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Like many low- and middle-income countries, Nepal is experiencing a massive motorization, predominantly from increased use of motorcycles which is driving a surge in road-related injuries and fatalities. Motorcycles and their riders have been identified as a focal point for road traffic injury prevention measures. While helmet use is mandatory for both motorcycle drivers and passengers, fines for helmet non-use are only levied on drivers, not on passengers, and it is unclear how this unequal enforcement translates to helmet use rates in Nepal. Hence, a video-based observation on motorcyclists’ helmet use was conducted alongside a questionnaire survey on fatalism, perceived police enforcement, risk-taking personality, and perceived usefulness of helmets. For the observation and questionnaire survey, seven rural and urban sites were selected from all seven provinces of Nepal, representing varied populations, road environments, and elevations. The observation of the helmet use behavior of 2548 motorcycle riders revealed an alarming picture of helmet use in Nepal. While more than 98% of observed motorcycle drivers in Nepal used a motorcycle helmet, less than 1% of observed passengers did so. Interviews of 220 riders show that the absence of a fine for helmet non-use by passengers is accompanied by an unawareness of the traffic law, where only 11.8% of respondents knew about the mandatory helmet use law for passengers. Unhelmeted riders had a significantly higher attribution of road related crashes to fate, compared with riders that used a helmet. Results of this study can serve as an evidence base for revisions of Nepal’s Vehicle and Transportation Management Act in regard to traffic rule enforcement and fines. They further show the global importance of comprehensive regulation on safety related behaviors of road users. The feasibility of more comprehensive enforcement is discussed against the background of helmet availability for passengers.
Felix Wilhelm Siebert; Lennart Hellmann; Puspa Raj Pant; Hanhe Lin; Rüdiger Trimpop. Disparity of motorcycle helmet use in Nepal – Weak law enforcement or riders' reluctance? Transportation Research Part F: Traffic Psychology and Behaviour 2021, 79, 72 -83.
AMA StyleFelix Wilhelm Siebert, Lennart Hellmann, Puspa Raj Pant, Hanhe Lin, Rüdiger Trimpop. Disparity of motorcycle helmet use in Nepal – Weak law enforcement or riders' reluctance? Transportation Research Part F: Traffic Psychology and Behaviour. 2021; 79 ():72-83.
Chicago/Turabian StyleFelix Wilhelm Siebert; Lennart Hellmann; Puspa Raj Pant; Hanhe Lin; Rüdiger Trimpop. 2021. "Disparity of motorcycle helmet use in Nepal – Weak law enforcement or riders' reluctance?" Transportation Research Part F: Traffic Psychology and Behaviour 79, no. : 72-83.
Shared e-scooters are introduced as a new form of mobility around the world. Alongside this rise in micromobility, e-scooter crashes are reported, and e-scooter riders are injured and killed in traffic. Little research has been conducted on the relation between ergonomics and the safe use of e-scooters, and it is unclear whether e-scooter riders know about prevailing e-scooter related regulation and if they adhere to existing regulation in traffic. We conducted a field observation (n = 2972) in combination with a questionnaire survey (n = 156), to investigate the influence of ergonomics on the safe use of shared e-scooters, and to explore riders’ knowledge and self-reported behavior. Riders’ brake readiness, dual use (two riders per vehicle), and helmet use was registered, and specific knowledge about the braking system of e-scooters was assessed, alongside knowledge about road rules and reported past safety related behavior. Results reveal a clear effect of braking system design, with significantly more riders readying the left hand brake, in comparison with the right hand or foot brake (depending on the e-scooter model). This was found regardless of the brake-lever-to-wheel coupling, indicating that the preference for the left hand brake can be detrimental to targeted braking of the front or rear wheel. Only one third of respondents could correctly identify the basic braking system of the shared e-scooter they had last used. In addition, high shares of illegal behavior were reported by riders. Implications of these findings for the safe operation of e-scooters, their ergonomic design, and road safety regulation are discussed.
Felix Wilhelm Siebert; Madlen Ringhand; Felix Englert; Michael Hoffknecht; Timothy Edwards; Matthias Rötting. Braking bad – Ergonomic design and implications for the safe use of shared E-scooters. Safety Science 2021, 140, 105294 .
