Multisensor simultaneous vehicle tracking and shape estimation

J. Elfring, R.P.W. Appeldoorn, M.R.J.A.E. Kwakkernaat

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

7 Citations (Scopus)


This work focuses on vehicle automation applications that require both the estimation of kinematic and geometric information of surrounding vehicles, e.g., automated overtaking or merging. Rather then using one sensor that is able to estimate a vehicle's geometry from each sensor frame, e.g., a lidar, a multisensor simultaneous vehicle tracking and shape estimation approach is proposed. Advanced measurement models and adequate Bayesian filters enable the shape estimation that is impossible with any of the sensors individually. The use of multiple sensors increases robustness, lowers the complexity of the sensors involved and leads to a gradual loss of performance in case a sensor fails. A series of real world experiments is performed to analyze the performance of the proposed method.
Original languageEnglish
Title of host publication2016 IEEE Intelligent Vehicles Symposium (IV), 19-22 June 2016, Gothenburg, Sweden
Place of PublicationPiscataway
PublisherInstitute of Electrical and Electronics Engineers
ISBN (Electronic)978-1-5090-1821-5
ISBN (Print)978-1-5090-1822-2
Publication statusPublished - 2016
Event2016 IEEE Intelligent Vehicles Symposium, IV 2016 - Gothenburg, Sweden
Duration: 19 Jun 201622 Jun 2016


Conference2016 IEEE Intelligent Vehicles Symposium, IV 2016
Abbreviated titleIV 2016


Dive into the research topics of 'Multisensor simultaneous vehicle tracking and shape estimation'. Together they form a unique fingerprint.

Cite this