Fetching the paper…

Reading the bibliography…

2020

Probabilistic 3D Multi-Object Tracking for Autonomous Driving

Chiu, Hsu-kuang, Prioletti, Antonio, Li, Jie et al.

Original
PDF
Post on X

Understand

3D multi-object tracking is a key module in autonomous driving applications that provides a reliable dynamic representation of the world to the planning module.

In this paper, we present our on-line tracking method, which made the first place in the NuScenes Tracking Challenge, held at the AI Driving Olympics Workshop at NeurIPS 2019.
Our method estimates the object states by adopting a Kalman Filter.
We initialize the state covariance as well as the process and observation noise covariance with statistics from the training set.

Probabilistic 3D Multi-Object Tracking for Autonomous Driving · Around

Built on

On the generalized distance in statistics
Prasanta Chandra Mahalanobis · 1936
Earlier work this paper cites.
A new approach to linear filtering and prediction problems
Rudolph Emil Kalman · 1960
Earlier work this paper cites.
Probabilistic Robotics (Intelligent Robotics and Autonomous Agents)
Sebastian Thrun, Wolfram Burgard, and Dieter Fox · 2005
Earlier work this paper cites.

Similar

Pointnet++: Deep hierarchical feature learning on point sets in a metric space
Original
Charles R Qi, Li Yi, Hao Su, and Leonidas J Guibas · 2017
Cited alongside, same era.
nuscenes: A multimodal dataset for autonomous driving
Original
Holger Caesar, Varun Bankiti, Alex H. Lang, Sourabh Vora, Venice Erin Liong, Qiang Xu, Anush Krishnan, Yu Pan, Giancarlo Baldan, and Oscar Beijbom · 2019
Cited alongside, same era.
Argoverse: 3d tracking and forecasting with rich maps
Ming-Fang Chang, John W Lambert, Patsorn Sangkloy, Jagjeet Singh, Slawomir Bak, Andrew Hartnett, De Wang, Peter Carr, Simon Lucey, Deva Ramanan, and James Hays · 2019
Cited alongside, same era.

Then

Joint monocular 3d detection and tracking
Hou-Ning Hu, Qi-Zhi Cai, Dequan Wang, Ji Lin, Min Sun, Philipp Krähenbühl, Trevor Darrell, and Fisher Yu · 2019
Later among the works it cites.
A Baseline for 3D Multi-Object Tracking
Original
Xinshuo Weng and Kris Kitani · 2019
Later among the works it cites.
Class-balanced Grouping and Sampling for Point Cloud 3D Object Detection
Original
Benjin Zhu, Zhengkai Jiang, Xiangxin Zhou, Zeming Li, and Gang Yu · 2019
Later among the works it cites.

Beyond the bibliography

alphaXiv searches the wider corpus for related work and actual follow-ups.

Open on alphaXiv

alphaXiv is searching for related work…