The ideal vulnerable road user – a study of parameters affecting VRU detection
Bibliographic record
Abstract
OBJECTIVE: Advanced Driving Assistance Systems (ADAS) have the potential to reduce occurrences and severity of collisions with Vulnerable Road Users (VRUs). However, the nearly infinite number of possible VRU visual appearances (e.g., size, clothing, accessories) represent a technical challenge as systems need to correctly detect and identify VRUs to take adequate mitigation measures. The aim of this study was to determine, through track testing, which parameters affect systems' capabilities in detecting pedestrians. METHODS: The standardized articulated adult male pedestrian (EPTa) and seven-year-old articulated child pedestrian (EPTc) targets were used as the control group. Evaluations on the track followed the Euro NCAP AEB-VRU test protocols, and derivatives thereof. An iterative test approach was used to benchmark the detection capabilities of systems with variations in target configuration and environmental conditions against the control group (baseline condition). Over 1,000 track tests using 24 configurations and 13 vehicles (model years 2019-2021) were conducted. The environmental conditions included nighttime and snow-covered roads. Pedestrians were dressed in winter clothing and/or equipped with accessories, including a hat, jackets of different colors, backpack, umbrella, and a scooter. Other scenarios involved parked vehicles as obstructions or using multiple pedestrian targets (to simulate a parent crossing the road with child or a crowd waiting at an intersection) to challenge the vehicles with realistic urban-like scenarios. RESULTS: This study illustrates how the variation of parameters outside the baseline condition can affect a vehicles' safety performance. Weather conditions and urban-like scenarios on the test track affected some systems more than others. The validity of these findings is however limited by the small vehicle sample size and number of tests performed per scenario. CONCLUSIONS: In Canada, vehicles are exposed to less-than-ideal road conditions and a wide range of pedestrian profiles. The vehicles tested demonstrated various levels of performance and capabilities when mitigating collisions with pedestrians. The research illustrates the safety risks associated with weather and types of VRUs.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".