Abstract

This workshop aims to foster communication around current paradigms for autonomous vehicles. While we see sufficient maturity in all parts of the classical self-driving stack, their combined performance leaves room for improvement. In the past years, ML-first solutions have been showing promising results, not only in simulation or simplified scenarios, but also in real-world, complex environments. We want to open up the discussion on the challenges that need to be solved in order to enable the next generation of AVs and to encourage new ideas regarding their scalability and safety.