AMA StyleFelix Wilhelm Siebert, Madlen Ringhand, Felix Englert, Michael Hoffknecht, Timothy Edwards, Matthias Rötting. Braking bad – Ergonomic design and implications for the safe use of shared E-scooters. Safety Science. 2021; 140 ():105294.
Chicago/Turabian StyleFelix Wilhelm Siebert; Madlen Ringhand; Felix Englert; Michael Hoffknecht; Timothy Edwards; Matthias Rötting. 2021. "Braking bad – Ergonomic design and implications for the safe use of shared E-scooters." Safety Science 140, no. : 105294.
Cycling behavior remains a key issue for explaining several traffic causalities occurring every day. However, recent studies have shown how the assessment of the own safety-related behaviors on the road may substantially differ from how third parties assess them. Thus, the aim of this study was to evaluate the differences between cyclists’ self-reported behavior and the proxy-reported behavior that other (non-cyclist) road users perceive from bike riders. For this purpose, this study used data from two samples: (i) 1064 cyclists (M = 32.83 years) answering the Cycling Behavior Questionnaire—CBQ, and (ii) 1070 non-cyclists (M = 30.83 years) answering an adapted version of the CBQ for external raters—ECBQ. The results show how the self-reported and proxy-reported behaviors of cyclists greatly differ in terms of all behavioral factors composing the CBQ model, i.e., traffic violations, riding errors, and positive behaviors. Also, external raters (non-cyclists) are those targeting significantly riskier behaviors than those self-reported by cyclists. These discrepancies between perceived behaviors may give rise to conflicting viewpoints on the interaction between bicycle riders and other road users. Therefore, this study underscores the importance of behavioral awareness, providing highlights for future studies on the behavioral interaction between cyclists and other road users. Results can be used to improve the road safety of all road users by giving indications on self-and proxy-perceived safety-related behaviors and visibility of protective riding habits.
Sergio Useche; Javier Gene-Morales; Felix Siebert; Francisco Alonso; Luis Montoro. “Not as Safe as I believed”: Differences in Perceived and Self-Reported Cycling Behavior between Riders and Non-Riders. Sustainability 2021, 13, 1614 .
AMA StyleSergio Useche, Javier Gene-Morales, Felix Siebert, Francisco Alonso, Luis Montoro. “Not as Safe as I believed”: Differences in Perceived and Self-Reported Cycling Behavior between Riders and Non-Riders. Sustainability. 2021; 13 (4):1614.
Chicago/Turabian StyleSergio Useche; Javier Gene-Morales; Felix Siebert; Francisco Alonso; Luis Montoro. 2021. "“Not as Safe as I believed”: Differences in Perceived and Self-Reported Cycling Behavior between Riders and Non-Riders." Sustainability 13, no. 4: 1614.
The spatial behavior of robots working alongside humans critically influences the experience of comfort and personal space of users. The spatial behavior of service robots is especially important, as they move in close proximity to their users. To identify acceptable spatial behavior of Follow Me robots, we conducted an experimental study with 24 participants. In a within-subject design, human-robot distance was varied within the personal space (0.5 and 1.0 m) and social space (1.5 and 2.0 m). In all conditions, the robot carried a personal item of the participants. After each condition, the subjective experience of users in their interaction with the robot was assessed on the dimensions of trust, likeability, human likeness, comfort, expectation conformity, safety, and unobtrusiveness. The results show that the subjective experience of participants during the interaction with the Follow Me robot was generally more positive in the social distance conditions (1.5 and 2.0 m) than in the personal distance conditions (0.5 and 1 m). Interestingly, the following behavior was not perceived as comparable to human-human following behavior in the 0.5 and 2.0 m conditions, which were rated as either closer than human following or further away. This result, in combination with the more positive user experience in the social space conditions, illustrates that an exact transfer of interaction conventions from human-human interaction to human-robot interaction may not be feasible. And while users generally rate the interaction with Follow Me robots as positive, the following-distance of robots will need to be considered to optimize robot-behavior for user acceptance.
Felix Wilhelm Siebert; Johannes Pickl; JacobE Klein; Matthias Rötting; Eileen Roesler. Let’s Not Get Too Personal – Distance Regulation for Follow Me Robots. Communications in Computer and Information Science 2020, 459 -467.
AMA StyleFelix Wilhelm Siebert, Johannes Pickl, JacobE Klein, Matthias Rötting, Eileen Roesler. Let’s Not Get Too Personal – Distance Regulation for Follow Me Robots. Communications in Computer and Information Science. 2020; ():459-467.
