Roadside Fisheye Vision for Cooperative Perception in V2I-Assisted Automated Driving

Engineering notes

The proposed algorithm achieves remarkable localization accuracy, with a mean absolute error of 31 cm for pedestrians and 76 cm for cars, even at intersections with sloped roads.

Chinese explanation / 中文解读

中文解读待补充：本站会优先为端到端自动驾驶、BEV感知、3D目标检测、轨迹预测、路径规划、LiDAR感知等高价值论文补充中文说明。

Original abstract

Precise road object perception and localization are crucial for autonomous vehicle navigation, yet onboard sensors occasionally encounter challenges with occlusions and blind spots, particularly at intersections. One potential solution is to use stationary sensors at intersections, which can enhance the perceptual capabilities of connected automated vehicles (CAVs) by leveraging vehicle-to-infrastructure (V2I) communication. In this context, this paper introduces an innovative perception and localization algorithm utilizing a stationary overhead fisheye camera installed at intersections. Addressing challenges inherent in overhead fisheye perspectives, a fine-tuning technique is employed to optimize detection performance for overhead traffic scenes. A novel camera calibration method is introduced to minimize localization inaccuracies derived from variations in road surface elevation. Road object dimensions are estimated for accurate localization and mapping in the birdeye view (BEV) map by fitting predefined 3D boxes in the real-world coordinate system. This is achieved by tracking and estimating object heading using the extended Kalman filter with the constant turn rate and velocity (CTRV) model. The proposed algorithm achieves remarkable localization accuracy, with a mean absolute error of 31 cm for pedestrians and 76 cm for cars, even at intersections with sloped roads. Experimental evaluations underscore the algorithm’s practical potential as a component for V2I-based cooperative perception and road safety warning systems.

Links and sources

• Official / arXiv page

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