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Moritz Niendorf

2 accepted papers

2023

Safe Real-World Autonomous Driving by Learning to Predict and Plan with a Mixture of Experts

ICRA 2023poster

The goal of autonomous vehicles is to navigate public roads safely and comfortably. To enforce safety, traditional planning approaches rely on handcrafted rules to generate trajectories. Machine learning-based systems, on the other hand, scale with data and are able to learn more complex behaviors.…

Cited by 45SourceScholar
2022

SafetyNet: Safe Planning for Real-World Self-Driving Vehicles Using Machine-Learned Policies

ICRA 2022poster

In this paper we present the first safe system for full control of self-driving vehicles trained from human demonstrations and deployed in challenging, real-world, urban environments. Current industry-standard solutions use rule-based systems for planning. Although they perform reasonably well in co…

Cited by 82SourceScholar