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The unmanned vehicle will calculate reckless drivers

04.04.2022

The University of Maryland has developed a new method for simulating the behavior of drivers on the road to train autonomous vehicles.

A traffic simulation method developed by researchers at the University of Maryland is based on classifying the behavior of other road users. This model, which the authors called CMetric, analyzes the trajectories of other drivers and pedestrians. Based on the analysis and data obtained using computer vision, the algorithm can predict the possible actions of road users.

“Using CMetric, our simulator can generate virtual motorists with different driving styles to pretrain self-driving cars,” says Angelos Mavrogiannis, one of the developers of the model. “Modeling heterogeneous driving behavior is a core element of our work. We use a deep reinforcement learning model, based on DQN (Deep Q-Network)."

The developers note that in recent years, many companies have been working on the creation of safe and reliable unmanned vehicles. However, for universal 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 autonomous vehicles, current 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 others traffic participants (cars, buses, trucks, bicycles, pedestrians) and use the received 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.

Accurate detection of these behaviors can be very useful for autonomous vehicles, especially at critical moments (for example, when changing lanes or entering a highway). Understanding the actions of other drivers allows AI to adapt its trajectory accordingly, to take safety measures.

"Autonomous navigation systems are usually trained before field testing," said Rohan Chandra, co-author of the development. the navigation system can be trained to handle the complex behavior of drivers in an urban environment."

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