TY - JOUR
T1 - Early and accurate recognition of highway traffic maneuvers considering real world application
T2 - a novel framework using Bayesian networks
AU - Weidl, Galia
AU - Madsen, Anders Læsø
AU - Wang, Stevens
AU - Dietmar, Kasper
AU - Karlsen, Martin
PY - 2018/9/1
Y1 - 2018/9/1
N2 - This paper presents a novel application of artificial cognitive systems to traffic scene understanding and early recognition of highway maneuvers. This is achieved by use of Bayesian networks for knowledge representation, to mimic the human reasoning on situation analysis and to manage inherited uncertainties in the automotive domain, that requires efficient and effective analysis of high volume and frequency data streams. The maneuver recognition uses features, analyzing the observed vehicles behavior and available free space on the target lane.
AB - This paper presents a novel application of artificial cognitive systems to traffic scene understanding and early recognition of highway maneuvers. This is achieved by use of Bayesian networks for knowledge representation, to mimic the human reasoning on situation analysis and to manage inherited uncertainties in the automotive domain, that requires efficient and effective analysis of high volume and frequency data streams. The maneuver recognition uses features, analyzing the observed vehicles behavior and available free space on the target lane.
UR - http://www.scopus.com/inward/record.url?scp=85048494494&partnerID=8YFLogxK
U2 - 10.1109/MITS.2018.2842049
DO - 10.1109/MITS.2018.2842049
M3 - Journal article
SN - 1939-1390
VL - 10
SP - 146
EP - 158
JO - IEEE Intelligent Transportation Systems Magazine
JF - IEEE Intelligent Transportation Systems Magazine
IS - 3
M1 - 8375109
ER -