Chicago/Turabian StyleFelix Wilhelm Siebert; Johannes Pickl; JacobE Klein; Matthias Rötting; Eileen Roesler. 2020. "Let’s Not Get Too Personal – Distance Regulation for Follow Me Robots." Communications in Computer and Information Science , no. : 459-467.
Automated detection of motorcycle helmet use through video surveillance can facilitate efficient education and enforcement campaigns that increase road safety. However, existing detection approaches have a number of shortcomings, such as the inabilities to track individual motorcycles through multiple frames, or to distinguish drivers from passengers in helmet use. Furthermore, datasets used to develop approaches are limited in terms of traffic environments and traffic density variations. In this paper, we propose a CNN-based multi-task learning (MTL) method for identifying and tracking individual motorcycles, and register rider specific helmet use.We further release the HELMET dataset, which includes 91,000 annotated frames of 10,006 individual motorcycles from 12 observation sites in Myanmar. Along with the dataset, we introduce an evaluation metric for helmet use and rider detection accuracy, which can be used as a benchmark for evaluating future detection approaches.We show that the use of MTL for concurrent visual similarity learning and helmet use classification improves the efficiency of our approach compared to earlier studies, allowing a processing speed of more than 8 FPS on consumer hardware, and a weighted average F-measure of 67.3% for detecting the number of riders and helmet use of tracked motorcycles. Our work demonstrates the capability of deep learning as a highly accurate and resource efficient approach to collect critical road safety related data.
Hanhe Lin; Jeremiah D. Deng; Deike Albers; Felix Wilhelm Siebert. Helmet Use Detection of Tracked Motorcycles Using CNN-Based Multi-Task Learning. IEEE Access 2020, 8, 162073 -162084.
AMA StyleHanhe Lin, Jeremiah D. Deng, Deike Albers, Felix Wilhelm Siebert. Helmet Use Detection of Tracked Motorcycles Using CNN-Based Multi-Task Learning. IEEE Access. 2020; 8 (99):162073-162084.
Chicago/Turabian StyleHanhe Lin; Jeremiah D. Deng; Deike Albers; Felix Wilhelm Siebert. 2020. "Helmet Use Detection of Tracked Motorcycles Using CNN-Based Multi-Task Learning." IEEE Access 8, no. 99: 162073-162084.
Robots that are designed to work in close proximity to humans are required to move and act in a way that ensures social acceptance by their users. Hence, a robot's proximal behavior toward a human is a main concern, especially in human-robot interaction that relies on relatively close proximity. This study investigated how the distance and lateral offset of “Follow Me” robots influences how they are perceived by humans. To this end, a Follow Me robot was built and tested in a user study for a number of subjective variables. A total of 18 participants interacted with the robot, with the robot's lateral offset and distance varied in a within-subject design. After each interaction, participants were asked to rate the movement of the robot on the dimensions of comfort, expectancy conformity, human likeness, safety, trust, and unobtrusiveness. Results show that users generally prefer robot following distances in the social space, without a lateral offset. However, we found a main influence of affinity for technology, as those participants with a high affinity for technology preferred closer following distances than participants with low affinity for technology. The results of this study show the importance of user-adaptiveness in human-robot-interaction.
Felix Wilhelm Siebert; JacobE Klein; Matthias Rötting; Eileen Roesler. The Influence of Distance and Lateral Offset of Follow Me Robots on User Perception. Frontiers in Robotics and AI 2020, 7, 1 .
AMA StyleFelix Wilhelm Siebert, JacobE Klein, Matthias Rötting, Eileen Roesler. The Influence of Distance and Lateral Offset of Follow Me Robots on User Perception. Frontiers in Robotics and AI. 2020; 7 ():1.
Chicago/Turabian StyleFelix Wilhelm Siebert; JacobE Klein; Matthias Rötting; Eileen Roesler. 2020. "The Influence of Distance and Lateral Offset of Follow Me Robots on User Perception." Frontiers in Robotics and AI 7, no. : 1.
