Unstructured off-road environments present new challenges for autonomous driving. This project focuses on developing a perception system for unmanned vehicles in off-road scenarios.
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Creating high-quality, large-scale datasets is crucial for artificial intelligence research. This project aims to develop an automatic labeling system to reduce human resource costs in dataset creation and enhance dataset quality.
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While driving, humans encounter vast amounts of information, selectively focus on key details, and make quick decisions. This project aims to develop driving intelligence that replicates the efficient selection and concentration processes observed in human perception systems.
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LiDAR sensors are resilient to variations in light and weather, providing accurate position measurements that are crucial for object detection algorithms to deliver precise distance information about surrounding objects. Therefore, the use of LiDAR sensors is essential for autonomous driving. This project focuses on developing a deep learning-based object detection algorithm using LiDAR for autonomous vehicles operating in urban environments. As the project lead, I was involved in all stages, from designing the deep learning model to implementing the source code for execution within a ROS environment.
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