1 citations · 2 across the 5 of their papers we have counts for
6 papers
SafetyNet: Safe planning for real-world self-driving vehicles using machine-learned policies
Matt Vitelli, Yan Chang, Yawei Ye +7
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 environmen…
Autonomy 2.0: Why is self-driving always 5 years away?
Ashesh Jain, Luca Del Pero, Hugo Grimmett +1
Despite the numerous successes of machine learning over the past decade (image recognition, decision-making, NLP, image synthesis), self-driving technology has not yet followed the…
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…
Collaborative Augmented Reality on Smartphones via Life-long City-scale Maps
Lukas Platinsky, Michal Szabados, Filip Hlasek +6
In this paper we present the first published end-to-end production computer-vision system for powering city-scale shared augmented reality experiences on mobile devices. In doing s…
VALUE: Large Scale Voting-based Automatic Labelling for Urban Environments
Giacomo Dabisias, Emanuele Ruffaldi, Hugo Grimmett +1
This paper presents a simple and robust method for the automatic localisation of static 3D objects in large-scale urban environments. By exploiting the potential to merge a large v…