Since smooth pursuit eye movements can be used without calibration in spontaneous gaze interaction, the intuitiveness of the gaze interface design has been a topic of great interest in the human-computer interaction field. However, since most related research focuses on curved smooth-pursuit trajectories, the design issues of linear trajectories are poorly understood. Hence, this study evaluated the user performance of gaze interfaces based on linear smooth pursuit eye movements. We conducted an experiment to investigate how the number of objects (6, 8, 10, 12, or 15) and object moving speed (7.73 ˚/s vs. 12.89 ˚/s) affect the user performance in a gaze-based interface. Results show that the number and speed of the displayed objects influence users’ performance with the interface. The number of objects significantly affected the correct and false detection rates when selecting objects in the display. Participants’ performance was highest on interfaces containing 6 and 8 objects and decreased for interfaces with 10, 12, and 15 objects. Detection rates and orientation error were significantly influenced by the moving speed of displayed objects. Faster moving speed (12.89 ˚/s) resulted in higher detection rates and smaller orientation error compared to slower moving speeds (7.73 ˚/s). Our findings can help to enable a calibration-free accessible interaction with gaze interfaces.
Zhe Zeng; Felix Siebert; Antje Christine Venjakob; Matthias Roetting. Calibration-free gaze interfaces based on linear smooth pursuit. Journal of Eye Movement Research 2020, 13, 1 .
AMA StyleZhe Zeng, Felix Siebert, Antje Christine Venjakob, Matthias Roetting. Calibration-free gaze interfaces based on linear smooth pursuit. Journal of Eye Movement Research. 2020; 13 (1):1.
Chicago/Turabian StyleZhe Zeng; Felix Siebert; Antje Christine Venjakob; Matthias Roetting. 2020. "Calibration-free gaze interfaces based on linear smooth pursuit." Journal of Eye Movement Research 13, no. 1: 1.
The continuous motorization of traffic has led to a sustained increase in the global number of road related fatalities and injuries. To counter this, governments are focusing on enforcing safe and law-abiding behavior in traffic. However, especially in developing countries where the motorcycle is the main form of transportation, there is a lack of comprehensive data on the safety-critical behavioral metric of motorcycle helmet use. This lack of data prohibits targeted enforcement and education campaigns which are crucial for injury prevention. Hence, we have developed an algorithm for the automated registration of motorcycle helmet usage from video data, using a deep learning approach. Based on 91,000 annotated frames of video data, collected at multiple observation sites in 7 cities across the country of Myanmar, we trained our algorithm to detect active motorcycles, the number and position of riders on the motorcycle, as well as their helmet use. An analysis of the algorithm's accuracy on an annotated test data set, and a comparison to available human-registered helmet use data reveals a high accuracy of our approach. Our algorithm registers motorcycle helmet use rates with an accuracy of −4.4% and +2.1% in comparison to a human observer, with minimal training for individual observation sites. Without observation site specific training, the accuracy of helmet use detection decreases slightly, depending on a number of factors. Our approach can be implemented in existing roadside traffic surveillance infrastructure and can facilitate targeted data-driven injury prevention campaigns with real-time speed. Implications of the proposed method, as well as measures that can further improve detection accuracy are discussed.
Felix Wilhelm Siebert; Hanhe Lin. Detecting motorcycle helmet use with deep learning. Accident Analysis & Prevention 2019, 134, 105319 .
AMA StyleFelix Wilhelm Siebert, Hanhe Lin. Detecting motorcycle helmet use with deep learning. Accident Analysis & Prevention. 2019; 134 ():105319.
Chicago/Turabian StyleFelix Wilhelm Siebert; Hanhe Lin. 2019. "Detecting motorcycle helmet use with deep learning." Accident Analysis & Prevention 134, no. : 105319.
Current implementations of automated driving rely on the driver to monitor the vehicle and be ready to assume control in situations that the automation cannot successfully manage. However, research has shown that drivers are not able to monitor an automated vehicle for longer periods of time, as the monotonous monitoring task leads to attention reallocation or fatigue. Driver involvement in the automated driving task promises to counter this effect. The authors researched how the implementation of a haptic human–vehicle interface, which allows the driver to adjust driving parameters and initiate manoeuvres, influences the subjective experience of drivers in automated vehicles. In a simulator study, they varied the level of control that drivers have over the vehicle, between manual driving, automated driving without the possibility to adjust the automation, as well as automated driving with the possibility to initiate manoeuvres and adjust driving parameters of the vehicle. Results show that drivers have a higher level of perceived control and perceived level of responsibility when they have the ability to interact with the automated vehicle through the haptic interface. The authors conclude that the possibility to interact with automated vehicles can be beneficial for driver experience and safety.
