Abstract

International audience; 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.


Original document

The different versions of the original document can be found in:

http://www.itsc2015.org
http://dx.doi.org/10.1109/itsc.2015.301
https://hal.laas.fr/hal-01851196,
http://dx.doi.org/10.1109/ITSC.2015.301,
https://dl.acm.org/citation.cfm?id=2865529,
https://ieeexplore.ieee.org/document/7313393,
https://academic.microsoft.com/#/detail/1929621241
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Published on 01/01/2015

Volume 2015, 2015
DOI: 10.1109/itsc.2015.301
Licence: CC BY-NC-SA license

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