Evaluation of sensors impact on information redundancy in cooperative perception system
Bibliographic record
Abstract
Cooperative perception is a widely adopted approach to cope with occlusion and non-line-of-sight limitations of the vehicles' local sensors. It enables vehicles to increase their awareness of the environment by sharing their local perception information with others using Vehicle-to-Everything (V2X) technology, thus, avoiding potential accidents. This paper studies the sensor errors and properties and reflects their impact on redundant information shared over communication while arguing for the cases where redundant information could be accepted. Specifically, three perspectives are evaluated: perception issues due to object detection errors, localization errors due to inaccuracy in the onboard navigation system (NS) and the effect of different perception Field of View (FoV). The system is implemented and evaluated using Simulation of Urban MObility (SUMO) traffic simulator and a centralized basestation that coordinates the CV2X communication. Results confirm that 63% of the missed vehicles due to detection error can be retrieved using the suggested Estimated Error Detection (EED) approach. The drawback is increasing the number of duplicate information sent to the receiver. While this exhausts the communication resources, it is still useful for cases where detection is hindered (e.g., by weather conditions). Moreover, our experiments show that the system becomes less reliable when the positioning error is above 1 meter. Lastly, we analyze the effect of the Field of View (FoV) on the centralized basestation objective value, highlighting the importance of 360° perception although it increases duplicate information (51%), pointing to further research required for mitigating duplicate information.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".