Vision-based hybrid object tracking for autonomous vehicles

Vision-based object detection and tracking play a critical role in the field of autonomous driving as they enable the ego-car to identify potential dangers and detect objects that emerge suddenly. However, the current frequency of out-of-state vision-based object detection and tracking perception is limited to approximately 10-20Hz, which is insufficient for the autonomous driving system to promptly respond to events such as the sudden appearance of objects in close proximity or high-speed scenarios. In contrast, animals possess the ability to promptly avoid dangers despite lacking precise knowledge of an object’s exact distance, owing to their eyes’ high flicker fusion rates. Consequently, this paper introduces a novel vision-based hybrid object tracking technique. The proposed approach incorporates two distinct perception systems: a multi-camera perception system (MCP) and a high-frequency perception system (HFP). These systems operate independently to detect objects, with the MCP offering higher accuracy albeit lower update rates, and the HFP providing lower accuracy but higher update rates. Experimental results demonstrate that our method surpasses traditional perception approaches by achieving faster detection of real-time pop-out motorbikes across various ego-car speeds. Moreover, when both MCP and HFP information are available, our proposed method effectively combines the strengths of both systems to estimate the object’s status.