Multisensor Vehicle Tracking with the Probability Hypothesis Density Filter

Author

Maehlisch, Mirko ; Schweiger, Roland ; Ritter, Werner ; Dietmayer, Klaus

Author_Institution

Dept. of Meas., Control & Microtechnol., Ulm Univ.

fYear

2006

fDate

10-13 July 2006

Firstpage

1

Lastpage

8

Abstract

In this contribution we apply the probability hypothesis density (PHD) filter algorithm for joint tracking of an unknown varying number of targets to automotive environment sensing systems. We use data from a vision and a lidar sensor as well as the vehicle ESP system. After deriving a method to parametrise the algorithm systematically from detection performance statistics we proof the applicability of the method for automotive tracking based on real sensor data

Keywords

probability; road vehicles; sensor fusion; target tracking; tracking filters; PHD filter algorithm; automotive environment sensing system; detection performance statistics; lidar sensor; multisensor vehicle tracking; probability hypothesis density; vehicle ESP system; Automotive engineering; Decision making; Filters; Radar tracking; Sensor fusion; Sensor phenomena and characterization; Sensor systems; Target tracking; Vehicles; Velocity measurement; Future Driver Assistance Systems; Joint Target Tracking; Probability Hypothesis Density; Vehicle Detection;

fLanguage

English

Publisher

ieee

Conference_Titel

Information Fusion, 2006 9th International Conference on

Conference_Location

Florence

Print_ISBN

1-4244-0953-5

Electronic_ISBN

0-9721844-6-5

Type

conf

DOI

10.1109/ICIF.2006.301648

Filename

4085934