Uncertainty estimation of the bounding box centroid for autonomous driving

Kaipei Yang; Shida Ye; Yaakov Bar-Shalom; Shawn Hunt

doi:10.1117/12.2618521

6 June 2022 Uncertainty estimation of the bounding box centroid for autonomous driving

Kaipei Yang, Shida Ye, Yaakov Bar-Shalom, Shawn Hunt

Proceedings Volume 12115, Autonomous Systems: Sensors, Processing and Security for Ground, Air, Sea and Space Vehicles and Infrastructure 2022; 1211505 (2022) https://doi.org/10.1117/12.2618521
Event: SPIE Defense + Commercial Sensing, 2022, Orlando, Florida, United States

Abstract

Object detection provides information needed for target tracking and plays a core role in autonomous driving. In this work, we study the uncertainty in the estimation of the centroid (position) of a bounding box of the measurements from an object detected by the sensor of an autonomous vehicle (AV). The estimated centroid uncertainty will be used in object tracking as measurement noise variance, which is not available from the sensor manufacturer, for measurement association and target state estimation. When the (position) uncertainty that captures the noise inherent in the sensor observations is available for each detected point (this can be done using Bayesian deep learning), the bounding box centroid uncertainty is obtained using a Least-Squares estimator (LS). When the uncertainty for each detected point is not available, one can assume a uniform distribution of the clustered points in a single rectangular bounding box. A Maximum Likelihood estimator is used for the bounding box centroid estimation. Experiments using real data are carried out to show the performance of proposed methods for autonomous driving applications. A comparison with the sample mean approach showed the superiority of new algorithm.

Conference Presentation

Citation Download Citation

Kaipei Yang, Shida Ye, Yaakov Bar-Shalom, and Shawn Hunt "Uncertainty estimation of the bounding box centroid for autonomous driving", Proc. SPIE 12115, Autonomous Systems: Sensors, Processing and Security for Ground, Air, Sea and Space Vehicles and Infrastructure 2022, 1211505 (6 June 2022); https://doi.org/10.1117/12.2618521

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