Bibliographic record
Abstract
Abstract. One of the most critical features of autonomous vehicles is the detection of road active objects such as vehicles and pedestrians. The autonomous vehicles’ navigation planning and manoeuvre decision-making are aided by the detection of such active objects, resulting in safe and efficient navigation. Deep Convolutional Neural Networks (CNNs) have recently advanced to become one of the state-of-the-art ways to solving detection challenges, particularly in the autonomous vehicle area. Deep CNNs typically use a large number of processing layers with a high number of kernels per layer to enable detection of the target classes which also demands the use of powerful hardware units. In this research, we present a tailored lean detection strategy for vehicle detection using radar observations. The proposed method employs a compact set of convolutions, as well as pixel classification and a customized selection of kernels and kernel sizes, to provide an efficient technique that greatly decreases detection burden and enables real-time processing on average processing units. A training dataset is used to train the convolution window sizes and the pixel classifiers. Finally, the pixel classified grids are processed to identify the vehicles' bounding boxes. Experimental data sets have been collected using medium-range radar sensors mounted on top of a vehicle to evaluate the suggested approach, the Intersection over Union (IoU) values of the test scenes’ detections range from 0.51 to 0.78.
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 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.003 | 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".