Dynamic and Static Object Detection Considering Fusion Regions and Point-wise Features
This work addresses the need for more detailed object information to improve autonomous vehicle safety in urban environments, but it is incremental as it builds on existing methods.
The paper tackles the problem of detecting static and dynamic objects for autonomous vehicles by fusing YoloV3 and a Bayesian filter to extract additional characteristics like position, velocity, and heading, achieving performance assessed on benchmark and real-world data compared to another approach.
Object detection is a critical problem for the safe interaction between autonomous vehicles and road users. Deep-learning methodologies allowed the development of object detection approaches with better performance. However, there is still the challenge to obtain more characteristics from the objects detected in real-time. The main reason is that more information from the environment's objects can improve the autonomous vehicle capacity to face different urban situations. This paper proposes a new approach to detect static and dynamic objects in front of an autonomous vehicle. Our approach can also get other characteristics from the objects detected, like their position, velocity, and heading. We develop our proposal fusing results of the environment's interpretations achieved of YoloV3 and a Bayesian filter. To demonstrate our proposal's performance, we asses it through a benchmark dataset and real-world data obtained from an autonomous platform. We compared the results achieved with another approach.