# Fast Road Segmentation via Uncertainty-aware Symmetric Network

arXiv: https://arxiv.org/abs/2203.04537 · HTML: https://mlsanity.com/paper/2203.04537/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- Builds on 28 of its 42 references that are in the map.
- First posted on arXiv 9 Mar 2022.

## Builds on (most cited first, 28 of 28)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [Fully Convolutional Networks for Semantic Segmentation](https://mlsanity.com/paper/1411.4038/) (2014)
- [Squeeze-and-Excitation Networks](https://mlsanity.com/paper/1709.01507/) (2017)
- [The Cityscapes Dataset for Semantic Urban Scene Understanding](https://mlsanity.com/paper/1604.01685/) (2016)
- [DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs](https://mlsanity.com/paper/1606.00915/) (2016)
- [SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation](https://mlsanity.com/paper/1511.00561/) (2015)
- [Evidential Deep Learning to Quantify Classification Uncertainty](https://mlsanity.com/paper/1806.01768/) (2018)
- [Holistically-Nested Edge Detection](https://mlsanity.com/paper/1504.06375/) (2015)
- [Bi-directional Cross-Modality Feature Propagation with Separation-and-Aggregation Gate for RGB-D Semantic Segmentation](https://mlsanity.com/paper/2007.09183/) (2020)
- [TANet: Robust 3D Object Detection from Point Clouds with Triple Attention](https://mlsanity.com/paper/1912.05163/) (2019)
- [ACNet: Attention Based Network to Exploit Complementary Features for RGBD Semantic Segmentation](https://mlsanity.com/paper/1905.10089/) (2019)
- [Trusted Multi-View Classification](https://mlsanity.com/paper/2102.02051/) (2021)
- [MultiNet: Real-time Joint Semantic Reasoning for Autonomous Driving](https://mlsanity.com/paper/1612.07695/) (2016)
- [SNE-RoadSeg: Incorporating Surface Normal Information into Semantic Segmentation for Accurate Freespace Detection](https://mlsanity.com/paper/2008.11351/) (2020)
- [LIDAR-Camera Fusion for Road Detection Using Fully Convolutional Neural Networks](https://mlsanity.com/paper/1809.07941/) (2018)
- [Progressive LiDAR Adaptation for Road Detection](https://mlsanity.com/paper/1904.01206/) (2019)
- [Real-time Fusion Network for RGB-D Semantic Segmentation Incorporating Unexpected Obstacle Detection for Road-driving Images](https://mlsanity.com/paper/2002.10570/) (2020)
- [Dynamic Fusion Module Evolves Drivable Area and Road Anomaly Detection: A Benchmark and Algorithms](https://mlsanity.com/paper/2103.02433/) (2021)
- [SNE-RoadSeg+: Rethinking Depth-Normal Translation and Deep Supervision for Freespace Detection](https://mlsanity.com/paper/2107.14599/) (2021)
- [Applying Surface Normal Information in Drivable Area and Road Anomaly Detection for Ground Mobile Robots](https://mlsanity.com/paper/2008.11383/) (2020)
- [Occlusion-shared and Feature-separated Network for Occlusion Relationship Reasoning](https://mlsanity.com/paper/1908.05898/) (2019)
- [A Novel Multi-layer Framework for Tiny Obstacle Discovery](https://mlsanity.com/paper/1904.10161/) (2019)
- [Video Text Tracking With a Spatio-Temporal Complementary Model](https://mlsanity.com/paper/2111.04987/) (2021)
- [Tiny Obstacle Discovery by Occlusion-Aware Multilayer Regression](https://mlsanity.com/paper/2111.09204/) (2021)
- [Embedding Structured Contour and Location Prior in Siamesed Fully Convolutional Networks for Road Detection](https://mlsanity.com/paper/1905.01575/) (2019)
- [RoadTrack: Realtime Tracking of Road Agents in Dense and Heterogeneous Environments](https://mlsanity.com/paper/1906.10712/) (2019)

