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Self-driving technologies for automotive industry

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About Roboauto

We are team of AI professionals developing technologies for automated cars. We invent and test new methods and algorithms of sensor fusion, object recognition and path planning on our prototype Roboauto #1. We measure their performance and develop improvements. We help other companies to validate their sensors and equipment for automotive industry.

We are located in heard of the Europe, Brno, Czech Republic.

Our services

Sensor data fusion

We develop methods to integrate multiple data sources to produce more consistent, accurate and useful world model to assist in self-driving capabilities.

Simulations

One of the most important part in developing self-driving car is to simulate as much driving as possible to reduce the cost of testing in real world. We are currently using simulations in Gazebo and GTA V which enables us to develop new algorithms much faster and safer.

Validations

We cooperate with sensor suppliers to validate performance of the sensors and measure KPI for particular testing scenarios.

Path planning

Once the model of outside world is properly acquired, path planning need to ensure the driving is save and lead to desired location.

How does Roboauto work?

You can see a lot of videos showing the functionality of Roboauto.
Check what Roboauto is capable of...

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Technologies

AI - Neural networks

We are using deep neural networks widely to improve self-driving capabilities of Roboauto. Every single drive or simulation helps to teach our neural networks and develop even more to expect any kind of situation which can occur on road.

Probabilistic robotics

Probabilistic robotics pays important role in our algorithms. We widely use Bayesian filters (e.g. particle filter, Kalman filter), probabilistic approach (Markov chains, MCMC, Gibbs, Metropolis-Hastings) and its improvements for simultaneous localization and mapping (SLAM), object based modeling surrounding environment and other problems in this field.

Object detection

We use YOLO and Full Resolution Residual Networks (FRRN) segmentation to recognize the type of tracked objects.

Lane detection

Lane detection is also implemented using deep neural networks and probabilistic algorithms.

Cameras, Radars and LiDARs

We use standard sensors to sense roads and objects near vehicle. We also employ other available sensors, like GPS, IMU, odometry, car CAN bus information, etc.

ROS

Robot Operating System is the basic framework for our solutions.

News from our Twitter

@roboauto

About us

Roboauto is a self-driving startup located at Brno, Czech Republic. Our team has started on 2007 on small car models and build first full scale prototype on 2014. The team consist of 15 sw developers and technicians.

Our achievements

We successfully participated in competitions like Udacity challenge (1st place in Challenge #3) and Didi challenge (7th place from 2 000 participants).

Our HQ

Artin, Mojmírovo nám. 11, Brno, 612 00

Our contact

tym@roboauto.cz


References

Valeo

Sensor data validation, KPI analysis

Skoda

Electric system prototyping