Felix Wilhelm Siebert; Fabian Radtke; Erin Kiyonaga; Rainer Höger. Adjustable automation and manoeuvre control in automated driving. IET Intelligent Transport Systems 2019, 13, 1780 -1784.
AMA StyleFelix Wilhelm Siebert, Fabian Radtke, Erin Kiyonaga, Rainer Höger. Adjustable automation and manoeuvre control in automated driving. IET Intelligent Transport Systems. 2019; 13 (12):1780-1784.
Chicago/Turabian StyleFelix Wilhelm Siebert; Fabian Radtke; Erin Kiyonaga; Rainer Höger. 2019. "Adjustable automation and manoeuvre control in automated driving." IET Intelligent Transport Systems 13, no. 12: 1780-1784.
The continuous motorization of traffic has led to a sustained increase in the global number of road related fatalities and injuries. To counter this, governments are focusing on enforcing safe and law-abiding behavior in traffic. However, especially in developing countries where the motorcycle is the main form of transportation, there is a lack of comprehensive data on the safety-critical behavioral metric of motorcycle helmet use. This lack of data prohibits targeted enforcement and education campaigns which are crucial for injury prevention. Hence, we have developed an algorithm for the automated registration of motorcycle helmet usage from video data, using a deep learning approach. Based on 91,000 annotated frames of video data, collected at multiple observation sites in 7 cities across the country of Myanmar, we trained our algorithm to detect active motorcycles, the number and position of riders on the motorcycle, as well as their helmet use. An analysis of the algorithm's accuracy on an annotated test data set, and a comparison to available human-registered helmet use data reveals a high accuracy of our approach. Our algorithm registers motorcycle helmet use rates with an accuracy of -4.4% and +2.1% in comparison to a human observer, with minimal training for individual observation sites. Without observation site specific training, the accuracy of helmet use detection decreases slightly, depending on a number of factors. Our approach can be implemented in existing roadside traffic surveillance infrastructure and can facilitate targeted data-driven injury prevention campaigns with real-time speed. Implications of the proposed method, as well as measures that can further improve detection accuracy are discussed.
Felix Wilhelm Siebert; Hanhe Lin. Detecting motorcycle helmet use with deep learning. 2019, 1 .
AMA StyleFelix Wilhelm Siebert, Hanhe Lin. Detecting motorcycle helmet use with deep learning. . 2019; ():1.
Chicago/Turabian StyleFelix Wilhelm Siebert; Hanhe Lin. 2019. "Detecting motorcycle helmet use with deep learning." , no. : 1.
While the introduction of highly automated vehicles promises lower accident numbers, a main requirement for wide use of these vehicles will be the acceptance by drivers. In this study a crucial variable for the acceptance of highly automated vehicles, the vehicle to vehicle distance expressed in time headway, was researched in a driving simulator. Research has shown that time headway distances, perceived as comfortable in self-driving and assisted driving with adaptive cruise control, remain constant over a range of different speeds. This study aims to test these findings for highly automated driving. Since time headway is perceived visually, the driving situation was varied to investigate the influence of visibility on the subjective comfort of the driver in a highly automated driving situation. In a within-subject design, drivers followed a passenger car in clear weather conditions, the same passenger car in fog which occluded parts of the traffic environment, as well as a truck that occluded the lane ahead, also in clear weather condition. Subjective comfort of drivers in each condition was rated with a haptic rating lever. Results suggest that comfortable time headway following distances in highly automated driving are not constant over different speeds, but that these distances decrease with increasing speed. Reduced visibility generally led to a shift in comfortable following distances towards larger headways. These results have implications for the introduction of highly automated vehicles and their time headway adjustments, which will need to be adaptive to speed and visibility in the road environment.
Felix Wilhelm Siebert; Fares Lian Wallis. How speed and visibility influence preferred headway distances in highly automated driving. Transportation Research Part F: Traffic Psychology and Behaviour 2019, 64, 485 -494.
AMA StyleFelix Wilhelm Siebert, Fares Lian Wallis. How speed and visibility influence preferred headway distances in highly automated driving. Transportation Research Part F: Traffic Psychology and Behaviour. 2019; 64 ():485-494.
