On Reliable Awareness System for Autonomous River Vessels

Abstract

For autonomous vehicles (AVs), an intelligent awareness system is a fundamental task that provides crucial information on the driving environment. The main techniques for self-driving systems include solving tasks like self-localization, parsing the driving road, and understanding objects, enabling the system to reason and act. However, compared to other AVs, like cars, such robust awareness systems are not yet developed for inland autonomous vessels. Therefore, this paper proposes a system to accurately delimit safe navigation areas by simultaneously locating and mapping the environment. First, we construct the first open-source dataset; the InlandAutoDetect dataset comprises 3,377 images comprehensively labeled for object detection in a fluvial domain with almost 30,000 objects annotated. Second, we analyze and compare the results of nine deep learning perception models adapted to the inland environment in accuracy and run-time speed. The best one, namely Retinanet, is selected, and its different variants are tested by modifying the feature extractor block. Then, we propose the most suitable configuration for inland navigation. Finally, the selected model results are integrated into the complete system to delimit the safe sailing area. The achieved accuracy rate is above 88%, and the run-time speed rate is 0.8 seconds per image. Hence, the performance evaluations show the proposed system’s robustness and effectiveness to give accurate results and fulfill real-time operation requirements.