A data set of real world driving to assess driver workload

Schneegaß, Stefan; Pfleging, Bastian; Broy, Nora; Heinrich, Frederik; Schmidt, Albrecht

doi:10.1145/2516540.2516561

Cited by 102 publications

(67 citation statements)

References 19 publications

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“…Literature on measuring physiological signals in the car can be divided into simulator studies [13,18,19,24,36] and field studies [14,15,18,28,30,33,40]. The major advantage of simulator studies is their relative ease of controlling experimental variables and conditions.…”

Section: Measuring Physiological Signals In the Carmentioning

confidence: 99%

“…Schneegaß, et al [33] present a field study with ten participants in which they collected EKG, skin conductance, and skin temperature data while participants drove in road environments presumed to induce differing levels of demand. This preliminary report only considered the skin conductance and skin temperature data, and found skin conductance for the group varied significantly across the road types and to be the more sensitive measure.…”

Section: Measuring Physiological Signals In the Carmentioning

confidence: 99%

See 1 more Smart Citation

Classifying driver workload using physiological and driving performance data

Solovey

Zec

Perez

et al. 2014

Proceedings of the SIGCHI Conference on Human Factors in Computing Systems

160

127

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Understanding the driver's cognitive load is important for evaluating in-vehicle user interfaces. This paper describes experiments to assess machine learning classification algorithms on their ability to automatically identify elevated cognitive workload levels in drivers, leading towards the development of robust tools for automobile user interface evaluation. We look at using both driver performance as well as physiological data. These measures can be collected in real-time and do not interfere with the primary task of driving the vehicle. We report classification accuracies of up to 90% for detecting elevated levels of cognitive load, and show that the inclusion of physiological data leads to higher classification accuracy than vehicle sensor data evaluated alone. Finally, we show results suggesting that models can be built to classify cognitive load across individuals, instead of building individual models for each person. By collecting data from drivers in two large field studies on the highway (20 drivers and 99 drivers), this work extends prior work and demonstrates feasibility and potential of such measures for HCI research in vehicles.

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Section: Measuring Physiological Signals In the Carmentioning

confidence: 99%

Section: Measuring Physiological Signals In the Carmentioning

confidence: 99%

Classifying driver workload using physiological and driving performance data

Solovey

Zec

Perez

et al. 2014

Proceedings of the SIGCHI Conference on Human Factors in Computing Systems

160

127

View full text Add to dashboard Cite

show abstract

“…The limited number of studies that are conducted in the natural environment followed scripted routes under supervision for a limited duration [14, 38, 41]. For example, in [14], four drivers wore physiological sensors during highway and urban driving episodes and self-report showed that urban driving was more stressful than highway, which, in turn, was more stressful than when parked in a garage.…”

Section: Related Workmentioning

confidence: 99%

Estimating Drivers' Stress from GPS Traces

Vhaduri

Ali

Sharmin

et al. 2014

Proceedings of the 6th International Conference on Automotive User Interfaces and Interactive Vehicular Applications

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Driving is known to be a daily stressor. Measurement of driver’s stress in real-time can enable better stress management by increasing self-awareness. Recent advances in sensing technology has made it feasible to continuously assess driver’s stress in real-time, but it requires equipping the driver with these sensors and/or instrumenting the car. In this paper, we present “GStress”, a model to estimate driver’s stress using only smartphone GPS traces. The GStress model is developed and evaluated from data collected in a mobile health user study where 10 participants wore physiological sensors for 7 days ( for an average of 10.45 hours/day) in their natural environment. Each participant engaged in 10 or more driving episodes, resulting in a total of 37 hours of driving data. We find that major driving events such as stops, turns, and braking increase stress of the driver. We quantify their impact on stress and thus construct our GStress model by training a Generalized Linear Mixed Model (GLMM) on our data. We evaluate the applicability of GStress in predicting stress from GPS traces, and obtain a correlation of 0.72. By obviating any burden on the driver or the car, we believe, GStress can make driver’s stress assessment ubiquitous.

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“…For instance works such as [22] and [13], use multiple sensors to provide intelligent information on the driver's physiological signals, which can include eye activity measures, the inclination of the driver's face, heart rate monitoring, skin electric potential, and electroencephalographic (EEG) activities. In [2] is proposed a novel and non-intrusive driver behaviour detection system using a context-aware system combined with in-vehicle sensors collecting information regarding to vehicle's speed, acceleration, the direction of driver's eyes, the position in lane and the level of alcohol in the driver's blood.…”

Section: Related Workmentioning

confidence: 99%

Comparison of different driving style analysis approaches based on trip segmentation over GPS information

Brambilla

Mascetti

Mauri

2017

2017 IEEE International Conference on Big Data (Big Data)

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Over one billion cars interact with each other on the road every day. Each driver has his own driving style, which could impact safety, fuel economy and road congestion. Knowledge about the driving style of the driver could be used to encourage "better" driving behaviour through immediate feedback while driving, or by scaling auto insurance rates based on the aggressiveness of the driving style. In this work we report on our study of driving behaviour profiling based on unsupervised data mining methods. The main goal is to detect the different driving behaviours, and thus to cluster drivers with similar behaviour. This paves the way to new business models related to the driving sector, such as Pay-How-You-Drive insurance policies and car rentals. Driver behavioral characteristics are studied by collecting information from GPS sensors on the cars and by applying three different analysis approaches (DP-means, Hidden Markov Models, and Behavioural Topic Extraction) to the contextual scene detection problems on car trips, in order to detect different behaviour along each trip. Subsequently, drivers are clustered in similar profiles based on that and the results are compared with a human-defined groundtruth on drivers classification. The proposed framework is tested on a real dataset containing sampled car signals. While the different approaches show relevant differences in trip segment classification, the coherence of the final driver clustering results is surprisingly high.

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A data set of real world driving to assess driver workload

Cited by 102 publications

References 19 publications

Classifying driver workload using physiological and driving performance data

Classifying driver workload using physiological and driving performance data

Estimating Drivers' Stress from GPS Traces

Comparison of different driving style analysis approaches based on trip segmentation over GPS information

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