Chicago/Turabian StyleFelix Wilhelm Siebert; Fares Lian Wallis. 2019. "How speed and visibility influence preferred headway distances in highly automated driving." Transportation Research Part F: Traffic Psychology and Behaviour 64, no. : 485-494.
Developing countries are subject to increased motorization, particularly in the number of motorcycles. As helmet use is critical to the safety of motorcycle riders, the goal of this study was to identify observable patterns of helmet use, which allow a more accurate assessment of helmet use in developing countries. In a video based observation study, 124,784 motorcycle riders were observed at seven observation sites throughout Myanmar. Recorded videos were coded for helmet use, number of riders on the motorcycle, rider position, gender, and time of day. Generally, motorcycle helmet use in Myanmar was found to be low with only 51.5% percent of riders wearing a helmet. Helmet use was highest for drivers (68.1%) and decreased for every additional passenger. It was lowest for children standing on the floorboard of the motorcycle (11.3%). During the day, helmet use followed a unimodal distribution, with the highest use observed during the late morning and lowest use observed in the early morning and late afternoon. Helmet use varied significantly between observation sites, ranging from 74.8% in Mandalay to 26.9% in Pakokku. In Mandalay, female riders had a higher helmet use than male riders, and helmet use decreased drastically on a national holiday in the city. Helmet use of motorcycle riders in Myanmar follows distinct patterns. Knowledge of these patterns can be used to design more precise helmet use evaluations and guide traffic law policy and police enforcement measures. Video based observation proved to be an efficient tool to collect helmet use data.
Felix Wilhelm Siebert; Deike Albers; U Aung Naing; Paolo Perego; Chamaiparn Santikarn. Patterns of motorcycle helmet use – A naturalistic observation study in Myanmar. Accident Analysis & Prevention 2019, 124, 146 -150.
AMA StyleFelix Wilhelm Siebert, Deike Albers, U Aung Naing, Paolo Perego, Chamaiparn Santikarn. Patterns of motorcycle helmet use – A naturalistic observation study in Myanmar. Accident Analysis & Prevention. 2019; 124 ():146-150.
Chicago/Turabian StyleFelix Wilhelm Siebert; Deike Albers; U Aung Naing; Paolo Perego; Chamaiparn Santikarn. 2019. "Patterns of motorcycle helmet use – A naturalistic observation study in Myanmar." Accident Analysis & Prevention 124, no. : 146-150.
In this study the location of vehicle to vehicle distance thresholds for self-reported subjective risk and comfort was researched. Participants were presented with ascending and descending time headway sequences in a driving simulator. This so called method of limits of ascending and descending stimuli (Gouy, Diels, Reed, Stevens, & Burnett, 2012) was refined to efficiently determine individual thresholds for stable time headways with a granularity of 0.1 s. Time headway thresholds were researched for 50, 100, and 150 km/h in a city, rural, and highway setting. Furthermore, thresholds for self-driving (level 0 automation: NHTSA, 2013) were compared with thresholds for the experience of subjective risk and comfort in assisted driving, similar to adaptive cruise control (level 1 automation). Results show that preferred individual time headways vary between subjects. Within subjects however, time headway thresholds do not significantly differ for different speeds. Furthermore we found that there was no significant difference between time headways of self-driving and distance-assisted driving. The relevance of these findings for the development of adaptive cruise control systems, autonomous driving and driver behavior modelling is discussed.
Felix Wilhelm Siebert; Michael Oehl; Florian Bersch; Hans-Rüdiger Pfister. The exact determination of subjective risk and comfort thresholds in car following. Transportation Research Part F: Traffic Psychology and Behaviour 2017, 46, 1 -13.
AMA StyleFelix Wilhelm Siebert, Michael Oehl, Florian Bersch, Hans-Rüdiger Pfister. The exact determination of subjective risk and comfort thresholds in car following. Transportation Research Part F: Traffic Psychology and Behaviour. 2017; 46 ():1-13.
Chicago/Turabian StyleFelix Wilhelm Siebert; Michael Oehl; Florian Bersch; Hans-Rüdiger Pfister. 2017. "The exact determination of subjective risk and comfort thresholds in car following." Transportation Research Part F: Traffic Psychology and Behaviour 46, no. : 1-13.
