Vol. 2 No. 2 (2022): Journal of AI-Assisted Scientific Discovery
Articles

Deep Learning-based Behavior Prediction for Enhanced Safety in Autonomous Vehicle Environments

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  • Dr. Małgorzata Pioro-Mianowska
Dr. Małgorzata Pioro-Mianowska
Associate Professor of Computer Science, AGH University of Science and Technology, Poland
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Published 30-12-2022

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How to Cite

[1]
Dr. Małgorzata Pioro-Mianowska, “Deep Learning-based Behavior Prediction for Enhanced Safety in Autonomous Vehicle Environments”, Journal of AI-Assisted Scientific Discovery, vol. 2, no. 2, pp. 121–141, Dec. 2022, Accessed: Nov. 22, 2024. [Online]. Available: https://scienceacadpress.com/index.php/jaasd/article/view/76

Abstract

By doing so, vehicles can proactively communicate the state of the current environment to the vehicle's systems, to the driver, to the surrounding infrastructure, and to other vehicles. Predicting the state of the environment, possibly including the intentions of the actors and objects within it, has the potential for significant advancements in enhancing the safety of transportation systems. Collaborative initiatives among auto manufacturers, governments, and academic institutions coordinate activities related to the development, design, and operation of ADAS.

Traditional safety systems, such as vehicle airbags, have been designed to respond to the intrusion of an external stimulus rather than to predict and prevent an impending event. Furthermore, by the time other advanced driver-assistance system (ADAS) sensors are triggered, it is normally too late to reverse the series of actions that led to the crash. Vehicle safety can benefit from real-time information on the state of its environment outside the vehicle and the intended goals of agents within that environment. Thus, the design and implementation of ADAS in the form of cars communicate predictions about the intentions of the actors like pedestrians, bicycles, motorcyclists, and other drivers.

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