An adaptive model switching approach for a multisensor tracking system used for autonomous driving in an urban environment

Abstract

For most of the existing commercial driver assistance systems the use of a single environmental sensor and a tracking model tied to the characteristics of this sensor is sufficient. When using a multi-sensor fusion approach with heterogeneous sensors the information available for tracking depends on the sensors detecting the object. This paper describes an approach where multiple models are used for tracking moving objects. The best model for tracking is chosen based on the available sensor information. The architecture of the tracking system along with the tracking models and algorithm for model selection are presented. The design of the architecture and algorithms allows an extension of the system with new sensors and tracking models without changing existing software. The approach was implemented and successfully used in Tartan Racing? autonomous vehicle for the Urban Grand Challenge. The advantages of the multisensor approach are explained and practical results of a representative scenario are presented.