Extended object tracking using IMM approach for a real-world vehicle sensor fusion system

Abstract

Autonomous driving poses unique challenges for vehicle environment perception due to the complicated driving environment where the autonomous vehicle connects itself with surrounding objects. Precise tracking of the relevant dynamic traffic participants (e.g., vehicle/byciclist/pedestrian) becomes a key component for the task of comprehensive environmental perception and reliable scene understanding. It is necessary for vehicle trackers to treat the objects as extended (rigid) target, as opposed to traditional point target tracking (say, in aerospace applications). The extended object tracking is an extremely challenging problem in real world, due to high requirements of the object estimation on accuracy of kinematic/shape information, association robustness, model match on various target motion behaviors, and statistical property amicability (e.g., estimation consistency/covariance reliability). We present an extended object tracker - based on an interacting multiple model with unbiased mixing estimator for kinematic information at a specified tracking reference point, a truncated Gaussian scheme for shape (width/length/orientation) estimation, and a hierarchical association method according to both kinematic and shape information - to tackle all of the major challenges. Our special effort is put on handling an intriguing conflict between theory and practice: the so-called likelihood credibility issue. That is, the likelihood is expected to credibly reflect the data statistical probability but is actually distorted/drifting in real world systems, due to mainly artificial physics introduced in multiple-stage data processing. In this study, from systematic point of view, we design an interacting multiple model based extended object tracker with proper likelihood compensation in the statistically-distorted real world. It can be shown that the presented tracker can deliver an effective estimation performance in real road traffic of the imperfect world.