The image shows a view of Hungary’s capital Budapest at sunset. Several elements in the photo are marked with orange rectangles, which are part of AI-empowered object recognition and image analysis. Each rectangle is labeled with a designation and a percentage indicating the probability of recognition or model confidence.

AUMOVIO AI Development Center

Developing concrete AI solutions in Hungary’s capital Budapest.

At the AUMOVIO Artificial Intelligence Development Center in Budapest, Hungary, we create next-generation automotive software solutions for making automated driving safe.  

Who we are

Our teams develop AI technologies for driver assistance and automated driving systems that encompass computer vision, sensor fusion and environmental modeling. The goal: eliminating fatal road accidents within AUMOVIO’s Vision Zero.

What we do

At our AI Development Center in Budapest, Hungary, we are passionate about bringing state-of-the-art deep learning technology into our products, based on camera, LiDAR, and radar perception.
A futuristic vehicle drives on a bridge with other road users such as a motorcycle and a truck. Around the vehicles, you can see orange sensor lines for assisted and automated driving.

Assisted and automated driving

We began our journey with delivering front-vision AI solutions for the 5th generation MFC camera family, and since then, we have been gradually increasing the complexity. Currently, our neural networks are trained on front-view MFC and surround-view SVC cameras, as well as on LiDAR and radar sensors. Delivering these solutions calls for the use of cutting-edge deep learning algorithms with ever-evolving neural architectures. Through our partnership with Aurora, we are expanding our scope from advanced driver assistance systems to highly autonomous and fully automated driving.
A woman uses her smartphone to automatically move a futuristic vehicle out of its parking space in a parking garage.

Automated parking solutions

In our automated parking solutions, we leverage camera vision, AI-based perception and scene understanding. We develop intelligent algorithms to detect parking-related objects and scenarios seamlessly. With a "Safety First" mindset, our low-speed collision avoidance systems ensure utmost safety for all. Our robust data management infrastructure, including data labeling, engineering, enrichment, and multimodal LLM-based description generation, fuels high-quality data for model training.
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Synthetic data generation

We are actively engaged in data science research and experimentation, exploring novel techniques for synthetic data generation. This approach not only augments the available data but also enables the creation of diverse and realistic scenarios, further enhancing the robustness andadaptability of their AI solutions.
A futuristic vehicle is driving on a road. Various orange and purple sensor and radar lines can be seen coming from the vehicle, visualizing object detection and other road users.

Highly scalable hardware

In order to master growing algorithmic complexity, we have teamed up with Ambarella to achieve highly scalable hardware for deploying our solutions. Together, we develop perception solutions for advanced driver assistance and highly automated driving to achieve up to five times higher energy efficiency.

Publications

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Archived Publications

The following publications were released at a time when AUMOVIO was still operating under its former company name,  Continental Automotive. For this reason, the previous company name still appears in the publications. Please note that we have no influence over the company name used in already published materials.

  • Utasi, Á. (2022): “PEA: Improving the Performance of ReLU Networks for Free by Using Progressive Ensemble Activations”, in: Efficient Deep Learning for Computer Vision (ECV) CVPR Workshop.
  • T. Lorincz, M. Szemenyei, and R. Moni. Imitation Learning for Generalizable Self- Driving Policy with Sim-to-Real Transfer. ICLR 2022 Workshop on Generalizable Policy Learning in Physical World, Poster.
  • S. Skribanek, M. Szemenyei and R. Moni. Semantically consistent sim-to-real image translation with neural networks. 2022 IEEE Intelligent Vehicles Symposium (IV 2022)

  • Andras Kalapos, Csaba Ger, Robert Moni, and Istvan Harmati. Vision based reinforcement learning for lane tracking control. Acta International Measurement Confederation (IMEKO), 10(3):4-7, 2021.

  • P. Almasi, R. Moni, and B. Gyires-Toth. Robust reinforcement learning-based autonomous driving agent for simulation and real world. In 2020 International Joint Conference on Neural Networks (IJCNN), pages 1-8, 2020.
  • A. Kalapos, C. Cor, R. Moni, and I. Harmati. Sim-to-real reinforcement learning applied to end-to-end vehicle control. In 2020 23rd International Symposium on Measurement and Control in Robotics (ISMCR), pages 1-6, 2020.
  • M. Tim, M. Szemenyei, and R. Moni. Simulation to real domain adaptation for lane segmentation. In 2020 23rd International Symposium on Measurement and Control in Robotics (ISMCR), pages 1-6, 2020.)

Our AI experts

With passion, our AI experts at the AI Development Center in Budapest, are developing new AI technologies for driver assistance and automated driving systems day by day.

  

For insights into the innovative work being done by our experts, please head to our expert overview.  
Our AI experts