Roadside Fisheye Vision for Cooperative Perception in V2I-Assisted Automated Driving
Bibliographic record
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 algorithms practical potential as a component for V2I-based cooperative perception and road safety warning systems.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".