Object Detection for Connected and Autonomous Vehicles using CNN with Attention Mechanism
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This paper addresses object detection and scene perception for connected and autonomous vehicles. Road object detection at high accuracy and fast inference speed is a challenging task for safe autonomous driving as false positives arising from false localization can lead to fatal outcomes. The paper proposes a convolutional neural network (CNN) to recognize images to enhance intelligent adaptive behavior in autonomous vehicles by correctly classifying, detecting, and segmenting spatially distributed objects in the driving environment. By focusing on specific regions of an image, the most significant region of the image is learned by appending a CNN with probabilistic attention mechanism aided with transformers. The proposed approach is analyzed for detection efficiency and accuracy to distinguish different objects to make appropriate driving decisions. The proposed method is validated on the publicly available Berkeley deep drive (BDD) dataset and shows an accuracy comparable to other state-of-the-art deep learning algorithms to make driving decisions based on real-time assessment of the temporal states encountered while navigating the driving environment. The proposed model performance is evaluated using mean average precision (mAP) and speed-accuracy trade-off.
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.
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 | 0.000 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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 it