Driving is a complex activity which requires constant care and attention. Intelligent Advance Driver Assistance Systems (ADAS) can improve vehicle control performance and, thus, drivers and passengers safety. In particular, identification and prediction of driving intention can provide prompt information to drivers and vehicles in their vicinity that are fundamental for avoiding collisions. In this paper, we propose a lane change prediction model based on machine learning able to distinguish between left and right lane changes, a distinction that becomes particularly important when driving in a highway. Models have been trained and validated using a real dataset gathered online by using a high-tech demonstrator vehicle provided by Centro Ricerche Fiat (i.e., Fiat Research Center). Data, which refer to real driving conditions on a highway, have been collected by monitoring different drivers showing different behaviors. We address the problem of unbalanced data, typical of real data sets, and propose two prediction models based on Support Vector Machines and Random Forests. The results of our computational experiments show the validity of the approach with respect to state of the art models, both in terms of prediction accuracy and prediction time.
Baldi, M., Cilli, N., Messina, E., Tango, F. (2021). A Decision Model for Enhancing Driving Security. In Optimization and Decision Science ODS, Virtual Conference, November 19, 2020 (pp.143-152). Springer Nature [10.1007/978-3-030-86841-3_12].
A Decision Model for Enhancing Driving Security
Messina E.;
2021
Abstract
Driving is a complex activity which requires constant care and attention. Intelligent Advance Driver Assistance Systems (ADAS) can improve vehicle control performance and, thus, drivers and passengers safety. In particular, identification and prediction of driving intention can provide prompt information to drivers and vehicles in their vicinity that are fundamental for avoiding collisions. In this paper, we propose a lane change prediction model based on machine learning able to distinguish between left and right lane changes, a distinction that becomes particularly important when driving in a highway. Models have been trained and validated using a real dataset gathered online by using a high-tech demonstrator vehicle provided by Centro Ricerche Fiat (i.e., Fiat Research Center). Data, which refer to real driving conditions on a highway, have been collected by monitoring different drivers showing different behaviors. We address the problem of unbalanced data, typical of real data sets, and propose two prediction models based on Support Vector Machines and Random Forests. The results of our computational experiments show the validity of the approach with respect to state of the art models, both in terms of prediction accuracy and prediction time.
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