Search

Autonomous driving is a complex problem that requires intelligent decision making, and it has recently garnered significant interest due to its potential advantages in convenience and safety. In autonomous driving, conventional path planning to reach a destination is a time-consuming challenge. Therefore, learning-based approaches have been successfully applied to the controller or decision-making aspects of autonomous driving. However, these methods often lack explainability, as passengers cannot discern where the vehicle is headed. Additionally, most experiments primarily focus on highway scenarios, which do not effectively represent road curvature. To address these issues, we propose a reinforcement-learning-based trajectory learning in the Frenet frame (RLTF), which involves learning trajectories in the Frenet frame. Learning trajectories enable the consideration of future states and enhance explainability. We demonstrate that RLTF achieves a 100% success rate in the simulation environment, considering future states on curvy roads with continuous obstacles while overcoming issues associated with the Frenet frame. [ABSTRACT FROM AUTHOR]

Autonomous driving is a complex problem that requires intelligent decision making, and it has recently garnered significant interest due to its potential advantages in convenience and safety. In autonomous driving, conventional path planning to reach a destination is a time-consuming challenge. Therefore, learning-based approaches have been successfully applied to the controller or decision-making aspects of autonomous driving. However, these methods often lack explainability, as passengers cannot discern where the vehicle is headed. Additionally, most experiments primarily focus on highway scenarios, which do not effectively represent road curvature. To address these issues, we propose a reinforcement-learning-based trajectory learning in the Frenet frame (RLTF), which involves learning trajectories in the Frenet frame. Learning trajectories enable the consideration of future states and enhance explainability. We demonstrate that RLTF achieves a 100% success rate in the simulation environment, considering future states on curvy roads with continuous obstacles while overcoming issues associated with the Frenet frame.

The use of augmented reality (AR) continues to increase, particularly in marketing and advertising, where virtual objects are showcased in the AR world, thereby expanding its various applications. In this paper, a method of linking coordinate systems to connect the metaverse with the real world is proposed and a system for correcting and displaying virtual objects in the AR environment is implemented. The proposed method calculates errors to accurately represent virtual objects in AR and presents a method to show these objects without errors. The proposed method was verified through experiments to successfully display virtual objects in AR. To minimize localization errors, semantic segmentation was used to recognize objects and estimate buildings, thereby correcting the device location. An error correction expression is also presented. The proposed system is designed to correct and display virtual objects in AR, with confirmed functionality for location correction.