End-to-end, real time and robust behavioral prediction module with ROS for autonomous vehicles

Year 2024, , 1 - 25, 14.06.2024

Tolga Kayın , Çağatay Berke Erdaş

https://doi.org/10.33769/aupse.1292652

Abstract

In the world where urbanization and population density are increasing, transportation methods are also diversifying and the use of unmanned vehicles is becoming widespread. In order for unmanned vehicles to perform their tasks autonomously, they need to be able to perceive their own position, the environment and predict the possible movements/routes of environmental factors, similar to living things. In autonomous vehicles, it is extremely important for the safety of the vehicle and the surrounding factors to be able to predict the future position of the objects around it with high performance so that the vehicle can plan correctly. Due to the stated reasons, the behavioral prediction module is a very important component for autonomous vehicles, especially in moving environments. In this study, fast and successful robotic behavioral prediction module has been developed to enable the autonomous vehicle to plan more safely and successfully.

Keywords

Behavioral prediction, trajectory prediction, autonomous vehicles, ROS

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