Robust and Accurate Object Velocity Detection by Stereo Camera for Autonomous Driving
This work aims to improve the accuracy and robustness of object velocity detection for autonomous vehicles, potentially reducing reliance on radar systems.
This paper addresses the challenge of robust and accurate object velocity detection using stereo cameras for autonomous driving. The authors developed a method that leverages a large-scale dataset, achieving improved velocity detection accuracy in severe environments.
Although the number of camera-based sensors mounted on vehicles has recently increased dramatically, robust and accurate object velocity detection is difficult. Additionally, it is still common to use radar as a fusion system. We have developed a method to accurately detect the velocity of object using a camera, based on a large-scale dataset collected over 20 years by the automotive manufacturer, SUBARU. The proposed method consists of three methods: an High Dynamic Range (HDR) detection method that fuses multiple stereo disparity images, a fusion method that combines the results of monocular and stereo recognitions, and a new velocity calculation method. The evaluation was carried out using measurement devices and a test course that can quantitatively reproduce severe environment by mounting the developed stereo camera on an actual vehicle.