Abstract
This article proposes a novel longitudinal vehicle speed estimator for snowy roads in extreme conditions (four-wheel slip) based on low-cost wheel speed encoders and a longitudinal acceleration sensor. The tire rotation factor, η, is introduced to reduce the deviation between the rotation tire radius and the manufacturer’s marked tire radius. The Local Vehicle Speed Estimator is defined to eliminate longitudinal vehicle speed estimation error. It improves the tire slip accuracy of four-wheel slip, even with a high slip rate. The final vehicle speed is estimated using two fuzzy control strategies that use vehicle speed estimates from speed encoders and a longitudinal acceleration sensor. Experimental and simulation results confirm the algorithm’s validity for estimating longitudinal vehicle speed for four-wheel slip in snowy road conditions.
Highlights
Smart, advanced driver assistance systems have drawn considerable research attention because these active systems are essential for vehicle state estimation and control [1,2,3]
Vehicle speed is a key variable in wheel slip and the foundation of vehicle state estimation [4], which is used in lane change assist (LCA), emergency stop assist (ESA) and active cruise control (ACC) technologies [5]
To take the number and slip level of wheels into account based on input variables, fuzzy control logic is used to calculate the confidence level of the vehicle longitudinal speed estimated from the minimum wheel speed
Summary
Smart, advanced driver assistance systems have drawn considerable research attention because these active systems are essential for vehicle state estimation and control [1,2,3]. This paper aims to develop a novel longitudinal speed estimator for four-wheel drive vehicles when more than two wheels have high slip rates on snowy roads. In the vehicle estimate, speed logic is based on wheel speed encoders and the minimum wheel speed is selected as a basic reference; the fuzzy logic algorithm is used to consider the effect of each slip condition. Ininwwhhicihch, ,1 1≤≤i,ij, ≤j ≤2.2 TToommakakeeththeefifrisrts-tl-alyayerercacalcluculalatitoionnstsrtarateteggyyssmmoooothth, ,sslilpiprraatetessaarreeddivividideeddininttoololoww, , mmididaannddlalargrgeeslsilpipininththeefufuzzzyzycoconntrtorlorl urulel.e. TThheesstrtraatteeggyyiiss ttoo eessttiimmaattee vveehhiicclleessppeeeeddoonnnnoot tonolnylya ahihgihghfrifcrtiicotnioanspahspalhtarlotardoabdut baultsoalsaosapespcieaclia(sln(sonwoyw) yr)oraoda,dw, whehreerethteheslsilpipfrfricictitoionnbbeettwweeeenn tthhee ttiirree aanndd tthheeroroaaddisis aapppproroxximimaatetelyly00.1.1aannddththeemmaaxximimuummfrfircitcitoionnisisapappproroxixmimataetleyly0.02..2.TThheelolowwslsilpipcactaetgegooryry oonnlylycoconnsisdideersrssnsnoowwyyroroaaddddrirvivininggcoconndditiitoionnss; ;ththeeslsilpipfrfircitcitoionnbbeetwtweeeennththeetitriereananddththee snsnoowwsusurfrafacceeisisbbetewtweeeenn00.1.1anandd0.01.7157.5.MMididslsilpipcoconnsisdideersrsaaspsphhaaltltroroaaddaannddsnsnoowwroraodad ddrirviviningg, ,ininwwhhicihchththeefrfircitcitoionncocoefefiffciiceinent tisisbbetewtweeenen0.01.1anandd0.02.52.5.TThheelalragrgeeslsilpipcocnondditiitoionn coconnsisdideersrsfrfircitcitoionncocoefefiffciiceinentstsababovove e0.02.2[2[12]1](F(iFgiugurere2)2.). To take the number and slip level of wheels into account based on input variables, fuzzy control logic is used to calculate the confidence level of the vehicle longitudinal speed estimated from the minimum wheel speed.
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