Chapter

Predicting Take-Over Times of Truck Drivers in Conditional Autonomous Driving

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Abstract

Conditional autonomous driving requires the description of sufficient time reserves for drivers in take-over situations. The definition of this time reserve has not been addressed for the truck context thus far. Through the observation of physiological measures, the possibility of estimating reaction times is considered. Driver data is collected with a remote eye-tracker and body posture camera. Empirical data from a simulator study is utilized to train and compare four machine learning algorithms and generate driver features. The estimation of take-over times is defined as a classification problem with four reaction time classes, leading to a misclassification rate of a linear support vector machine (SVM) of 38.7%. Utility of driver features for reaction time estimation are discussed.

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... Since understanding of the influencing factors on the driver's reaction is needed, better measurement 65 techniques and algorithms are required that potentially predict the right time interval for secure human 66 behavior (Lotz, et al., 2019). The second approach for the definition of adequate take-over times is a more 67 elegant, however, technically more complex due to additional sensors and algorithms necessary for driver 68 observation. ...
... This incorporates the data collection of reaction times and 258 eye-tracking data. The measurements of the body posture are not included in this publication but are 259 considered in Lotz et al. (2019). A moving-based simulator will be used to manipulate environmental factors, 260 ...
Article
The desire to enable conditional automated driving (CAD) in the near future, entails the challenge to manage drivers’ safe transitions from automation back to manual control. Several factors have been considered in recent years in the passenger car context, while the truck has largely been disregarded. For the first time take-over behavior of heavy-duty truck drivers in time critical take-overs is considered in CAD research. This study analyzes the effect of non-driving related tasks, CAD duration, take-over situations and number of take-overs on reaction times of truck drivers. Gaze behavior was tracked with a remote eye-tracker; reaction times and driver interaction during CAD drives was recorded and analyzed. Two different non-driving related tasks were presented in nine unique take-over situations, while also controlling for the duration of CAD. Contrary to assumption, no influence of non-driving related tasks or CAD duration on reaction times is found. Notably, different reaction times are recorded due to the nine unique take-over situations. Finally, it is shown that our take-over times decrease over the course of the experiment and are far lower than other published reaction times (M = 1.35 s) in the passenger car context. The findings are discussed and implications with regard to other published studies are drawn.
... The driver's reaction time in three typical collision scenarios with controlled TTC illustrated that the drivers' steering manoeuvre reaction was significantly faster than the brake reaction time with the mean of 0.86 s with comparison to 2.29 s for braking reaction (Li et al., 2019). Another study in the driving simulator for the takeover action (TOR) of truck drivers in autonomous driving (level 3) by using eye tracker from 755 TOR found braking pedal reaction time lower than 1.7 s (Lotz et al., 2019). The initial reaction braking delay of 2.8 s is being considered in the calculation of the braking distance in the Italian road design code, which decreases 1% with the speed of the driver. ...
Thesis
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... In terms of driver take-over prediction, Lotz et al. was able to predict a drivers take-over time within four discrete ranges, in seconds [9]. Gold et al. improved upon Lotz's work by modeling driver take-over time as a continuous variable through the use of a generalized non-linear regression model [10]. ...
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... regression models, to extrapolate data based on empirical findings post-hoc and explain correlations in the data (McDonald, et al., 2019;Zhang, et al., 2019). A second class of models provides online prediction based on data obtained through driver and environment monitoring (Nilsson et al., 2015;Braunagel et al., 2017;Lotz & Weissenberger, 2019). However, subjective driver interpretation is missing as input data. ...
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Verkehr auf einen Blick
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