Information fusion for online estimation of the behavior of traffic participants using belief function theory

Frontiers in Future Transportation

Future Transportation

Marion Leibold¹  Xuhui Zhang¹  Dirk Wollherr¹  Tommaso Benciolini² 

[1] Chair of Automatic Control Engineering, Technical University of Munich, Munich, Germany;null;

关键词: information fusion;    trajectory estimation;    estimate combination;    uncertainty handling;    belief function theory;   

DOI  :  10.3389/ffutr.2023.1216527

 received in 2023-05-04, accepted in 2023-08-09,  发布年份 2023

来源: Frontiers

【 摘 要 】

Motion planning algorithms for automated vehicles need to assess the intended behavior of other Traffic Participants (TPs), in order to predict the likely future trajectory of TPs and plan the motion consequently. Information resulting from several sources, like sensors, must be gathered and combined into a reliable estimate of the intended behavior of TPs. Such estimates must be sufficiently steady and quantify the inherent uncertainty around the assessment. We present a novel information fusion algorithm to combine information from different sources into a coherent and reliable estimate. To explicitly account for the uncertainty of estimates, we leverage the Belief Function Theory and evaluate and handle possible disagreements between estimates individually provided by the sources. The algorithm is flexible and can also handle sources that do not discern between some of the considered behaviors and are only capable of assessing the probability of unions or clusters of different behaviors. We discuss the strengths of the approach through simulations in SUMO, comparing it to the Interactive Multiple Model algorithm.

【 授权许可】

Unknown   
Copyright © 2023 Benciolini, Zhang, Wollherr and Leibold.