Deep multi-object tracking for ground truth trajectory estimation
Research Project , 2018 – 2022

The development of automated vehicles is of great importance for the Swedish vehicle industry, and may lead to substantial gains for society. Accurate environment perception is essential to automated vehicles, since it enables vehicles to sense nearby objects, and estimate their positions as well as other relevant properties. The perception systems in modern vehicles make use of data from cameras, LIDAR sensors, etc., in order to obtain a detailed understanding of the current situation. However, more development is needed before these systems can robustly provide the accuracy required for a vehicle to drive autonomously in all situations. We will address certain aspects of environment perception, pertaining to tracking of multiple dynamic objects. Specifically, we aim to develop algorithms that provide high-precision estimates of the trajectories of all dynamic objects located around the host vehicle. The purpose is to obtain an efficient technique to extract estimates that can be viewed as ground truth, which is of utmost importance to have for the development and verification of both perception and control modules. We will investigate off-line techniques, and combining deep learning with sensor fusion, to enable extraction of as much information as possible from the data.

Participants

Tomas McKelvey (contact)

Professor vid Chalmers, Electrical Engineering, Signalbehandling och medicinsk teknik

Karl Granström

Forskare vid Chalmers, Electrical Engineering, Signalbehandling och medicinsk teknik

Lennart Svensson

Biträdande professor vid Chalmers, Electrical Engineering, Signalbehandling och medicinsk teknik

Collaborations

Zenuity

Gothenburg, Sweden

Funding

VINNOVA

Funding Chalmers participation during 2018–2022

More information

Latest update

2019-04-05