The Classifier Algorithm for Recognition of Basic Driving Scenarios

Doniec, Rafał; Sieciński, Szymon; Piaseczna, Natalia; Mocny‐Pachońska, Katarzyna; Lang, Marta; Szymczyk, J.

doi:10.1007/978-3-030-49666-1_28

Cited by 7 publications

(13 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The performance of 1D CNN deep learning model is at least 14%, better than BFS approach with soft assignment to specific configurations for the same data set. The results obtained for both data sets (training and validation sets) emphasize two points: the superiority of automatically learned functions over manually created ones used in [17], and the stability of 1D CNN deep learning architectures.…”

Section: Discussionmentioning

confidence: 72%

“…The average time of completing all the tasks during the experiment was 75 min. Experienced drivers generally completed the route faster than learner drivers, regardless of road conditions [17].…”

Section: Experiments Setupmentioning

confidence: 89%

“…The recordings were divided into four scenarios (categories): parking, driving through a roundabout, driving in city traffic and driving through an intersection chosen based on the classification accuracy reported in [17]. By considering only EOG signals, we can distinguish specific patterns associated with analyzed activities, regardless of style of driving, driving dynamics and experience visible in acceleration and gyration.…”

Section: Data Preprocessingmentioning

confidence: 99%

“…To the best of our knowledge, the problem of extracting and selecting appropriate features from electrooculograms in recognition of drivers' behavior has not been thoroughly investigated due to the cumbersome placement of electrodes and the breadth of the topic, which indicates a clear need for further research. The studies conducted by Niwa et al [9] and Doniec et al [17] are the only known study on drivers' behavior which used JINS MEME smart glasses. Another recent study on recognizing the gaze on the left turn was conducted by Stapel et al [18].…”

Section: Introductionmentioning

confidence: 99%

“…Because the average accuracy of the classifier based on k-means clustering and BFS reported in [17] was 85% when analyzing four activities related to driving (driving on the motorway, parking, urban traffic, traffic in the neighborhood), we attempted to improve the accuracy by changing the approach to classification.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Recognition of Drivers’ Activity Based on 1D Convolutional Neural Network

et al. 2020

Self Cite

View full text Add to dashboard Cite

Background and objective: Driving a car is a complex activity which involves movements of the whole body. Many studies on drivers’ behavior are conducted to improve road traffic safety. Such studies involve the registration and processing of multiple signals, such as electroencephalography (EEG), electrooculography (EOG) and the images of the driver’s face. In our research, we attempt to develop a classifier of scenarios related to learning to drive based on the data obtained in real road traffic conditions via smart glasses. In our approach, we try to minimize the number of signals which can be used to recognize the activities performed while driving a car. Material and methods: We attempt to evaluate the drivers’ activities using both electrooculography (EOG) and a deep learning approach. To acquire data we used JINS MEME smart glasses furnished with 3-point EOG electrodes, 3-axial accelerometer and 3-axial gyroscope. Sensor data were acquired on 20 drivers (ten experienced and ten learner drivers) on the same 28.7 km route under real road conditions in southern Poland. The drivers performed several tasks while wearing the smart glasses and the tasks were linked to the signal during the drive. For the recognition of four activities (parking, driving through a roundabout, city traffic and driving through an intersection), we used one-dimensional convolutional neural network (1D CNN). Results: The maximum accuracy was 95.6% on validation set and 99.8% on training set. The results prove that the model based on 1D CNN can classify the actions performed by drivers accurately. Conclusions: We have proved the feasibility of recognizing drivers’ activity based solely on EOG data, regardless of the driving experience and style. Our findings may be useful in the objective assessment of driving skills and thus, improving driving safety.

show abstract

Section: Discussionmentioning

confidence: 72%

Section: Experiments Setupmentioning

confidence: 89%

Section: Data Preprocessingmentioning

confidence: 99%