Enhancing Multiple Vehicle Collision Protections with Parallelization and Adaptive Data Compression

Recent advancements in intelligent transportation systems have enabled smart vehicles to autonomously detect, predict, and respond to potential hazards in real time. However, achieving sub-second reaction performance remains challenging due to computational latency in sensor data processing. This paper presents an adaptive parallel processing framework that integrates multi-core concurrency and adjustable spatial down-sampling (compression) for real-time multi-vehicle collision prevention. We benchmark four operating modes (sequential/parallel × compressed/uncompressed) on a 22-thread CPU platform. Compared to the sequential uncompressed baseline, the proposed fork-compress mode reduces end-to-end pipeline latency by approximately 66%. Compared to the sequential compressed baseline, the reduction is smaller (≈24%), highlighting the importance of explicitly stating the baseline for headline claims. The scalability analysis is based on Amdahl’s Law and indicates an effective parallelizable fraction of about 25% under our implementation, with the remaining time dominated by I/O, synchronization, and coordination overhead. We define compression factor k as linear spatial down-sampling where both image width and height are divided by k (pixel area reduced to 1/k2). Empirical results show that moderate down-sampling (around k ≈ 4–6) provides the best latency–accuracy trade-off. A supporting detection study using YOLOv4-tiny on BDD100K demonstrates that down-sampling can significantly reduce mAP if the model is not retrained, and that compression-aware fine-tuning partially recovers the lost accuracy.