SAFER partner) ; John D. Lee of University of Wisconsin - Madison (as a consultant to SAFER); and Carol Flannagan of University of Michigan Transp ortation Research Institute (UMTRI) (as a consultant to SAFER). The authors acknowledge the contributions to this research from Ines Heinig, Vera Lisovskaja, Olle Nerman, Holger Rootzen, Dmitrii Zholud, Helena Gellerman , Leyla Vujic, Martin Rensfeldt, Stefan Venbrant, Akhil Krishnan, Bharat Mohan Redrouthu, Daniel Nilsson of Chalmers; Mikael Ljung - Aust of Volvo Cars; Erwin Boer; Christer Ahlstrom and Omar Bagdadi of VTI.

238 citations

Patent
Johan Engström1, Trent Victor1
23 Mar 2005
TL;DR: In this article, a system and method for real-time, automatic, recognition of large time-scale driving patterns employs a statistical pattern recognition framework, implemented by means of feed-forward neural network utilizing models developed for recognizing, for example, four classes of driving environments, namely highway, main road, suburban traffic and city traffic, from vehicle performance data.
Abstract: System and method for real-time, automatic, recognition of large time-scale driving patterns employs a statistical pattern recognition framework, implemented by means of feed-forward neural network utilizing models developed for recognizing, for example, four classes of driving environments, namely highway, main road, suburban traffic and city traffic, from vehicle performance data. A vehicle control application effects changes in vehicle performance aspects based on the recognized driving environment.

233 citations


Cited by
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Journal ArticleDOI
TL;DR: The technical aspect of automated driving is surveyed, with an overview of available datasets and tools for ADS development and many state-of-the-art algorithms implemented and compared on their own platform in a real-world driving setting.
Abstract: Automated driving systems (ADSs) promise a safe, comfortable and efficient driving experience. However, fatalities involving vehicles equipped with ADSs are on the rise. The full potential of ADSs cannot be realized unless the robustness of state-of-the-art is improved further. This paper discusses unsolved problems and surveys the technical aspect of automated driving. Studies regarding present challenges, high-level system architectures, emerging methodologies and core functions including localization, mapping, perception, planning, and human machine interfaces, were thoroughly reviewed. Furthermore, many state-of-the-art algorithms were implemented and compared on our own platform in a real-world driving setting. The paper concludes with an overview of available datasets and tools for ADS development.

851 citations

Journal ArticleDOI
TL;DR: In this article, the effects of visual and cognitive load on driving performance and driver state were systematically investigated by means of artificial, or surrogate, In-vehicle Information Systems (S-IVIS).
Abstract: As part of the HASTE European Project, effects of visual and cognitive demand on driving performance and driver state were systematically investigated by means of artificial, or surrogate, In-vehicle Information Systems (S-IVIS). The present paper reports results from simulated and real motorway driving. Data were collected in a fixed base simulator, a moving base simulator and an instrumented vehicle driven in real traffic. The data collected included speed, lane keeping performance, steering wheel movements, eye movements, physiological signals and self-reported driving performance. The results show that the effects of visual and cognitive load affect driving performance in qualitatively different ways. Visual demand led to reduced speed and increased lane keeping variation. By contrast, cognitive load did not affect speed and resulted in reduced lane keeping variation. Moreover, the cognitive load resulted in increased gaze concentration towards the road centre. Both S-IVIS had an effect on physiological signals and the drivers’ assessment of their own driving performance. The study also investigated differences between the three experimental settings (static simulator, moving base simulator and field). The results are discussed with respect to the development of a generic safety test regime for In-vehicle Information Systems.

756 citations

Journal ArticleDOI
TL;DR: The risk of a crash or near-crash among novice drivers increased with the performance of many secondary tasks, including texting and dialing cell phones, and among experienced drivers, the prevalence of high-risk attention to secondary tasks increased over time.
Abstract: BackgroundDistracted driving attributable to the performance of secondary tasks is a major cause of motor vehicle crashes both among teenagers who are novice drivers and among adults who are experienced drivers MethodsWe conducted two studies on the relationship between the performance of secondary tasks, including cell-phone use, and the risk of crashes and near-crashes To facilitate objective assessment, accelerometers, cameras, global positioning systems, and other sensors were installed in the vehicles of 42 newly licensed drivers (163 to 170 years of age) and 109 adults with more driving experience ResultsDuring the study periods, 167 crashes and near-crashes among novice drivers and 518 crashes and near-crashes among experienced drivers were identified The risk of a crash or near-crash among novice drivers increased significantly if they were dialing a cell phone (odds ratio, 832; 95% confidence interval [CI], 283 to 2442), reaching for a cell phone (odds ratio, 705; 95% CI, 264 to 1883)

619 citations

Journal ArticleDOI
07 Dec 2012-Sensors
TL;DR: It is concluded that by designing a hybrid drowsiness detection system that combines non-intusive physiological measures with other measures one would accurately determine the drowsy level of a driver.
Abstract: In recent years, driver drowsiness has been one of the major causes of road accidents and can lead to severe physical injuries, deaths and significant economic losses. Statistics indicate the need of a reliable driver drowsiness detection system which could alert the driver before a mishap happens. Researchers have attempted to determine driver drowsiness using the following measures: (1) vehicle-based measures; (2) behavioral measures and (3) physiological measures. A detailed review on these measures will provide insight on the present systems, issues associated with them and the enhancements that need to be done to make a robust system. In this paper, we review these three measures as to the sensors used and discuss the advantages and limitations of each. The various ways through which drowsiness has been experimentally manipulated is also discussed. We conclude that by designing a hybrid drowsiness detection system that combines non-intusive physiological measures with other measures one would accurately determine the drowsiness level of a driver. A number of road accidents might then be avoided if an alert is sent to a driver that is deemed drowsy.

583 citations