Skip to Main content Skip to Navigation
Journal articles

Longitudinal Jerk Estimation of Driver Intentions for Advanced Driver Assistance Systems

Abstract : 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.
Document type :
Journal articles
Complete list of metadata

Cited literature [27 references]  Display  Hide  Download

https://hal.laas.fr/hal-01700165
Contributor : Luca Zaccarian Connect in order to contact the contributor
Submitted on : Saturday, February 17, 2018 - 10:51:13 PM
Last modification on : Monday, July 4, 2022 - 9:47:20 AM
Long-term archiving on: : Monday, May 7, 2018 - 10:30:14 PM

File

FINALLATEX.pdf
Files produced by the author(s)

Identifiers

Citation

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, Institute of Electrical and Electronics Engineers, 2017, 22 (4), pp.1531 - 1541. ⟨10.1109/TMECH.2017.2716838⟩. ⟨hal-01700165⟩

Share

Metrics

Record views

38

Files downloads

0