How Machine Learning is turning the Automotive Industry upside down (EN)

How Machine Learning is turning the Automotive Industry upside down (EN)

14:45 - 15:15 | festival.stage | Machine Learning

The automotive industry has mobilized the global economy for decades. German automobile manufacturers (OEMs) alone employ more than 1 million people worldwide and generate sales of more than USD 500 billion.
Since a Google + Stanford team won the Darpa Self-Driving Vehicles Challenge 2006 with the help of machine learning, among other things, the industry has been undergoing rapid change. Machine learning opens up brand-new business models, from autonomous driving to smart production to personal assistance in the car.
However, the use of machine learning requires a different infrastructure than that found in OEMs. Technology-first companies like Waymo or Tesla threaten to overtake established OEMs with billion-dollar market capitalization. OEMs fear being degraded to pure hardware suppliers.
Autonomous vehicles produce terabytes of data every day. This data can be immensely valuable in developing machine learning-driven functions. OEMs encounter the following problems:
1. data storage
2. data transfer
3. Expense of Sensors
4. Training Data Acquisition
5. Verification of Neural Networks
The automotive industry must develop from a mechanical engineering to a software industry. It needs support in this process.
Finally, I would like to encourage the audience to research these problems and to apply to car manufacturers to work on solutions.

 

Jan Zawadzki, Project Lead Data Science, Carmeq GmbH