205 citations · 213 across the 6 of their papers we have counts for
8 papers
DriverGym: Democratising Reinforcement Learning for Autonomous Driving
Parth Kothari, Christian Perone, Luca Bergamini +2
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…
Urban Driver: Learning to Drive from Real-world Demonstrations Using Policy Gradients
Oliver Scheel, Luca Bergamini, Maciej Wołczyk +2
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 demonstration…
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…
Robust Re-Identification by Multiple Views Knowledge Distillation
Angelo Porrello, Luca Bergamini, Simone Calderara
To achieve robustness in Re-Identification, standard methods leverage tracking information in a Video-To-Video fashion. However, these solutions face a large drop in performance fo…
Future Urban Scenes Generation Through Vehicles Synthesis
Alessandro Simoni, Luca Bergamini, Andrea Palazzi +2
In this work we propose a deep learning pipeline to predict the visual future appearance of an urban scene. Despite recent advances, generating the entire scene in an end-to-end fa…
One Thousand and One Hours: Self-driving Motion Prediction Dataset
John Houston, Guido Zuidhof, Luca Bergamini +6
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. This w…
Warp and Learn: Novel Views Generation for Vehicles and Other Objects
Andrea Palazzi, Luca Bergamini, Simone Calderara +1
In this work we introduce a new self-supervised, semi-parametric approach for synthesizing novel views of a vehicle starting from a single monocular image. Differently from paramet…
Multi-views Embedding for Cattle Re-identification
Luca Bergamini, Angelo Porrello, Andrea Capobianco Dondona +4
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 N…