Latest revision as of 21:50, 3 February 2021

Abstract

Future driver assistance systems are likely to use a multisensor approach with heterogeneous sensors for tracking dynamic objects around the vehicle. The quality and type of data available for a data fusion algorithm depends heavily on the sensors detecting an object. This article presents a general framework which allows the use sensor specific advantages while abstracting the specific details of a sensor. Different tracking models are used depending on the current set of sensors detecting the object. A sensor independent algorithm for classifying objects regarding their current and past movement state is presented. The described architecture and algorithms have been successfully implemented in Tartan racingpsilas autonomous vehicle for the urban grand challenge. Results are presented and discussed.

Original document

The different versions of the original document can be found in:

• http://dx.doi.org/10.1184/r1/6552260

• http://dx.doi.org/10.1184/r1/6552260.v1

• http://www.ri.cmu.edu/pub_files/pub4/darms_michael_2008_3/darms_michael_2008_3.pdf

• https://figshare.com/articles/journal_contribution/Classification_and_Tracking_of_Dynamic_Objects_with_Multiple_Sensors_for_Autonomous_Driving_in_Urban_Environments/6552260

• http://repository.cmu.edu/robotics/894,

https://ieeexplore.ieee.org/document/4621259,

https://academic.microsoft.com/#/detail/2171072057

• http://xplorestaging.ieee.org/ielx5/4607789/4621124/04621259.pdf?arnumber=4621259,

http://dx.doi.org/10.1109/ivs.2008.4621259

DOIS: 10.1184/r1/6552260.v1 10.1184/r1/6552260 10.1109/ivs.2008.4621259