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Found 4 papers, 1 papers with code
Driving in Real Life with Inverse Reinforcement Learning
In this paper, we introduce the first learning-based planner to drive a car in dense, urban traffic using Inverse Reinforcement Learning (IRL).
Importance is in your attention: agent importance prediction for autonomous driving
Trajectory prediction is an important task in autonomous driving.
NuPlan: A closed-loop ML-based planning benchmark for autonomous vehicles
In this work, we propose the world's first closed-loop ML-based planning benchmark for autonomous driving.
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.
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