Extended Object Tracking with Automotive Radar Using Learned Structural Measurement Model
Paper i proceeding, 2020

This paper presents a data-driven measurement model for extended object tracking (EOT) with automotive radar. Specifically, the spatial distribution of automotive radar measurements is modeled as a hierarchical truncated Gaussian with structural geometry parameters (e.g., truncation bounds, their orientation, and a scaling factor) learned from the training data. The contribution is twofold. First, the learned measurement model can provide an adequate resemblance to the spatial distribution of real-world automotive radar measurements. Second, large-scale offline training datasets can be leveraged to learn the geometry-related parameters and offload the computationally demanding model parameter estimation from the state update step. The learned structural measurement model is further incorporated into the random matrix-based EOT approach with a new state update step. The effectiveness of the proposed approach is verified on the nuScenes dataset.

autonomous driving

random matrix

Automotive radar

nuScenes

extended object tracking

Författare

Yuxuan Xia

Chalmers, Elektroteknik, Signalbehandling och medicinsk teknik, Signalbehandling

Pu Wang

Mitsubishi Electric Research Laboratories

Karl Berntorp

Mitsubishi Electric Research Laboratories

Petros Boufounos

Mitsubishi Electric Research Laboratories

Philip V. Orlik

Mitsubishi Electric Research Laboratories

Lennart Svensson

Chalmers, Elektroteknik, Signalbehandling och medicinsk teknik, Signalbehandling

Karl Granström

Chalmers, Elektroteknik, Signalbehandling och medicinsk teknik, Signalbehandling

2020 IEEE Radar Conference (RadarConf20)

2375-5318 (ISSN) 1097-5659 (eISSN)

2020 IEEE Radar Conference (RadarConf20)
Florence, Italy,

Ämneskategorier

Sannolikhetsteori och statistik

Reglerteknik

Datorseende och robotik (autonoma system)

DOI

10.1109/RadarConf2043947.2020.9266598

Mer information

Senast uppdaterat

2021-03-08