Single Photon Detectors for Automotive LiDAR Applications: State-of-the-Art and Research Challenges
Bibliographic record
Abstract
Due to the fast growth of the autonomous vehicles industry, there is a growing need for distance sensing systems. Among such systems, the light detection and ranging (LiDAR) system is very attractive because of their wide detection range, good spatial resolution, and high precision. For various LiDAR systems, detectors that have single photon counting (SPC) abilities are becoming increasingly popular as they have high sensitivity and can achieve long detection range. However, many publications about LiDAR only focus on the data processing and the algorithms, without sufficient considerations on the sensor-design stage. Only a few studies investigated the sensor's performance in various LiDAR applications. To assist in the future improvement and application of SPC LiDAR, this article provides a new perspective by reviewing the optical sensors and the related circuits architectures in LiDAR systems, focusing on automotive applications. The principles, architectures, emerging techniques, research challenges and future directions are critically discussed.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.007 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".