Novelty Based Driver Identification on RR Intervals from ECG Data

Heidecker, Florian; Gruhl, Christian; Sick, Bernhard

doi:10.1007/978-3-030-68799-1_29

Cited by 1 publication

(2 citation statements)

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“…The real time needed for identification is 3 min of active driving, which a very important point that every work should mention; even so, the performance of this job has a significant improvement with only three features. In the case of Heidecker et al [ 35 ], they consider a smaller number of features, and this lasts longer but has much higher performance. However, it uses an Encephalogram (EEC) for data acquisition, which implies an invasive method that could even hinder driving and the user’s experience.…”

Section: Discussionmentioning

confidence: 99%

“…The potential benefits of incorporating Driver2vec for numerous downstream applications are thus strongly suggested. Meanwhile, Heidecker et al [ 35 ] presented an approach to driver identification in a simulated environment that is useful for many automotive applications. The method is based on the driver’s physiological state (electrocardiogram data).…”

Section: Introductionmentioning

confidence: 99%

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Driver Identification Using Statistical Features of Motor Activity and Genetic Algorithms

Espino-Salinas

Luna-García

Celaya-Padilla

et al. 2023

Sensors

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Driver identification refers to the process whose primary purpose is identifying the person behind the steering wheel using collected information about the driver him/herself. The constant monitoring of drivers through sensors generates great benefits in advanced driver assistance systems (ADAS), to learn more about the behavior of road users. Currently, there are many research works that address the subject in search of creating intelligent models that help to identify vehicle users in an efficient and objective way. However, the different methodologies proposed to create these models are based on data generated from sensors that include different vehicle brands on routes established in real environments, which, although they provide very important information for different purposes, in the case of driver identification, there may be a certain degree of bias due to the different situations in which the route environment may change. The proposed method seeks to intelligently and objectively select the most outstanding statistical features from motor activity generated in the main elements of the vehicle with genetic algorithms for driver identification, this process being newer than those established by the state-of-the-art. The results obtained from the proposal were an accuracy of 90.74% to identify two drivers and 62% for four, using a Random Forest Classifier (RFC). With this, it can be concluded that a comprehensive selection of features can greatly optimize the identification of drivers.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%