no code implementations • 12 Nov 2021 • Parth Kothari, Christian Perone, Luca Bergamini, Alexandre Alahi, Peter Ondruska
Despite promising progress in reinforcement learning (RL), developing algorithms for autonomous driving (AD) remains challenging: one of the critical issues being the absence of an open-source platform capable of training and effectively validating the RL policies on real-world data.
no code implementations • 28 Sep 2021 • Matt Vitelli, Yan Chang, Yawei Ye, Maciej Wołczyk, Błażej Osiński, Moritz Niendorf, Hugo Grimmett, Qiangui Huang, Ashesh Jain, Peter Ondruska
To combat this, our approach uses a simple yet effective rule-based fallback layer that performs sanity checks on an ML planner's decisions (e. g. avoiding collision, assuring physical feasibility).
no code implementations • 27 Sep 2021 • Oliver Scheel, Luca Bergamini, Maciej Wołczyk, Błażej Osiński, Peter Ondruska
In this work we are the first to present an offline policy gradient method for learning imitative policies for complex urban driving from a large corpus of real-world demonstrations.
no code implementations • 16 Jul 2021 • Ashesh Jain, Luca Del Pero, Hugo Grimmett, Peter Ondruska
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 same trend.
no code implementations • 26 May 2021 • Luca Bergamini, Yawei Ye, Oliver Scheel, Long Chen, Chih Hu, Luca Del Pero, Blazej Osinski, Hugo Grimmett, Peter Ondruska
We train our system directly from 1, 000 hours of driving logs and measure both realism, reactivity of the simulation as the two key properties of the simulation.
no code implementations • 26 May 2021 • Long Chen, Lukas Platinsky, Stefanie Speichert, Blazej Osinski, Oliver Scheel, Yawei Ye, Hugo Grimmett, Luca Del Pero, Peter Ondruska
If cheaper sensors could be used for collection instead, data availability would go up, which is crucial in a field where data volume requirements are large and availability is small.
no code implementations • 10 Nov 2020 • Lukas Platinsky, Michal Szabados, Filip Hlasek, Ross Hemsley, Luca Del Pero, Andrej Pancik, Bryan Baum, Hugo Grimmett, Peter Ondruska
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.
3 code implementations • 25 Jun 2020 • John Houston, Guido Zuidhof, Luca Bergamini, Yawei Ye, Long Chen, Ashesh Jain, Sammy Omari, Vladimir Iglovikov, Peter Ondruska
Motivated by the impact of large-scale datasets on ML systems we present the largest self-driving dataset for motion prediction to date, containing over 1, 000 hours of data.
no code implementations • 5 Jun 2020 • Giacomo Dabisias, Emanuele Ruffaldi, Hugo Grimmett, Peter Ondruska
This paper presents a simple and robust method for the automatic localisation of static 3D objects in large-scale urban environments.
no code implementations • 29 Sep 2016 • Julie Dequaire, Dushyant Rao, Peter Ondruska, Dominic Wang, Ingmar Posner
This paper presents an end-to-end approach for tracking static and dynamic objects for an autonomous vehicle driving through crowded urban environments.
no code implementations • 18 Apr 2016 • Peter Ondruska, Julie Dequaire, Dominic Zeng Wang, Ingmar Posner
In this work we present a novel end-to-end framework for tracking and classifying a robot's surroundings in complex, dynamic and only partially observable real-world environments.
no code implementations • 2 Feb 2016 • Peter Ondruska, Ingmar Posner
This paper presents to the best of our knowledge the first end-to-end object tracking approach which directly maps from raw sensor input to object tracks in sensor space without requiring any feature engineering or system identification in the form of plant or sensor models.
1 code implementation • 17 Jul 2015 • Markus Wulfmeier, Peter Ondruska, Ingmar Posner
This paper presents a general framework for exploiting the representational capacity of neural networks to approximate complex, nonlinear reward functions in the context of solving the inverse reinforcement learning (IRL) problem.
10 code implementations • 24 Jun 2015 • Ankit Kumar, Ozan .Irsoy, Peter Ondruska, Mohit Iyyer, James Bradbury, Ishaan Gulrajani, Victor Zhong, Romain Paulus, Richard Socher
Most tasks in natural language processing can be cast into question answering (QA) problems over language input.
Ranked #61 on Sentiment Analysis on SST-2 Binary classification