Search Results for author: Hugo Grimmett

Found 7 papers, 0 papers with code

Quantity over Quality: Training an AV Motion Planner with Large Scale Commodity Vision Data

no code implementations3 Mar 2022 Lukas Platinsky, Tayyab Naseer, Hui Chen, Ben Haines, Haoyue Zhu, Hugo Grimmett, Luca Del Pero

This motivates the use of commodity sensors like cameras for data collection, which are an order of magnitude cheaper than HD sensor suites, but offer lower fidelity.

Motion Planning

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

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

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

no code implementations26 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.

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

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.

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

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