Search Results for author: Luca Bergamini

Found 8 papers, 4 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

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.

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.

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

Warp and Learn: Novel Views Generation for Vehicles and Other Objects

1 code implementation24 Jul 2019 Andrea Palazzi, Luca Bergamini, Simone Calderara, Rita Cucchiara

An Image Completion Network (ICN) is then trained to generate a realistic image starting from this geometric guidance.

3D Object Detection Image Generation

Multi-views Embedding for Cattle Re-identification

no code implementations13 Feb 2019 Luca Bergamini, Angelo Porrello, Andrea Capobianco Dondona, Ercole Del Negro, Mauro Mattioli, Nicola D'Alterio, Simone Calderara

People re-identification task has seen enormous improvements in the latest years, mainly due to the development of better image features extraction from deep Convolutional Neural Networks (CNN) and the availability of large datasets.

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