A dynamic fuzzy video compression control algorithm for wireless Advanced Driver Assistance Systems
Bibliographic record
Abstract
As video-based Advanced Driver Assistance Systems (ADAS) become integral to modern vehicle safety, the demand for reliable, high-performance wireless solutions for retrofitting vehicles have grown. This study introduces a wireless ADAS that employs a novel dynamic video compression control algorithm, integrating a hardware-based Motion Joint Photographic Experts Group (MJPEG) compression engine with adaptive fuzzy logic control strategies. The system dynamically adjusts video compression levels based on real-time conditions such as wireless data rates and available bandwidth, addressing key challenges in maintaining video quality and minimizing latency in wireless environments. The adaptive control is governed by two distinct fuzzy control strategies: Fuzzy Rule-Based (FRB) and Evolutionary Fuzzy Rules (EFR). Both strategies were optimized using nature-inspired algorithms, including Differential Evolution (DE), Particle Swarm Optimization (PSO), and Genetic Programming (GP). Among these, the EFR-based control was found to offer the best overall performance. Key performance indicators such as compression efficiency, latency, and throughput rates were thoroughly evaluated. Experimental results demonstrated that the EFR-based system provided up to a 35% improvement in compression efficiency compared to traditional methods, reduced video latency by approximately 20%, and optimized data throughput. Furthermore, the EFR-based control showcased enhanced generalization capabilities, outperforming FRB-based control under previously unobserved conditions, which is critical for real-world vehicular applications where network conditions may vary significantly. The implementation of artificial intelligence in the form of EFR significantly enhanced the system’s ability to adapt to varying data rates and environmental conditions, making it a promising solution for real-time video compression in computationally constrained embedded systems. • A fuzzy logic method controls video compression in wireless driver assistance systems. • Compression efficiency improved by 35 percent compared to traditional approaches. • Video latency reduced by 20 percent under unstable wireless communication conditions. • The method runs on microcontrollers with limited processing and memory resources. • Controllers are optimized offline using historical data and nature-inspired algorithms.
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 machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".