Authors:
Nadhir Mansour Ben Lakhal
1
;
Othman Nasri
2
;
Lounis Adouane
3
and
Jaleleddine Ben Hadj Slama
2
Affiliations:
1
Institut Pascal, UCA/SIGMA - UMR CNRS 6602, Clermont Auvergne University, France, LATIS Lab, National Engineering School of Sousse (ENISo), University of Sousse, BP 264 Sousse Erriadh 1023, Tunisia
;
2
LATIS Lab, National Engineering School of Sousse (ENISo), University of Sousse, BP 264 Sousse Erriadh 1023, Tunisia
;
3
Heudiasyc UMR CNRS/UTC 7253, Université de Technologie de Compiègne, 60203 Compiègne, France
Keyword(s):
Intelligent Vehicles, Risk Management, Interval-based Modeling, Correlation Analysis, Interval Polynomial, Second-order Time to Collision.
Abstract:
Developing high fidelity models to compute the Time-To-Collision (TTC) between vehicles is addressed in this work. A TTC interval value is over-approximated while considering several uncertainties via interval analysis. Furthermore, to decrease modeling inaccuracy, a novel second-order set-membership TTC formalization is introduced by solving a polynomial equation with interval coefficients. This latter is derived from vehicles’ motion equations. Hence, an approach based on correlation analysis is exploited to improve the uncertainty evaluation. The simulation results applied on an adaptive cruise control system of both high/low-order TTC formalizations prove that the low-order model inaccuracy is compensated. Thanks to interval analysis and correlation characterization, a great balance between modeling accuracy and simplicity is reached.