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Article Dans Une Revue IEEE/ASME Transactions on Mechatronics Année : 2017

Longitudinal Jerk Estimation of Driver Intentions for Advanced Driver Assistance Systems

Résumé

This work aims at estimating the longitudinal jerk of the vehicle as it is acted by a human driver, in the context of preventive safety. A reliable estimate is needed to infer the current driver intention in an advanced driving assistance system developed by the authors. The derived intention-oriented model for the longitudinal dynamics is embedded into an enhanced Kalman filter that provides the user with a knob to trade off between responsiveness of the estimate and noise rejection. The scheme is fit for on-line usage, relies on signals commonly available on the CAN bus of modern vehicles, and requires a very limited number of parameters. Its effectiveness is validated on experimental data, and compared with alternative approaches.
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Dates et versions

hal-01700165 , version 1 (17-02-2018)

Identifiants

Citer

Andrea Bisoffi, Francesco Biral, Mauro da Lio, Luca Zaccarian. Longitudinal Jerk Estimation of Driver Intentions for Advanced Driver Assistance Systems. IEEE/ASME Transactions on Mechatronics, 2017, 22 (4), pp.1531 - 1541. ⟨10.1109/TMECH.2017.2716838⟩. ⟨hal-01700165⟩
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