# ANNA: A Deep Learning Based Dataset in Heterogeneous Traffic for Autonomous Vehicles

arXiv: https://arxiv.org/abs/2401.11358 · HTML: https://mlsanity.com/paper/2401.11358/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 13 of its 30 references that are in the map.
- First posted on arXiv 21 Jan 2024.

## Builds on (most cited first, 13 of 13)

- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [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)
- [CARLA: An Open Urban Driving Simulator](https://mlsanity.com/paper/1711.03938/) (2017)
- [Virtual Worlds as Proxy for Multi-Object Tracking Analysis](https://mlsanity.com/paper/1605.06457/) (2016)
- [The ApolloScape Open Dataset for Autonomous Driving and its Application](https://mlsanity.com/paper/1803.06184/) (2018)
- [WoodScape: A multi-task, multi-camera fisheye dataset for autonomous driving](https://mlsanity.com/paper/1905.01489/) (2019)
- [Real-time Kinematic Ground Truth for the Oxford RobotCar Dataset](https://mlsanity.com/paper/2002.10152/) (2020)
- [Comparing Offline and Online Testing of Deep Neural Networks: An Autonomous Car Case Study](https://mlsanity.com/paper/1912.00805/) (2019)
- [YOLO-Z: Improving small object detection in YOLOv5 for autonomous vehicles](https://mlsanity.com/paper/2112.11798/) (2021)
- [Behavioral Research and Practical Models of Drivers' Attention](https://mlsanity.com/paper/2104.05677/) (2021)
- [2nd Place Solution for Waymo Open Dataset Challenge -- Real-time 2D Object Detection](https://mlsanity.com/paper/2106.08713/) (2021)
- [Authoring Platform for Mobile Citizen Science Apps with Client-side ML](https://mlsanity.com/paper/2212.05411/) (2022)

## Abstract (arXiv)

Recent breakthroughs in artificial intelligence offer tremendous promise for the development of self-driving applications. Deep Neural Networks, in particular, are being utilized to support the operation of semi-autonomous cars through object identification and semantic segmentation. To assess the inadequacy of the current dataset in the context of autonomous and semi-autonomous cars, we created a new dataset named ANNA. This study discusses a custom-built dataset that includes some unidentified vehicles in the perspective of Bangladesh, which are not included in the existing dataset. A dataset validity check was performed by evaluating models using the Intersection Over Union (IOU) metric. The results demonstrated that the model trained on our custom dataset was more precise and efficient than the models trained on the KITTI or COCO dataset concerning Bangladeshi traffic. The research presented in this paper also emphasizes the importance of developing accurate and efficient object detection algorithms for the advancement of autonomous vehicles.