Modeling the driver's behavior is an importance task which has shown its advantages in improving the prediction accuracy of different applications in the automotive field. A model of driver behavior could be used to increase safety, to improve the functionality of an advanced driver assistant system (ADAS), as well as to increase the driving experience and comfort for users. Most data-driven approaches build a general model that works well for the majority of drivers in the training set. Personalization, on the other hand, addresses the problem of adapting the model on the driver. A personalized model also has to be able to adjust to changing user preferences over time. In this work, we formulate the problem of personalization in automotive scenes, and propose an approach to extract and incorporate information from previous maneuver execution to improve the performance of prediction tasks in impending maneuvers. We apply our proposed adaptation method to predict the gap taken at a left-turn scenario and show that it can boost the prediction of a neural network in terms of F1 score in comparison to the baseline method by about 7% using fully connected layer, and by more than 9% when using LSTM layer.
Using Past Maneuver Executions for Personalization of a Driver Model
2018-11-01
633578 byte
Conference paper
Electronic Resource
English
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