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 the 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 (the 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 the self-driving car is to simulate as much driving as possible to reduce the cost of testing in the 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 the performance of the sensors and measure KPI for particular testing scenarios.
Path planning
Path planning
Once the model of the outside world is appropriately acquired, path planning needs to ensure the driving is safe and lead to the 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 situation which can occur on the road.
Probabilistic robotics
Probabilistic robotics
Probabilistic robotics plays a vital 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
Exciting 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 in 2007 on small car models and build the first full-scale prototype in 2014. The team consists of 15 software developers and technicians.
Our contact
Our references
Škoda logo Valeo logo