22,128 research outputs found
Automated Lane Change Decision Making using Deep Reinforcement Learning in Dynamic and Uncertain Highway Environment
Autonomous lane changing is a critical feature for advanced autonomous
driving systems, that involves several challenges such as uncertainty in other
driver's behaviors and the trade-off between safety and agility. In this work,
we develop a novel simulation environment that emulates these challenges and
train a deep reinforcement learning agent that yields consistent performance in
a variety of dynamic and uncertain traffic scenarios. Results show that the
proposed data-driven approach performs significantly better in noisy
environments compared to methods that rely solely on heuristics.Comment: Accepted to IEEE Intelligent Transportation Systems Conference - ITSC
201
Efficient Learning of Urban Driving Policies Using Bird's-Eye-View State Representations
Autonomous driving involves complex decision-making in highly interactive
environments, requiring thoughtful negotiation with other traffic participants.
While reinforcement learning provides a way to learn such interaction behavior,
efficient learning critically depends on scalable state representations.
Contrary to imitation learning methods, high-dimensional state representations
still constitute a major bottleneck for deep reinforcement learning methods in
autonomous driving. In this paper, we study the challenges of constructing
bird's-eye-view representations for autonomous driving and propose a recurrent
learning architecture for long-horizon driving. Our PPO-based approach, called
RecurrDriveNet, is demonstrated on a simulated autonomous driving task in
CARLA, where it outperforms traditional frame-stacking methods while only
requiring one million experiences for efficient training. RecurrDriveNet causes
less than one infraction per driven kilometer by interacting safely with other
road users.Comment: IEEE International Conference on Intelligent Transportation Systems
202
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