Online Learning for Chance-Constrained Observer of Leading Heavy-Duty Vehicle Power Capability
Artikel i vetenskaplig tidskrift, 2021

This paper proposes a stochastic observer for estimating power capability of a preceding heavy-duty vehicle, using its speed measurement and road slope information. A chance-constrained optimisation problem is formulated to take into consideration the uncertainties associated with measurement error in the speed and imperfect knowledge of the road slope. An online learning approach is proposed to solve the chance-constrained optimisation problem, which learns probability distribution of the measurements along the travelled distance. The effectiveness of the proposed observer is analysed in two case studies on real road topographies and compared with an existing deterministic leading vehicle observer. The results show that the proposed leading vehicle observer is robust against uncertainties.

Leading vehicle observer

stochastic observer

chance constrained optimization

heavy-duty vehicles

online learning


Nalin Kumar Sharma

Chalmers, Elektroteknik, System- och reglerteknik, Mekatronik

Nikolce Murgovski

Chalmers, Elektroteknik, System- och reglerteknik, Mekatronik

Esteban Gelso

Volvo Group

IEEE Transactions on Intelligent Transportation Systems

1524-9050 (ISSN)

Vol. In Press


Övrig annan teknik





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