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Found 4 papers, 0 papers with code
Safe Deep Q-Network for Autonomous Vehicles at Unsignalized Intersection
We propose a safe DRL approach for autonomous vehicle (AV) navigation through crowds of pedestrians while making a left turn at an unsignalized intersection.
Don't Get Yourself into Trouble! Risk-aware Decision-Making for Autonomous Vehicles
Risk is traditionally described as the expected likelihood of an undesirable outcome, such as collisions for autonomous vehicles.
Pedestrian Collision Avoidance for Autonomous Vehicles at Unsignalized Intersection Using Deep Q-Network
Prior research has extensively explored Autonomous Vehicle (AV) navigation in the presence of other vehicles, however, navigation among pedestrians, who are the most vulnerable element in urban environments, has been less examined.
The Pedestrian Patterns Dataset
The purpose of the dataset is to capture the patterns of social and pedestrian behavior along the traversed routes at different times and to eventually use this information to make predictions about the risk associated with autonomously traveling along different routes.
