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11/12/2021
DriverGym: Democratising Reinforcement Learning for Autonomous Driving
Despite promising progress in reinforcement learning (RL), developing al...
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09/28/2021
SafetyNet: Safe planning for real-world self-driving vehicles using machine-learned policies
In this paper we present the first safe system for full control of self-...
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09/27/2021
Urban Driver: Learning to Drive from Real-world Demonstrations Using Policy Gradients
In this work we are the first to present an offline policy gradient meth...
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07/16/2021
Autonomy 2.0: Why is self-driving always 5 years away?
Despite the numerous successes of machine learning over the past decade ...
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05/26/2021
What data do we need for training an AV motion planner?
We investigate what grade of sensor data is required for training an imi...
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05/26/2021
SimNet: Learning Reactive Self-driving Simulations from Real-world Observations
In this work, we present a simple end-to-end trainable machine learning ...
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11/10/2020
Collaborative Augmented Reality on Smartphones via Life-long City-scale Maps
In this paper we present the first published end-to-end production compu...
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06/25/2020
One Thousand and One Hours: Self-driving Motion Prediction Dataset
We present the largest self-driving dataset for motion prediction to dat...
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06/05/2020
VALUE: Large Scale Voting-based Automatic Labelling for Urban Environments
This paper presents a simple and robust method for the automatic localis...
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09/29/2016
Deep Tracking on the Move: Learning to Track the World from a Moving Vehicle using Recurrent Neural Networks
This paper presents an end-to-end approach for tracking static and dynam...
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04/18/2016
End-to-End Tracking and Semantic Segmentation Using Recurrent Neural Networks
In this work we present a novel end-to-end framework for tracking and cl...
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02/02/2016
Deep Tracking: Seeing Beyond Seeing Using Recurrent Neural Networks
This paper presents to the best of our knowledge the first end-to-end ob...
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06/24/2015