There is no agreement on the relation between driving parameters and drivers’ subjective states. A linear as well as a threshold relationship for different subjective variables and driving parameters has been put forward. In this study we investigate the relationship between time headway and the ratings of risk, task difficulty, effort, and comfort. Knowledge about this interrelation may advance the development of adaptive cruise control and autonomous driving and can add to the discussion about driver behavior models. An earlier study (Lewis-Evans, De Waard, & Brookhuis, 2010) found a threshold effect for drivers’ ratings of subjective variables for time headways between 0.5 and 4.0 s at a speed of 50 km/h. This study aims to replicate the threshold effect and to expand the findings to time headways at different speeds. A new measure for criticality was added as a categorical variable, indicating the controllability of a driving situation to give indications for the appliance of time headway in adaptive cruise control systems. Participants drove 24 short routes in a driving simulator with predefined speed and time headway to a leading vehicle. Time headway was varied eightfold (0.5–4 s in 0.5 s increments) and speed was varied threefold (50, 100, 150 km/h). A threshold effect for the ratings of risk, task difficulty, effort, and comfort was found for all three different speeds. Criticality proved to be a useful variable in assessing the preferred time headway of drivers.
Felix Siebert; Michael Oehl; Hans-Rüdiger Pfister. The influence of time headway on subjective driver states in adaptive cruise control. Transportation Research Part F: Traffic Psychology and Behaviour 2014, 25, 65 -73.
AMA StyleFelix Siebert, Michael Oehl, Hans-Rüdiger Pfister. The influence of time headway on subjective driver states in adaptive cruise control. Transportation Research Part F: Traffic Psychology and Behaviour. 2014; 25 ():65-73.
Chicago/Turabian StyleFelix Siebert; Michael Oehl; Hans-Rüdiger Pfister. 2014. "The influence of time headway on subjective driver states in adaptive cruise control." Transportation Research Part F: Traffic Psychology and Behaviour 25, no. : 65-73.
Maladaptive driving is an important source of self-inflicted accidents and this driving style could include high speeds, speeding violations, and poor lateral control of the vehicle. The literature suggests that certain groups of drivers, such as novice drivers, males, highly motivated drivers, and those who frequently experience anger in traffic, tend to exhibit more maladaptive driving patterns compared to other drivers. Remarkably, no coherent framework is currently available to describe the relationships and distinct influences of these factors. We conducted two studies with the aim of creating a multivariate model that combines the aforementioned factors, describes their relationships, and predicts driving performance more precisely. The studies employed different techniques to elicit emotion and different tracks designed to explore the driving behaviors of participants in potentially anger-provoking situations. Study 1 induced emotions with short film clips. Study 2 confronted the participants with potentially anger-inducing traffic situations during the simulated drive. In both studies, participants who experienced high levels of anger drove faster and exhibited greater longitudinal and lateral acceleration. Furthermore, multiple linear regressions and path-models revealed that highly motivated male drivers displayed the same behavior independent of their emotional state. The results indicate that anger and specific risk characteristics lead to maladaptive changes in important driving parameters and that drivers with these specific risk factors are prone to experience more anger while driving, which further worsens their driving performance. Driver trainings and anger management courses will profit from these findings because they help to improve the validity of assessments of anger related driving behavior.
Ernst Roidl; Felix Wilhelm Siebert; Michael Oehl; Rainer Höger. Introducing a multivariate model for predicting driving performance: The role of driving anger and personal characteristics. Journal of Safety Research 2013, 47, 47 -56.
AMA StyleErnst Roidl, Felix Wilhelm Siebert, Michael Oehl, Rainer Höger. Introducing a multivariate model for predicting driving performance: The role of driving anger and personal characteristics. Journal of Safety Research. 2013; 47 ():47-56.
Chicago/Turabian StyleErnst Roidl; Felix Wilhelm Siebert; Michael Oehl; Rainer Höger. 2013. "Introducing a multivariate model for predicting driving performance: The role of driving anger and personal characteristics." Journal of Safety Research 47, no. : 47-56.
