2018
DOI: 10.1109/jsen.2018.2856112
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A Genetic Programming Approach for Driving Score Calculation in the Context of Intelligent Transportation Systems

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Cited by 20 publications
(16 citation statements)
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“…They reveal similar data reduction ratios, implying that drivers exhibit homogeneous behavior from the viewpoint of data reduction. However, in terms of other viewpoints, such as the number of sudden brakes or abrupt steering-changes, drivers exhibit different patterns, which was also observed in [35]. We also find that the distinguishable patterns between two states (relatively constant in a drowsy state vs. continuously fluctuating in a non-drowsy state) observed in Figure 7 are stable over time, meaning that they can be used as pertinent indicators for exploring driver drowsiness.…”
Section: Correlation Analysis Resultssupporting
confidence: 57%
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“…They reveal similar data reduction ratios, implying that drivers exhibit homogeneous behavior from the viewpoint of data reduction. However, in terms of other viewpoints, such as the number of sudden brakes or abrupt steering-changes, drivers exhibit different patterns, which was also observed in [35]. We also find that the distinguishable patterns between two states (relatively constant in a drowsy state vs. continuously fluctuating in a non-drowsy state) observed in Figure 7 are stable over time, meaning that they can be used as pertinent indicators for exploring driver drowsiness.…”
Section: Correlation Analysis Resultssupporting
confidence: 57%
“…This paper demonstrates one specific objective; the interface is linked with a function that determines whether a driver has his/her eyes open or closed based on EAR for drowsiness detection. Our scheme can also be used for other objectives, such as over-speed recognition, aggressive turn detection and good driver selection, wherein the generic interface is linked with a function of speed estimation [29], steering wheel angle calculation [32] and driving score assessment [35], respectively.…”
Section: Discussionmentioning
confidence: 99%
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“…For instance, in the work of [5] the authors attempt to score driving trips using a Bayesian classifier to differentiate between risky and safe maneuvers. In [7], the authors compared a Fuzzy Inference System [1], a Safety Index [13], a Bayesian regressor [5], a Multi-layer perceptron, a Random Forrest, a Support Vector regressor and a GP approach to learn how individuals score 200 virtual road trips, where each trip was represented by a feature vector containing the frequency of risky maneuvers. Results showed that the GP strategy was superior than competitors, even in some cases with statistical significance.…”
Section: Related Workmentioning
confidence: 99%
“…The problem of calculating a driving score based on the performance of the driver could be seen as a computational learning task, where given a feature vector that contains the frequency of risky maneuvers the goal is to assign a score to represent the driver's performance in a risk-safety scale (this problem could also be seen as a machine learning problem for human-rating). Recently, in [7] the authors presented the evaluation of seven different Machine Learning (ML) approaches to learn how individuals assigned a driving score. From this comparison a clear winner emerged, this being the Genetic Programming (GP) approach.…”
Section: Introductionmentioning
confidence: 99%