Driver state prediction from vehicle signals: An evaluation of segmentation approaches
Type
conference paper
Date Issued
2022-11-01
Author(s)
Maritsch, Martin
;
;
Thomsen, Hauke
;
Kühl, Niklas
;
Pfäffli, Matthias
;
Weinmann, Wolfgang
;
Abstract (De)
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 time-based (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.
Language
English
Keywords
Roads
Machine learning
Predictive models
Prediction algorithms
Behavioral sciences
Safety
Reliability
HSG Classification
contribution to scientific community
Publisher
IEEE
Start page
1106
End page
1113
Pages
8
Event Title
2022 IEEE 25th International Conference on Intelligent Transportation Systems (ITSC)
Event Location
Macau, China
Event Date
8.-12.Oktober 2022
Official URL
Subject(s)
Eprints ID
268168
File(s)![Thumbnail Image]()
Name
Driver_state_prediction_from_vehicle_signals_An_evaluation_of_segmentation_approaches.pdf
Size
425.87 KB
Format
Adobe PDF
Checksum (MD5)
6765ad9d427ecabf6ad5029646ea6cc3