Abstract
Inferring driver maneuvers is a fundamental issue in Advanced Driver Assistance Systems (ADAS), which can significantly increase security and reduce the risk of road accidents. This is not an easy task due to a number of factors such as driver distraction, unpredictable events on the road, and irregularity of the maneuvers. In this complex setting, Machine Learning techniques can play a fundamental and leading role to improve driving security. In this paper, we present preliminary results obtained within the Development Platform for Safe and Efficient Drive (DESERVE) European project. We trained a number of classifiers over a preliminary dataset to infer driver maneuvers of Lane Keeping and Lane Change. These preliminary results are very satisfactory and motivate us to proceed with the application of Machine Learning techniques over the whole dataset.
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Acknowledgements
This research was developed under the European Research Project DESERVE, Development Platform for Safe and Efficient Drive, Project reference: 295364, Funded under: FP7-JTI. The authors are grateful to Fabio Tango, Sandro Cumani and Kenneth Morton for the support provided during the project.
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Baldi, M.M., Perboli, G., Tadei, R. (2016). Driver Maneuvers Inference Through Machine Learning. In: Pardalos, P., Conca, P., Giuffrida, G., Nicosia, G. (eds) Machine Learning, Optimization, and Big Data. MOD 2016. Lecture Notes in Computer Science(), vol 10122. Springer, Cham. https://doi.org/10.1007/978-3-319-51469-7_15
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