1 citations · 1 across the 2 of their papers we have counts for
4 papers
What data do we need for training an AV motion planner?
Long Chen, Lukas Platinsky, Stefanie Speichert +6
We investigate what grade of sensor data is required for training an imitation-learning-based AV planner on human expert demonstration. Machine-learned planners are very hungry for…
SimNet: Learning Reactive Self-driving Simulations from Real-world Observations
Luca Bergamini, Yawei Ye, Oliver Scheel +6
In this work, we present a simple end-to-end trainable machine learning system capable of realistically simulating driving experiences. This can be used for the verification of sel…
CARLA Real Traffic Scenarios -- novel training ground and benchmark for autonomous driving
Błażej Osiński, Piotr Miłoś, Adam Jakubowski +6
This work introduces interactive traffic scenarios in the CARLA simulator, which are based on real-world traffic. We concentrate on tactical tasks lasting several seconds, which ar…
Learning to Run challenge solutions: Adapting reinforcement learning methods for neuromusculoskeletal environments
Łukasz Kidziński, Sharada Prasanna Mohanty, Carmichael Ong +26
In the NIPS 2017 Learning to Run challenge, participants were tasked with building a controller for a musculoskeletal model to make it run as fast as possible through an obstacle c…