DocumentCode :
2517969
Title :
A probabilistic discriminative approach for situation recognition in traffic scenarios
Author :
Tran, Quan ; Firl, Jonas
Author_Institution :
Dept. of Meas. & Control, Karlsruhe Inst. of Technol., Karlsruhe, Germany
fYear :
2012
fDate :
3-7 June 2012
Firstpage :
147
Lastpage :
152
Abstract :
Understanding of traffic situations is an essential part of future advanced driver assistance systems (ADAS). This has to handle spatio-temporal dependencies of multiple traffic participants and uncertainties from different sources. Most existing approaches use probabilistic generative joint structures like Hidden Markov Models (HMM), which have long been used for dealing with activity recognition problems. Two significant limitations of these models are the assumption of conditional independence of observations and the availability of prior information. In this study, we present a probabilistic discriminative approach based on undirected probabilistic graphical models (Markov Networks). We combine two well-studied models: the log-linear model and the Conditional Random Field (CRF), which use dynamic programming for efficient, exact inference and their parameters can be learned via convex optimization. Since CRF conditions on entire observation sequences, we can avoid the requirement of independence between observations. Additionally, with discriminative models prior information of each activity is not necessary when performing a classification step. These two advantages of the discriminative models are very useful for our focusing problem of traffic scene understanding. We evaluate our approach with real data and show that it is able to recognize different driving maneuvers occurring at an urban intersection.
Keywords :
Markov processes; convex programming; driver information systems; dynamic programming; graph theory; inference mechanisms; learning (artificial intelligence); pattern classification; probability; random processes; road safety; road traffic; ADAS; CRF; HMM; Markov network; activity classification; activity recognition; advanced driver assistance system; conditional random field; convex optimization; driving maneuver; dynamic programming; hidden Markov model; inference; log-linear model; multiple traffic participants; observation conditional independence; observation sequence; parameter learning; prior information availability; probabilistic discriminative approach; probabilistic generative joint structure; situation recognition; spatio-temporal dependency handling; traffic scenario; traffic scene understanding; traffic situation understanding; traffic uncertainties; undirected probabilistic graphical model; urban intersection; Graphical models; Hidden Markov models; Markov random fields; Probabilistic logic; Training; Vehicle dynamics; Vehicles;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Intelligent Vehicles Symposium (IV), 2012 IEEE
Conference_Location :
Alcala de Henares
ISSN :
1931-0587
Print_ISBN :
978-1-4673-2119-8
Type :
conf
DOI :
10.1109/IVS.2012.6232279
Filename :
6232279
Link To Document :
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