In modern vehicular systems, robust performance under harsh conditions has become a critical problem of autonomous driving. Our study delivers a comprehensive evaluation of the newest iteration of the YOLO series, which is YOLOv11 Nano architecture benchmarked against the widely adopted YOLOv8 Nano as a baseline on a custom fused dataset that combines the Indian Driving Dataset (IDD) [1] and Berkeley Deep Drive Dataset (BDD100K) [2]. We have analyzed the trade-offs among detection accuracy, inference speed, and computational efficiency in high-entropy scenarios involving dense mixed traffic, rain, and low-light conditions. Specifically, YOLOv11n achieves a mean Average Precision (mAP@50) of 46.6%, with a notable 3.2% improvement in Precision over the baseline, effectively reducing false positives in cluttered scenes. Furthermore, the proposed model exhibits enhanced energy efficiency, requiring 22% fewer FLOPs (6.3G vs. 8.1G) while maintaining real-time inference speed of 70.9 FPS on a Tesla T4 GPU, offering an optimal trade-off for safety-critical edge deployment.

翻译：在现代车辆系统中，恶劣条件下的鲁棒性能已成为自动驾驶的关键问题。本研究对YOLO系列最新迭代版本——YOLOv11 Nano架构进行了全面评估，并以广泛采用的YOLOv8 Nano作为基准，在融合印度驾驶数据集（IDD）[1]和伯克利深度驾驶数据集（BDD100K）[2]的定制数据集上进行了对比分析。我们研究了在涉及密集混合交通、降雨和低光照条件的高熵场景中，检测精度、推理速度与计算效率之间的权衡关系。具体而言，YOLOv11n的均值平均精度（mAP@50）达到46.6%，相比基准模型精度提升3.2%，有效减少了杂乱场景中的假阳性检测。此外，该模型展现出更高的能效性，在保持Tesla T4 GPU上70.9 FPS实时推理速度的同时，FLOPs降低22%（6.3G对比8.1G），为安全关键型边缘部署提供了最优权衡方案。