Compare and Contrast LiDAR and Non-LiDAR Technology in an Autonomous Vehicle: Developing a Safety Framework
Bibliographic record
Abstract
Abstract Safety has always been paramount in every vehicle we drive, and for years a driver has been synonymous with driving. However, with the advent of technology, we are now on the verge of having an Autonomous Vehicle wherein the control of the vehicle is gradually transferred to Artificial Intelligence. There is public clamour regarding the safety of such vehicles. Regarding safety, a human driver relies heavily on what the driver can see. Some factors that make driving safe are seeing the surroundings, controlling the vehicle, being able to react, and perceiving what will happen. With Autonomous Vehicles, these factors did not change. These vehicles rely on what they see using two technologies; LiDAR and Non-LiDAR. This study developed an Image Processing Model that takes input from the two technologies using Supervised Learning to make the Autonomous Vehicle see and be aware of its surroundings. The study also developed a Safety Framework measuring the ability of the two technologies to gather images fed into the Image Processing Model for comparing and contrasting. The study also proposed Experimental Research to create a baseline on the safety of an Autonomous Vehicle compared with a human driver in a controlled environment. The result of the proposed research can be a basis for trusting the Autonomous Vehicle if it performs at par with the performance of a human driver in the simulation developed in this study.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".