A probabilistic discriminative approach for situation recognition in traffic scenarios

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.