Optimized Lightweight AI Algorithms for Real-Time Data Processing and Decision-Making in Edge Computing for Autonomous Vehicles
Bibliographic record
Abstract
Autonomous vehicle (AV) technology progress necessitates the development of ultra-efficient, scalable, and lightweight AI algorithms for real-time decision-making on edge computing platforms. Traditional AI models often impose high computational overheads, resulting in inefficiencies when applied to real-world scenarios. This paper proposes an alternative AI-based framework for AVs, developed and validated using the KITTI dataset. The strategy integrates attention-based sensor fusion, federated learning, adaptive neural networks, and model optimization techniques such as quantization and structured pruning to reduce computational load while maintaining high accuracy. Multi-Agent Deep Reinforcement Learning (MADRL) is employed to achieve optimal path planning and real-time decision-making, addressing scalability challenges in edgebased AV systems. Empirical testing with the KITTI dataset demonstrates that the proposed framework improves inference efficiency by 23 %, reduces energy consumption by 17 %, and enhances decision accuracy by 19 %. The system achieves an overall classification accuracy of 94.1 % for object detection and 93.2 % for lane following, confirming the framework's effectiveness in enabling safer and smarter autonomous driving.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".