EKF and particle filter track-to-track fusion: a quantitative comparison from radar/lidar obstacle tracks

In road environment, road obstacle tracking is able to extract important information for driving safety. Indeed, kinematic characteristics estimation (relative position, relative speed, ...) provides a clearer road scene comprehension. This estimate is one of the important parameters of driver assistance systems. However, only one sensor generally does not allow to detect quickly (all the potentially dangerous obstacles) in all the directions, under all the atmospheric conditions. Moreover, the degree of obstacle recognition is different according to the sensor used. Multiplication of sensors makes it possible to face these various problems. Amalgamated information will represent entities in further details and with less uncertainty than with only one source. A system of higher level has been thus developed in order to have a robust management of all tracks and measurements coming from various sensors. This system, applied to radar and lidar measurements combination, gives important obstacles characteristics present in the front bumper of our experimental vehicle (VELAC: LASMEA´s experimental vehicle for driving assistance). This state estimate is based on the use of various Bayesian methods (Extended Kalman Filter and Particle Filter). Here we will use the fusion of two-obstacle tracking delivered by two independent systems: track-to-track fusion. These two systems propose estimates characterizing obstacles positions and relative speeds. Fusion estimation is based on the use of extended Kalman filter (EKF) or particle filters. A comparison of these two methods is presented in this article.