# AuxNet: Auxiliary tasks enhanced Semantic Segmentation for Automated Driving

arXiv: https://arxiv.org/abs/1901.05808 · HTML: https://mlsanity.com/paper/1901.05808/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Jan 2019.

## Built on by (most cited first, 19 of 19)

- [MobTCast: Leveraging Auxiliary Trajectory Forecasting for Human Mobility Prediction](https://mlsanity.com/paper/2110.01401/) (2021)
- [Surround-view Fisheye Camera Perception for Automated Driving: Overview, Survey and Challenges](https://mlsanity.com/paper/2205.13281/) (2022)
- [Near-field Perception for Low-Speed Vehicle Automation using Surround-view Fisheye Cameras](https://mlsanity.com/paper/2103.17001/) (2021)
- [Adversarial Attacks on Multi-task Visual Perception for Autonomous Driving](https://mlsanity.com/paper/2107.07449/) (2021)
- [BlazeNeo: Blazing fast polyp segmentation and neoplasm detection](https://mlsanity.com/paper/2203.00129/) (2022)
- [ViT-BEVSeg: A Hierarchical Transformer Network for Monocular Birds-Eye-View Segmentation](https://mlsanity.com/paper/2205.15667/) (2022)
- [Neuroevolutionary Multi-objective approaches to Trajectory Prediction in Autonomous Vehicles](https://mlsanity.com/paper/2205.02105/) (2022)
- [Adaptive Distillation: Aggregating Knowledge from Multiple Paths for Efficient Distillation](https://mlsanity.com/paper/2110.09674/) (2021)
- [Leveraging convergence behavior to balance conflicting tasks in multi-task learning](https://mlsanity.com/paper/2204.06698/) (2022)
- [A Threefold Review on Deep Semantic Segmentation: Efficiency-oriented, Temporal and Depth-aware design](https://mlsanity.com/paper/2303.04315/) (2023)
- [FisheyeDetNet: 360deg Surround view Fisheye Camera based Object Detection System for Autonomous Driving](https://mlsanity.com/paper/2404.13443/) (2024)
- [SpikiLi: A Spiking Simulation of LiDAR based Real-time Object Detection for Autonomous Driving](https://mlsanity.com/paper/2206.02876/) (2022)
- [Fast Line Search for Multi-Task Learning](https://mlsanity.com/paper/2110.00874/) (2021)
- [UnShadowNet: Illumination Critic Guided Contrastive Learning For Shadow Removal](https://mlsanity.com/paper/2203.15441/) (2022)
- [Leveraging Road Area Semantic Segmentation with Auxiliary Steering Task](https://mlsanity.com/paper/2212.09438/) (2022)
- [Regularization Through Simultaneous Learning: A Case Study on Plant Classification](https://mlsanity.com/paper/2305.13447/) (2023)
- [Neural Rendering based Urban Scene Reconstruction for Autonomous Driving](https://mlsanity.com/paper/2402.06826/) (2024)
- [Dataset Safety in Autonomous Driving: Requirements, Risks, and Assurance](https://mlsanity.com/paper/2511.08439/) (2025)
- [Multi-Modal Traffic Sign Detection with Semantic Attributes for Autonomous Driving](https://mlsanity.com/paper/2608.20874/) (2026)

## Abstract (arXiv)

Decision making in automated driving is highly specific to the environment and thus semantic segmentation plays a key role in recognizing the objects in the environment around the car. Pixel level classification once considered a challenging task which is now becoming mature to be productized in a car. However, semantic annotation is time consuming and quite expensive. Synthetic datasets with domain adaptation techniques have been used to alleviate the lack of large annotated datasets. In this work, we explore an alternate approach of leveraging the annotations of other tasks to improve semantic segmentation. Recently, multi-task learning became a popular paradigm in automated driving which demonstrates joint learning of multiple tasks improves overall performance of each tasks. Motivated by this, we use auxiliary tasks like depth estimation to improve the performance of semantic segmentation task. We propose adaptive task loss weighting techniques to address scale issues in multi-task loss functions which become more crucial in auxiliary tasks. We experimented on automotive datasets including SYNTHIA and KITTI and obtained 3% and 5% improvement in accuracy respectively.