Uncertainty-Aware AB3DMOT by Variational 3D Object Detection

TL;DR

This paper introduces an uncertainty-aware 3D object detection and tracking system for autonomous driving, leveraging variational neural networks and uncertainty estimation to improve detection accuracy and safety.

Contribution

It proposes a novel variational neural network-based 3D detector and integrates uncertainty estimation into a multi-object tracker, enhancing robustness over traditional methods.

Findings

External uncertainty estimation outperforms internal estimation.

The proposed method surpasses classical tracking approaches.

Pretraining the detector improves overall performance.

Abstract

Autonomous driving needs to rely on high-quality 3D object detection to ensure safe navigation in the world. Uncertainty estimation is an effective tool to provide statistically accurate predictions, while the associated detection uncertainty can be used to implement a more safe navigation protocol or include the user in the loop. In this paper, we propose a Variational Neural Network-based TANet 3D object detector to generate 3D object detections with uncertainty and introduce these detections to an uncertainty-aware AB3DMOT tracker. This is done by applying a linear transformation to the estimated uncertainty matrix, which is subsequently used as a measurement noise for the adopted Kalman filter. We implement two ways to estimate output uncertainty, i.e., internally, by computing the variance of the CNN outputs and then propagating the uncertainty through the post-processing, and externally, by associating the final predictions of different samples and computing the covariance of each predicted box. In experiments, we show that the external uncertainty estimation leads to better results, outperforming both internal uncertainty estimation and classical tracking approaches. Furthermore, we propose a method to initialize the Variational 3D object detector with a pretrained TANet model, which leads to the best performing models.