Self-driving
technologies
for automotive industry
Roboauto slide
Roboauto is first Self-driving car in the Czech Republic
More information about Roboauto
Our team with self-driving guru
Our team with self-driving guru Sebastian Thrun in San Francisco receiving 1st prize in Udacity challenge
Team Roboauto
Our achievements
Audacity & Didi logo
We successfully participated in competitions like
Udacity challenge (1st place in Challenge #3) and Didi challenge (7th place from 2 000 participants).
Our Expertise
Sensor data fusion
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
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
Validations
We cooperate with sensor suppliers to validate performance of the sensors and measure KPI for particular testing scenarios.
Path planning
Path planning
OOnce 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...
All videos on our Youtube channel
Technologies
Object detection
Object detection
We use YOLO and Full Resolution Residual Networks (FRRN) segmentation to recognize the type of tracked objects.
ROS
ROS
Robot Operating System is the basic framework for our solutions.
Cameras, Radars and LiDARs
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.
Lane detection
Lane detection
Lane detection is also implemented using deep neural networks and probabilistic algorithms.
AI - Neural networks
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
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.
News from our Twitter
Interesting news from the world of self-steering car @roboauto
More tweets on our Twitter
About Roboauto
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 contact
Our references
Škoda logo Valeo logo