# Learning an Uncertainty-Aware Object Detector for Autonomous Driving

arXiv: https://arxiv.org/abs/1910.11375 · HTML: https://mlsanity.com/paper/1910.11375/

## Key facts

- Cited by 34 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Oct 2019.

## Built on by (most cited first, 30 of 34)

- [DeepFusion: Lidar-Camera Deep Fusion for Multi-Modal 3D Object Detection](https://mlsanity.com/paper/2203.08195/) (2022)
- [Scene Transformer: A unified architecture for predicting multiple agent trajectories](https://mlsanity.com/paper/2106.08417/) (2021)
- [Uncertainty Quantification of Collaborative Detection for Self-Driving](https://mlsanity.com/paper/2209.08162/) (2022)
- [Collaborative Multi-Object Tracking with Conformal Uncertainty Propagation](https://mlsanity.com/paper/2303.14346/) (2023)
- [GLENet: Boosting 3D Object Detectors with Generative Label Uncertainty Estimation](https://mlsanity.com/paper/2207.02466/) (2022)
- [Keypoint-Based Category-Level Object Pose Tracking from an RGB Sequence with Uncertainty Estimation](https://mlsanity.com/paper/2205.11047/) (2022)
- [MVFuseNet: Improving End-to-End Object Detection and Motion Forecasting through Multi-View Fusion of LiDAR Data](https://mlsanity.com/paper/2104.10772/) (2021)
- [Vehicle-to-Everything Cooperative Perception for Autonomous Driving](https://mlsanity.com/paper/2310.03525/) (2023)
- [Uncertainty estimation for Cross-dataset performance in Trajectory prediction](https://mlsanity.com/paper/2205.07310/) (2022)
- [Mutual Information-calibrated Conformal Feature Fusion for Uncertainty-Aware Multimodal 3D Object Detection at the Edge](https://mlsanity.com/paper/2309.09593/) (2023)
- [Out-of-distribution detection algorithms for robust insect classification](https://mlsanity.com/paper/2305.01823/) (2023)
- [Safety Implications of Explainable Artificial Intelligence in End-to-End Autonomous Driving](https://mlsanity.com/paper/2403.12176/) (2024)
- [Autoregressive Uncertainty Modeling for 3D Bounding Box Prediction](https://mlsanity.com/paper/2210.07424/) (2022)
- [Not Every Side Is Equal: Localization Uncertainty Estimation for Semi-Supervised 3D Object Detection](https://mlsanity.com/paper/2312.10390/) (2023)
- [CUQDS: Conformal Uncertainty Quantification under Distribution Shift for Trajectory Prediction](https://mlsanity.com/paper/2406.12100/) (2024)
- [Estimating Regression Predictive Distributions with Sample Networks](https://mlsanity.com/paper/2211.13724/) (2022)
- [Probabilistic Deep Learning to Quantify Uncertainty in Air Quality Forecasting](https://mlsanity.com/paper/2112.02622/) (2021)
- [Uncertainty-Aware AB3DMOT by Variational 3D Object Detection](https://mlsanity.com/paper/2302.05923/) (2023)
- [Efficient Transformer-based 3D Object Detection with Dynamic Token Halting](https://mlsanity.com/paper/2303.05078/) (2023)
- [LMD: Light-weight Prediction Quality Estimation for Object Detection in Lidar Point Clouds](https://mlsanity.com/paper/2306.07835/) (2023)
- [A Light-Weight Framework for Open-Set Object Detection with Decoupled Feature Alignment in Joint Space](https://mlsanity.com/paper/2412.14680/) (2024)
- [Conformal Object Detection by Sequential Risk Control](https://mlsanity.com/paper/2505.24038/) (2025)
- [Uncertainty-Aware Lidar Place Recognition in Novel Environments](https://mlsanity.com/paper/2210.01361/) (2022)
- [TSGN: Temporal Scene Graph Neural Networks with Projected Vectorized Representation for Multi-Agent Motion Prediction](https://mlsanity.com/paper/2305.08190/) (2023)
- [Training of Neural Networks with Uncertain Data: A Mixture of Experts Approach](https://mlsanity.com/paper/2312.08083/) (2023)
- [CTS: Sim-to-Real Unsupervised Domain Adaptation on 3D Detection](https://mlsanity.com/paper/2406.18129/) (2024)
- [Object criticality for safer navigation](https://mlsanity.com/paper/2406.10232/) (2024)
- [Cooperative Variance Estimation and Bayesian Neural Networks for Disentangling Aleatoric and Epistemic Uncertainties](https://mlsanity.com/paper/2505.02743/) (2025)
- [Better than Average: Spatially-Aware Aggregation of Segmentation Uncertainty Improves Downstream Performance](https://mlsanity.com/paper/2603.29941/) (2026)
- [Variational Voxel Pseudo Image Tracking](https://mlsanity.com/paper/2302.05914/) (2023)

## Abstract (arXiv)

The capability to detect objects is a core part of autonomous driving. Due to sensor noise and incomplete data, perfectly detecting and localizing every object is infeasible. Therefore, it is important for a detector to provide the amount of uncertainty in each prediction. Providing the autonomous system with reliable uncertainties enables the vehicle to react differently based on the level of uncertainty. Previous work has estimated the uncertainty in a detection by predicting a probability distribution over object bounding boxes. In this work, we propose a method to improve the ability to learn the probability distribution by considering the potential noise in the ground-truth labeled data. Our proposed approach improves not only the accuracy of the learned distribution but also the object detection performance.