## Built on by (most cited first, 17 of 17)

- [Provable Dynamic Fusion for Low-Quality Multimodal Data](https://mlsanity.com/paper/2306.02050/) (2023)
- [Multimodal Fusion on Low-quality Data: A Comprehensive Survey](https://mlsanity.com/paper/2404.18947/) (2024)
- [A Comprehensive Survey on Evidential Deep Learning and Its Applications](https://mlsanity.com/paper/2409.04720/) (2024)
- [AnomalyNCD: Towards Novel Anomaly Class Discovery in Industrial Scenarios](https://mlsanity.com/paper/2410.14379/) (2024)
- [SNE-RoadSegV2: Advancing Heterogeneous Feature Fusion and Fallibility Awareness for Freespace Detection](https://mlsanity.com/paper/2402.18918/) (2024)
- [Multi-Modal Multi-Task (3MT) Road Segmentation](https://mlsanity.com/paper/2308.11983/) (2023)
- [Evidential Semantic Mapping in Off-road Environments with Uncertainty-aware Bayesian Kernel Inference](https://mlsanity.com/paper/2403.14138/) (2024)
- [Polarimetric Imaging for Perception](https://mlsanity.com/paper/2305.14787/) (2023)
- [SkipcrossNets: Adaptive Skip-cross Fusion for Road Detection](https://mlsanity.com/paper/2308.12863/) (2023)
- [Depth Matters: Exploring Deep Interactions of RGB-D for Semantic Segmentation in Traffic Scenes](https://mlsanity.com/paper/2409.07995/) (2024)
- [SM4Depth: Seamless Monocular Metric Depth Estimation across Multiple Cameras and Scenes by One Model](https://mlsanity.com/paper/2403.08556/) (2024)
- [Uncertainty-aware Semantic Mapping in Off-road Environments with Dempster-Shafer Theory of Evidence](https://mlsanity.com/paper/2405.06265/) (2024)
- [Cross-Modality Domain Adaptation for Freespace Detection: A Simple yet Effective Baseline](https://mlsanity.com/paper/2210.02991/) (2022)
- [Erase, then Redraw: A Novel Data Augmentation Approach for Free Space Detection Using Diffusion Model](https://mlsanity.com/paper/2409.20164/) (2024)
- [Task-Oriented Pre-Training for Drivable Area Detection](https://mlsanity.com/paper/2409.20166/) (2024)
- [LiteViLNet: Lightweight Vision-LiDAR Fusion Network for Efficient Road Segmentation](https://mlsanity.com/paper/2605.21007/) (2026)
- [Towards Reliable Detection of Empty Space: Conditional Marked Point Processes for Object Detection](https://mlsanity.com/paper/2506.21486/) (2025)

## Abstract (arXiv)

The high performance of RGB-D based road segmentation methods contrasts with their rare application in commercial autonomous driving, which is owing to two reasons: 1) the prior methods cannot achieve high inference speed and high accuracy in both ways; 2) the different properties of RGB and depth data are not well-exploited, limiting the reliability of predicted road. In this paper, based on the evidence theory, an uncertainty-aware symmetric network (USNet) is proposed to achieve a trade-off between speed and accuracy by fully fusing RGB and depth data. Firstly, cross-modal feature fusion operations, which are indispensable in the prior RGB-D based methods, are abandoned. We instead separately adopt two light-weight subnetworks to learn road representations from RGB and depth inputs. The light-weight structure guarantees the real-time inference of our method. Moreover, a multiscale evidence collection (MEC) module is designed to collect evidence in multiple scales for each modality, which provides sufficient evidence for pixel class determination. Finally, in uncertainty-aware fusion (UAF) module, the uncertainty of each modality is perceived to guide the fusion of the two subnetworks. Experimental results demonstrate that our method achieves a state-of-the-art accuracy with real-time inference speed of 43+ FPS. The source code is available at https://github.com/morancyc/USNet.