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Found 14 papers, 5 papers with code
DriverGym: Democratising Reinforcement Learning for Autonomous Driving
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.
SafetyNet: Safe planning for real-world self-driving vehicles using machine-learned policies
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).
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 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?
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.
What data do we need for training an AV motion planner?
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.
SimNet: Learning Reactive Self-driving Simulations from Real-world Observations
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
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
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.
VALUE: Large Scale Voting-based Automatic Labelling for Urban Environments
This paper presents a simple and robust method for the automatic localisation of static 3D objects in large-scale urban environments.
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 dynamic objects for an autonomous vehicle driving through crowded urban environments.
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 classifying a robot's surroundings in complex, dynamic and only partially observable real-world environments.
Deep Tracking: Seeing Beyond Seeing Using Recurrent Neural Networks
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.
Maximum Entropy Deep Inverse Reinforcement Learning
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.
Ask Me Anything: Dynamic Memory Networks for Natural Language Processing
Most tasks in natural language processing can be cast into question answering (QA) problems over language input.
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