A road simulation method developed by researchers at the University of Maryland is based on
“With CMetric, our simulator cangenerate virtual motorists with different driving styles for preliminary training of unmanned vehicles,” says Angelos Mavrogiannis, one of the developers of the model. — Simulation of heterogeneous driving behavior is the main element of our work. We use a deep reinforcement learning model based on DQN (Deep Q-Network).”
Developers note that in recent yearsmany companies are working to create safe and reliable unmanned vehicles. However, for widespread use, such machines must be able to move on a variety of roads, not collide with other vehicles, pedestrians, bicycles, animals or other obstacles.
“Despite the great interest in autonomoustransport, modern artificial intelligence methods do not take into account the behavior of human drivers or other drones on the road, points out Professor Dinesh Manocha, co-author of the work. “The goal of our work is to create reliable technologies that will detect and classify the behavior of other road users (cars, buses, trucks, bicycles, pedestrians) and use the obtained data when driving.”
Behavior behind the wheel, according to the developers,can be divided into two main categories: conservative and aggressive driving. Conservative drivers are more careful and attentive, while aggressive drivers are prone to dangerous maneuvers and sudden movements.
Accurately detecting these behavior patterns canbe very useful for autonomous vehicles, especially during critical moments (such as when changing lanes or entering a highway). Understanding the actions of other drivers allows the AI to adapt its trajectory accordingly and take safety measures.
"Autonomous navigation systems usuallyare trained before field trials,” said Rohan Chandra, co-author of the development. “In our article, we present a new behavior-based simulator that can simulate a large number of different behaviors seen in real traffic scenarios. This means that the underlying navigation system can be trained to handle the complex behavior of drivers in an urban environment.”
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