Leveraging Heteroscedastic Aleatoric Uncertainties for Robust Real-Time LiDAR 3D Object Detection

09/14/2018
by   Di Feng, et al.
0

We present a robust real-time LiDAR 3D object detector that leverages heteroscedastic aleatoric uncertainties to significantly improve its detection performance. A multi-loss function is designed to incorporate uncertainty estimations predicted by auxiliary output layers. Using our proposed method, the network ignores to train from noisy samples, and focuses more on informative ones. We validate our method on the KITTI object detection benchmark. Our method surpasses the baseline method which does not explicitly estimate uncertainties by up to nearly 9 It also produces state-of-the-art results compared to other methods while running with an inference time of only 72 ms. In addition, we conduct extensive experiments to understand how aleatoric uncertainties behave. Extracting aleatoric uncertainties brings almost no additional computation cost during the deployment, making our method highly desirable for autonomous driving applications.

READ FULL TEXT
research
02/01/2020

Leveraging Uncertainties for Deep Multi-modal Object Detection in Autonomous Driving

This work presents a probabilistic deep neural network that combines LiD...
research
11/07/2022

3D Harmonic Loss: Towards Task-consistent and Time-friendly 3D Object Detection on Edge for Intelligent Transportation System

Edge computing-based 3D perception has received attention in intelligent...
research
11/21/2019

RefinedMPL: Refined Monocular PseudoLiDAR for 3D Object Detection in Autonomous Driving

In this paper, we strive for solving the ambiguities arisen by the astou...
research
01/08/2018

Towards Multi-Object Detection and Tracking in Urban Scenario under Uncertainties

Urban-oriented autonomous vehicles require a reliable perception technol...
research
09/12/2023

RT-LM: Uncertainty-Aware Resource Management for Real-Time Inference of Language Models

Recent advancements in language models (LMs) have gained substantial att...
research
07/11/2022

Real-Time And Robust 3D Object Detection with Roadside LiDARs

This work aims to address the challenges in autonomous driving by focusi...
research
03/06/2023

EvCenterNet: Uncertainty Estimation for Object Detection using Evidential Learning

Uncertainty estimation is crucial in safety-critical settings such as au...

Please sign up or login with your details

Forgot password? Click here to reset