Semantic Guided Long Range Stereo Depth Estimation for Safer Autonomous Vehicle Applications

Chuah, Weiqin; Tennakoon, Ruwan; Hoseinnezhad, Reza; Suter, David; Bab–Hadiashar, Alireza

doi:10.1109/tits.2022.3170870

Cited by 10 publications

(1 citation statement)

References 57 publications

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“…15 Integrating the depth-based loss function can be used to increase the accuracy of depth estimation for distant objects and the bias between the multiple classes of objects can be balanced by separating the loss function into the foreground and background portions. 16 In, 17 a motion feedback-based system is proposed for tracking and estimating dynamic vehicle poses by using Interacting Multiple Model-Unscented Kalman Filter (IMM-UKF). Further, a LiDAR and NeuroIV sensors based coupled dynamic settings are introduced to obtain the location of the autonomous vehicle, where the noise is filtered by the intended Otsu method from the captured images.…”

Section: Introductionmentioning

confidence: 99%

Object recognition from on-the-road traffic data

Muhammad

Farooq²,

Zuguo³

et al. 2022

5th International Conference on Informatics Engineering and Information Science (ICIEIS 2022)

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In order to detect the object and inspect the road conditions in real-time, the 2-dimensional (2D) and 3dimensional (3D) data obtained from the onboard sensors, LiDAR and digital cameras are analyzed for object recognition to assist driving. Due to the uncertainties of the dynamic objects, such as pedestrians, animals or vibrated vehicles, extraction of complete and clear objects from LiDARs datasets requires complex post-processing since LiDAR data can be used for scanning at long distances, i.e., 300m, which can alarm the driver timely to take necessary actions. The dynamic and static objects from the LiDARs point clouds can be detected with the teacher-student framework algorithm along with the KITTI dataset. Furthermore, a semi-supervised theory is utilized to improve detection performance.

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Section: Introductionmentioning

confidence: 99%