# Highway Driving Dataset for Semantic Video Segmentation

arXiv: https://arxiv.org/abs/2011.00674 · HTML: https://mlsanity.com/paper/2011.00674/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Nov 2020.

## Built on by (most cited first, 5 of 5)

- [A Survey on Approximate Edge AI for Energy Efficient Autonomous Driving Services](https://mlsanity.com/paper/2304.14271/) (2023)
- [Efficient Semantic Segmentation by Altering Resolutions for Compressed Videos](https://mlsanity.com/paper/2303.07224/) (2023)
- [TruckDrive: Long-Range Autonomous Highway Driving Dataset](https://mlsanity.com/paper/2603.02413/) (2026)
- [Where It Moves, It Matters: Referring Surgical Instrument Segmentation via Motion](https://mlsanity.com/paper/2601.12224/) (2026)
- [Shift-Memory Network for Temporal Scene Segmentation](https://mlsanity.com/paper/2202.08399/) (2022)

## Abstract (arXiv)

Scene understanding is an essential technique in semantic segmentation. Although there exist several datasets that can be used for semantic segmentation, they are mainly focused on semantic image segmentation with large deep neural networks. Therefore, these networks are not useful for real time applications, especially in autonomous driving systems. In order to solve this problem, we make two contributions to semantic segmentation task. The first contribution is that we introduce the semantic video dataset, the Highway Driving dataset, which is a densely annotated benchmark for a semantic video segmentation task. The Highway Driving dataset consists of 20 video sequences having a 30Hz frame rate, and every frame is densely annotated. Secondly, we propose a baseline algorithm that utilizes a temporal correlation. Together with our attempt to analyze the temporal correlation, we expect the Highway Driving dataset to encourage research on semantic video segmentation.