Enhancing Autonomous Vehicle Navigation with Real-Time Object Detection Using Convolutional Neural Networks
Bibliographic record
Abstract
Autonomous vehicles’ (AVs) capacity to identify and categorise things in real-time is crucial to their development for the sake of efficient and safe navigation. Because of its exceptional capability to learn spatial feature hierarchies automatically, Convolutional Neural Networks (CNNs) have become an indispensable tool for object identification in intricate visual recognition problems. This research offers a thorough analysis of convolutional neural networks (CNNs) used for autonomous vehicle object identification in real-time, with an emphasis on improving detection accuracy and speed. We compare the processing speed, detection accuracy, and computational efficiency of several CNN designs, such as YOLO (You Only Look Once), SSD (Single Shot Multibox Detector), and Faster R-CNN. In addition, to address the demanding realtime needs of autonomous driving systems, we suggest CNN design improvements that use hardware acceleration methods like GPU and TPU optimisations. The experimental findings show that our suggested method significantly reduces detection latency while maintaining accuracy, which makes it suitable for use in real-world AV settings. Autonomous vehicle technology advances thanks to this study’s results, which aid in building scalable and reliable object identification algorithms.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".