# First qualitative observations on deep learning vision model YOLO and DETR for automated driving in Austria

arXiv: https://arxiv.org/abs/2312.12314 · HTML: https://mlsanity.com/paper/2312.12314/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 27 references that are in the map.
- First posted on arXiv 19 Dec 2023.

## Builds on (most cited first, 19 of 19)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Mask R-CNN](https://mlsanity.com/paper/1703.06870/) (2017)
- [You Only Look Once: Unified, Real-Time Object Detection](https://mlsanity.com/paper/1506.02640/) (2015)
- [nuScenes: A multimodal dataset for autonomous driving](https://mlsanity.com/paper/1903.11027/) (2019)
- [The Cityscapes Dataset for Semantic Urban Scene Understanding](https://mlsanity.com/paper/1604.01685/) (2016)
- [Fast R-CNN](https://mlsanity.com/paper/1504.08083/) (2015)
- [YOLOv3: An Incremental Improvement](https://mlsanity.com/paper/1804.02767/) (2018)
- [YOLO9000: Better, Faster, Stronger](https://mlsanity.com/paper/1612.08242/) (2016)
- [Argoverse: 3D Tracking and Forecasting with Rich Maps](https://mlsanity.com/paper/1911.02620/) (2019)
- [Argoverse 2: Next Generation Datasets for Self-Driving Perception and Forecasting](https://mlsanity.com/paper/2301.00493/) (2023)
- [KITTI-360: A Novel Dataset and Benchmarks for Urban Scene Understanding in 2D and 3D](https://mlsanity.com/paper/2109.13410/) (2021)
- [The ApolloScape Open Dataset for Autonomous Driving and its Application](https://mlsanity.com/paper/1803.06184/) (2018)
- [SSAST: Self-Supervised Audio Spectrogram Transformer](https://mlsanity.com/paper/2110.09784/) (2021)
- [MultiNet: Real-time Joint Semantic Reasoning for Autonomous Driving](https://mlsanity.com/paper/1612.07695/) (2016)
- [OmniVec: Learning robust representations with cross modal sharing](https://mlsanity.com/paper/2311.05709/) (2023)
- [A Commute in Data: The comma2k19 Dataset](https://mlsanity.com/paper/1812.05752/) (2018)
- [PixSet : An Opportunity for 3D Computer Vision to Go Beyond Point Clouds With a Full-Waveform LiDAR Dataset](https://mlsanity.com/paper/2102.12010/) (2021)
- [NVAutoNet: Fast and Accurate 360circ 3D Visual Perception For Self Driving](https://mlsanity.com/paper/2303.12976/) (2023)

## Built on by (most cited first, 1 of 1)

- [Object Detection for Autonomous Driving in Chinese Rural Scenes: An Experimental Study on Real-Synthetic Data Mixing and Model Evaluation](https://mlsanity.com/paper/2607.27058/) (2026)

## Abstract (arXiv)

This study investigates the application of single and two-stage 2D-object detection algorithms like You Only Look Once (YOLO), Real-Time DEtection TRansformer (RT-DETR) algorithm for automated object detection to enhance road safety for autonomous driving on Austrian roads. The YOLO algorithm is a state-of-the-art real-time object detection system known for its efficiency and accuracy. In the context of driving, its potential to rapidly identify and track objects is crucial for advanced driver assistance systems (ADAS) and autonomous vehicles. The research focuses on the unique challenges posed by the road conditions and traffic scenarios in Austria. The country's diverse landscape, varying weather conditions, and specific traffic regulations necessitate a tailored approach for reliable object detection. The study utilizes a selective dataset comprising images and videos captured on Austrian roads, encompassing urban, rural, and alpine environments.