Due to the increasing amount of automation in vehicles the role of the driver changes from having an active part in the driving of the vehicle to a reactive monitoring task. Since there is currently no method to measure subjective comfort or discomfort we developed a 14-item scale to measure the discomfort of a driver. Research suggests that it is easier for users to sense the lack of comfort and because of this we used experienced discomfort as an indicator for the absence of comfort. The questionnaire was applied in an experimental driving simulator study and proved to have a high internal consistency (r = .91). Results suggest that this questionnaire is a useful tool for assessing discomfort in automated HMI. This first version is focused on, but not limited to, automation and advanced driver assistance systems in vehicles.
Felix Siebert; Michael Oehl; Rainer Höger; Hans-Rüdiger Pfister. Discomfort in Automated Driving – The Disco-Scale. Communications in Computer and Information Science 2013, 337 -341.
AMA StyleFelix Siebert, Michael Oehl, Rainer Höger, Hans-Rüdiger Pfister. Discomfort in Automated Driving – The Disco-Scale. Communications in Computer and Information Science. 2013; ():337-341.
Chicago/Turabian StyleFelix Siebert; Michael Oehl; Rainer Höger; Hans-Rüdiger Pfister. 2013. "Discomfort in Automated Driving – The Disco-Scale." Communications in Computer and Information Science , no. : 337-341.
As cars become increasingly computerized, automatic emotion detection and affective computing provides a promising basis for future-oriented human-computer interaction (HCI) in cars. However, we are still facing severe problems when trying to detect the users’ emotional state reliably. This experimental study investigated grip-strength as a new non-invasive method to detect emotions directly in an automobile context. A positive emotion (happiness) and a negative emotion (anger) were examined regarding their influence on grip-strength applied to the steering wheel. Results confirmed and extended preliminary findings: Drivers’ grip-strength slightly increased while driving a car when happiness was experienced and especially decreased when anger was experienced. Implications for further research as well as for praxis are outlined.
Michael Oehl; Felix W. Siebert; Tessa-Karina Tews; Rainer Höger; Hans-Rüdiger Pfister. Improving Human-Machine Interaction – A Non Invasive Approach to Detect Emotions in Car Drivers. Transactions on Petri Nets and Other Models of Concurrency XV 2011, 6763, 577 -585.
AMA StyleMichael Oehl, Felix W. Siebert, Tessa-Karina Tews, Rainer Höger, Hans-Rüdiger Pfister. Improving Human-Machine Interaction – A Non Invasive Approach to Detect Emotions in Car Drivers. Transactions on Petri Nets and Other Models of Concurrency XV. 2011; 6763 ():577-585.
Chicago/Turabian StyleMichael Oehl; Felix W. Siebert; Tessa-Karina Tews; Rainer Höger; Hans-Rüdiger Pfister. 2011. "Improving Human-Machine Interaction – A Non Invasive Approach to Detect Emotions in Car Drivers." Transactions on Petri Nets and Other Models of Concurrency XV 6763, no. : 577-585.
Emotion detection provides a promising basis for designing future-oriented human centered design of Human-Machine Interfaces. Affective Computing can facilitate human-machine communication. Such adaptive advanced driver assistance systems (ADAS) which are dependent on the emotional state of the driver can be applied in cars. In contrast to the majority of former studies that only used static recognition methods, we investigated a new dynamic approach for detecting emotions in facial expressions in an artificial setting and in a driving context. By analyzing the changes of an area defined by a number of dots that were arranged on participants’ faces, variables were extracted to classify the participants’ emotions according to the Facial Action Coding System. The results of our novel way to categorize emotions lead to a discussion on additional applications and limitations that frames an attempted approach of emotion detection in cars. Implications for further research and applications are outlined.
Tessa-Karina Tews; Michael Oehl; Felix W. Siebert; Rainer Höger; Helmut Faasch. Emotional Human-Machine Interaction: Cues from Facial Expressions. Transactions on Petri Nets and Other Models of Concurrency XV 2011, 6771, 641 -650.
AMA StyleTessa-Karina Tews, Michael Oehl, Felix W. Siebert, Rainer Höger, Helmut Faasch. Emotional Human-Machine Interaction: Cues from Facial Expressions. Transactions on Petri Nets and Other Models of Concurrency XV. 2011; 6771 ():641-650.
Chicago/Turabian StyleTessa-Karina Tews; Michael Oehl; Felix W. Siebert; Rainer Höger; Helmut Faasch. 2011. "Emotional Human-Machine Interaction: Cues from Facial Expressions." Transactions on Petri Nets and Other Models of Concurrency XV 6771, no. : 641-650.