Search Results for author: Peter Ondruska

Found 14 papers, 5 papers with code

DriverGym: Democratising Reinforcement Learning for Autonomous Driving

no code implementations12 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.

Autonomous Driving OpenAI Gym +2

SafetyNet: Safe planning for real-world self-driving vehicles using machine-learned policies

no code implementations28 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).

Imitation Learning

Urban Driver: Learning to Drive from Real-world Demonstrations Using Policy Gradients

no code implementations27 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.

Autonomy 2.0: Why is self-driving always 5 years away?

no code implementations16 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.

Decision Making Image Generation

What data do we need for training an AV motion planner?

no code implementations26 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.

Imitation Learning Motion Planning

SimNet: Learning Reactive Self-driving Simulations from Real-world Observations

1 code implementation26 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.

Collaborative Augmented Reality on Smartphones via Life-long City-scale Maps

no code implementations10 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.

One Thousand and One Hours: Self-driving Motion Prediction Dataset

3 code implementations25 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.

Autonomous Vehicles Motion Forecasting +2

VALUE: Large Scale Voting-based Automatic Labelling for Urban Environments

no code implementations5 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.

Distributed Voting

Deep Tracking on the Move: Learning to Track the World from a Moving Vehicle using Recurrent Neural Networks

no code implementations29 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.

End-to-End Tracking and Semantic Segmentation Using Recurrent Neural Networks

no code implementations18 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.

Classification General Classification +1

Deep Tracking: Seeing Beyond Seeing Using Recurrent Neural Networks

1 code implementation2 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.

Feature Engineering Object +1

Maximum Entropy Deep Inverse Reinforcement Learning

1 code implementation17 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.

reinforcement-learning Reinforcement Learning (RL)

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