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Communication Dans Un Congrès Année : 2015

Longitudinal Jerk Estimation for Identification of Driver Intention

Résumé

We address the problem of estimating online the longitudinal jerk desired by a human driver piloting a car. This estimation is relevant in the context of suitable identification of driver intentions within modern Advanced Driver Assistance Systems (ADAS) such as the co-driver scheme proposed by some of the authors. The proposed architecture is based on suitably combining a Kalman filter with a scaling technique peculiar of the context of "high-gain" observers. The scaling is appealing because it allows for an easy tuning of the trade-off between phase lag and sensitivity to noise of the resulting estimate. Additionally, we show that using engine-related experimental measurements available in the CAN bus, it is possible to provide a more reliable estimate of the driver-intended jerk, especially in the presence of gear changes. The proposed scheme shows very desirable results on experimental data from a track test, also when compared to a brute force approach based on a mere kinematic model.
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Dates et versions

hal-01851196 , version 1 (29-07-2018)

Identifiants

Citer

Andrea Bisoffi, Francesco Biral, Mauro Da Lio, Luca Zaccarian. Longitudinal Jerk Estimation for Identification of Driver Intention. IEEE 18th International Conference on Intelligent Transportation Systems - (ITSC 2015), Sep 2015, Gran Canaria, Spain. 7p., ⟨10.1109/ITSC.2015.301⟩. ⟨hal-01851196⟩
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