Abstract:
Obstacles detection is used nowdays for a number of road safety applications, increasing the drivers awareness in potential dangerous situations. A reliable and robust ob...Show MoreMetadata
Abstract:
Obstacles detection is used nowdays for a number of road safety applications, increasing the drivers awareness in potential dangerous situations. A reliable and robust obstacles detection continues to be largely investigated and still remains an open challenge, especially for difficult scenarios and in general cases, with loosened constraints and multiple simultaneous use-cases. This work presents an obstacles detection, tracking and fusion algorithm which allows to reconstruct the environment surrounding the vehicle. While the techniques used for the detection are well-known in literature, the improvements introduced by this paper regard the data association and tracking approach of heterogeneous sensors observations. An innovative multi-dimensional structure based on association costs originating from a classifier provides an optimal solution to the association problem with respect to the total association cost. An Unscented Kalman Filter (UKF) managing a variable number of observations, arbitrarily composable, allows to correctly address the combined tracking and fusion challenge. The results, obtained on a public benchmark, show improvements with respect to state of the art systems.
Published in: 2016 IEEE Intelligent Vehicles Symposium (IV)
Date of Conference: 19-22 June 2016
Date Added to IEEE Xplore: 08 August 2016
ISBN Information: