Analysis of Driving Maneuvers: Is the Secret in the Distance or Time?

Sathyanarayana, Amardeep; Krishnamurthy, Nitish; Hansen, John H. L.

doi:10.4271/2014-01-0299

Cited by 5 publications

(3 citation statements)

References 8 publications

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“…Moreover, they have recently been used as simple building blocks for the synthesis of dihydropyridones [10]. Traditionally, chalcones compounds are synthesized through Claisen-Schmidt condensation between aldehydes with substituted ketones using homogeneous catalysts such as NaOH, KOH and Ba(OH) 2 [11][12][13][14][15][16][17].…”

Section: Introductionmentioning

confidence: 99%

Modified fluorapatite as highly efficient catalyst for the synthesis of chalcones via Claisen–Schmidt condensation reaction

Jioui

Dânoun

Solhy

et al. 2016

Journal of Industrial and Engineering Chemistry

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Section: Introductionmentioning

confidence: 99%

Modified fluorapatite as highly efficient catalyst for the synthesis of chalcones via Claisen–Schmidt condensation reaction

Jioui

Dânoun

Solhy

et al. 2016

Journal of Industrial and Engineering Chemistry

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“…When comparing both sliding window approaches for different velocities, then the distance-based approach outperforms the time-based one at lower velocities but loses its advantage at higher velocities. We attribute this to the fixed distance of the driving segment, for which the information value remains high even at low velocities where segments become longer in duration (see also [21]). In contrast, for FTW the segment duration remains constant, but the covered distance of a segment decreases with lower velocities, result-ing in a lower informative value, which in turn has a negative impact on the performance.…”

Section: Evaluation and Resultsmentioning

confidence: 99%

“…Sathyanarayana et al [21] propose a slightly adapted version of the FTW approach: instead of segmenting signals based on the time passed, they segment based on the distance traveled by a vehicle, therefore referring to it as fixed distance sliding window (FDW) approach. Compared to the FTW approach, the authors argue, that even at slow vehicle velocities the FDW approach yields a high information value of a segment because of the constant window distance.…”

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confidence: 99%

Driver state prediction from vehicle signals: An evaluation of segmentation approaches

Maritsch

Koch

Thomsen

et al. 2022

2022 IEEE 25th International Conference on Intelligent Transportation Systems (ITSC)

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Modern vehicles typically are equipped with assistance systems to support drivers in staying vigilant. To assess the driver state, such systems usually split characteristic vehicle signals into smaller segments which are subsequently fed into algorithms to identify irregularities in driver behavior. In this paper, we compare four different approaches for vehicle signal segmentation to predict driver impairment on a dataset from a drunk driving study (n=31). First, we evaluate two static approaches which segment vehicle signals based on fixed time and distance lengths. Intuitively, such approaches are straightforward to implement and provide segments with a specific frequency. Next, we analyze two dynamic approaches that segment vehicle signals based on pre-defined thresholds and well-defined maneuvers. Although more sophisticated to define, the more specific characteristics of driving situations can potentially improve a driver state prediction model. Finally, we train machine learning models for drunk driving detection on vehicle signals segmented by these four approaches. The maneuver-based approach detects impaired driving with a balanced accuracy of 68.73%, thereby outperforming timebased (67.20%), distance-based (65.66%), and threshold-based (61.53%) approaches in comparable settings. Therefore, our findings indicate that incorporating the driving context benefits the prediction of driver states. I. INTRODUCTIONIn recent years, with the advent of driver assistance systems, research in reliably predicting driver states has increased significantly. The aim is not only to enhance the comfort of drivers [1], but especially to improve road safety [2] by preventing accidents related to driver impairment. Advances in in-vehicle computing capacities and sensor technologies further support these endeavors. Besides camera-based technologies [3], the focus is on privacypreserving and non-intrusive approaches [4], [5]. As outlined in recent work, a large number of in-vehicle signals can be accessed via the controller area network (CAN-bus), which allows insights into the driving behavior [6], [7]. The research field around gathering driver insights from in-vehicle signals is extensive. It ranges from driver identification [4], [5], [8], over driving style recognition [9]-[11] to

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A Review of Advancements in Driver Emotion Detection: Deep Learning Approaches and Dataset Analysis

Alfaras,

Karan

2024

2024 4th International Conference on Innovative Research in Applied Science, Engineering and Technology (IRASET)

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Analysis of Driving Maneuvers: Is the Secret in the Distance or Time?

Cited by 5 publications

References 8 publications

Modified fluorapatite as highly efficient catalyst for the synthesis of chalcones via Claisen–Schmidt condensation reaction

Modified fluorapatite as highly efficient catalyst for the synthesis of chalcones via Claisen–Schmidt condensation reaction

Driver state prediction from vehicle signals: An evaluation of segmentation approaches

A Review of Advancements in Driver Emotion Detection: Deep Learning Approaches and Dataset Analysis

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