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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. This paper explores AV navigation in crowded, unsignalized intersections. We compare the performance of different deep reinforcement learning methods trained on our reward function and state representation. The performance of these methods and a standard rule-based approach were evaluated in two ways, first at the unsignalized intersection on which the methods were trained, and secondly at an unknown unsignalized intersection with a different topology. For both scenarios, the rule-based method achieves less than 40% collision-free episodes, whereas our methods result in a performance of approximately 100%. Of the three methods used, DDQN/PER outperforms the other two methods while it also shows the smallest average intersection crossing time, the greatest average speed, and the greatest distance from the closest pedestrian.
We propose a safe DRL approach for autonomous vehicle (AV) navigation through crowds of pedestrians while making a left turn at an unsignalized intersection. Our method uses two long-short term memory (LSTM) models that are trained to generate the pe
Decision-making module enables autonomous vehicles to reach appropriate maneuvers in the complex urban environments, especially the intersection situations. This work proposes a deep reinforcement learning (DRL) based left-turn decision-making framew
This paper describes a novel method for allowing an autonomous ground vehicle to predict the intent of other agents in an urban environment. This method, termed the cognitive driving framework, models both the intent and the potentially false beliefs
We study a novel principle for safe and efficient collision avoidance that adopts a mathematically elegant and general framework abstracting as much as possible from the controlled vehicles dynamics and of its environment. Vehicle dynamics is charact
For a foreseeable future, autonomous vehicles (AVs) will operate in traffic together with human-driven vehicles. Their planning and control systems need extensive testing, including early-stage testing in simulations where the interactions among auto