On The Performance of Perception Systems of Autonomous Vehicles
Bibliographic record
Abstract
The first stage in the pipeline of self-driving cars is a system that enables the vehicle to understand its surroundings which becomes the base for every decision it takes and every maneuver it performs. Therefore, it is of high importance to design the perceptual system in a way that renders a scene and extracts accurate information about the present entities with little latency. Furthermore, driving is a complex task that should be safely performed at any time, especially, under different weather conditions which are not necessarily normal. Thus, perception models should be robust to different situations and weather conditions. In this paper, we evaluate the performance of various perception models designed to do object detection task. We critically analyze the structure of each model to identify the advantages and drawbacks. Our evaluation is done based on the NuScenes dataset which is an extensive dataset that covers different times of the day and weather conditions. Related metrics such as mAP, NDS, and learning curves are used to compare the performance of respective models as well as how robust they are to situations that are not considered normal.
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".