Abstract
Validation and testing based on simulated scenarios is at the heart of the automotive industry for all vehicle development phases, since it enables perfect repeatability of the experiments and control of certain parameters. However, simulations alone can hardly capture all the complexities of the real world, thus true driving scenarios also represent an indispensable part of the process. Although invaluable, they offer very little freedom in changing the parameters, which motivates approaches for automated conversion of real-world driving scenarios to so-called logical scenarios, which can offer higher abstraction level. To be able to perform the complex process of converting real-world driving data, primarily it is necessary to be able to perform vehicle motion classification. For that purpose, this paper proposes and analyzes five different neural network models. The networks were trained and evaluated on a custom generated dataset to classify lateral vehicle behaviours in three main classes with respect to road lanes: lane keep, lane change right and lane change left. The dataset represents highway driving scenarios on a road with 7 lanes in the curvilinear coordinate system. Model training and evaluation was performed on four different subsets, each of them having a different signal-to-noise ratio. In the end, the best overall result was achieved with the network model composed of a bidirectional long-short term memory and multi-scale convolutional neural network layers.
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Diklić, V., Marković, I. (2023). Lane Change Classification with Neural Networks for Automated Conversion of Logical Scenarios. In: Petrovic, I., Menegatti, E., Marković, I. (eds) Intelligent Autonomous Systems 17. IAS 2022. Lecture Notes in Networks and Systems, vol 577. Springer, Cham. https://doi.org/10.1007/978-3-031-22216-0_52
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DOI: https://doi.org/10.1007/978-3-031-22216-0_52
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