Passive and Active Learning of Driver Behavior from Electric Vehicles
Paper i proceeding, 2022

Modeling driver behavior provides several advantages in the automotive industry, including prediction of electric vehicle energy consumption. Studies have shown that aggressive driving can consume up to 30% more energy than moderate driving, in certain driving scenarios. Machine learning methods are widely used for driver behavior classification, which, however, may yield some challenges such as sequence modeling on long time windows and lack of labeled data due to expensive annotation. To address the first challenge, passive learning of driver behavior, we investigate non-recurrent architectures such as self-attention models and convolutional neural networks with joint recurrence plots (JRP), and compare them with recurrent models. We find that self-attention models yield good performance, while JRP does not exhibit any significant improvement. However, with the window lengths of 5 and 10 seconds used in our study, none of the non-recurrent models outperform the recurrent models. To address the second challenge, we investigate several active learning methods with different informativeness measures. We evaluate uncertainty sampling, as well as more advanced methods, such as query by committee and active deep dropout. Our experiments demonstrate that some active sampling techniques can outperform random sampling, and therefore decrease the effort needed for annotation.


Federica Comuni

Göteborgs universitet

Christopher Mészáros

Chalmers, Data- och informationsteknik

Niklas Åkerblom

Volvo Cars

Chalmers, Data- och informationsteknik, Data Science och AI

Morteza Haghir Chehreghani

Chalmers, Data- och informationsteknik, Data Science och AI

IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC

Vol. 2022-October 929-936
978-166546880-0 (ISBN)

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

EENE: Energieffektiv Navigering för Elfordon

FFI - Fordonsstrategisk forskning och innovation (2018-01937), 2019-01-01 -- 2022-12-31.


Annan data- och informationsvetenskap

Datavetenskap (datalogi)


Informations- och kommunikationsteknik




Mer information

Senast uppdaterat