Compare and Contrast LiDAR and Non-LiDAR Technology in an Autonomous Vehicle: Developing a Safety Framework
Bibliographic record
Abstract
Ensuring safety has always been of utmost importance in vehicular operations, where traditionally, human drivers have been solely responsible for driving. However, with the emergence of advanced technologies, we are now on the brink of a new era with the introduction of Autonomous Vehicles (AVs), in which control over the vehicle gradually shifts to Artificial Intelligence (AI). The safety of these vehicles has raised concerns among the public. In terms of safety, human drivers heavily rely on visual perception. Key elements that contribute to safe driving include situational awareness, vehicle control, reaction capabilities, and anticipation of potential hazards. With the introduction of AVs, these fundamental factors remain unchanged. AVs rely on two primary technologies, namely LiDAR and Non-LiDAR, to perceive their surroundings. This research focuses on three primary aspects. Firstly, it involves the development of an image classification model to assess the safety of AVs. This model determines whether the images captured by LiDAR and Non-LiDAR technologies can be accurately predicted using supervised learning. An algorithm is employed to identify the input images obtained from both LiDAR and Non-LiDAR technologies. The results demonstrate that the model achieved a high accuracy rate of 94.63% in predicting the images. Secondly, a Safety Framework is established to facilitate the subsequent proposal for Experimental Research, which is the third aspect. The Safety Framework incorporates the application of LiDAR and Non-LiDAR technologies mounted on a vehicle that is operated in diverse weather conditions by both a human driver and an autonomous system. The Scenario tables, which are currently in a blank state, will be populated upon completion of the Experimental Research. The Experimental Research aims to compare and contrast the performance of LiDAR and Non-LiDAR technologies on a vehicle driven by a human operator versus an Autonomous Vehicle. The findings of this research will be documented in the Scenario table outlined within this study, ultimately shedding light on the safety implications of implementing LiDAR and Non-LiDAR technologies within an AV context.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".