Driving intention recognition and trajectory prediction of moving vehicles are two important requirements of future advanced driver assistance systems (ADAS) for urban intersections. In this paper, we present a consistent framework for solving these two problems. The key idea is to model the spatio-temporal dependencies of traffic situations with a two-dimensional Gaussian process regression. With this representation the driving intention can be recognized by evaluating the data likelihood for each individual regression model. For the trajectory prediction purpose, we transform these regression models into the corresponding dynamical models and combine them with Unscented Kalman Filters (UKF) to overcome the non-linear issue. We evaluate our framework with data collected from real traffic scenarios and show that our approach can be used for recognition of different driving intentions and for long-term trajectory prediction of traffic situations occurring at urban intersections.
Modelling of traffic situations at urban intersections with probabilistic non-parametric regression
2013-06-01
1307256 byte
Conference paper
Electronic Resource
English
MODELLING OF TRAFFIC SITUATIONS AT URBAN INTERSECTIONS WITH PROBABILISTIC NON-PARAMETRIC REGRESSION
British Library Conference Proceedings | 2013
|Traffic performance at urban street intersections
Engineering Index Backfile | 1947
|Modelling of Traffic Hazards at Urban Intersections Using Potential Collision Areas
Springer Verlag | 2017
|Modelling of traffic noise in the vicinity of urban road intersections
Elsevier | 2022
|