A project report showing a DRL-plus-control-barrier pipeline and a real-vehicle-in-virtual-environment test setup for bicyclist collision avoidance, with only a pure-pursuit controller actually tested on the real vehicle.
End-to-End Race Driving with Deep Reinforcement Learning
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abstract
We present research using the latest reinforcement learning algorithm for end-to-end driving without any mediated perception (object recognition, scene understanding). The newly proposed reward and learning strategies lead together to faster convergence and more robust driving using only RGB image from a forward facing camera. An Asynchronous Actor Critic (A3C) framework is used to learn the car control in a physically and graphically realistic rally game, with the agents evolving simultaneously on tracks with a variety of road structures (turns, hills), graphics (seasons, location) and physics (road adherence). A thorough evaluation is conducted and generalization is proven on unseen tracks and using legal speed limits. Open loop tests on real sequences of images show some domain adaption capability of our method.
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Vehicle-in-Virtual-Environment (VVE) Method for Developing and Evaluating VRU Safety of Connected and Autonomous Driving with Focus on Bicyclist Safety
A project report showing a DRL-plus-control-barrier pipeline and a real-vehicle-in-virtual-environment test setup for bicyclist collision avoidance, with only a pure-pursuit controller actually tested on the real vehicle.