用光衍射实现自动驾驶图像分割，能耗更低、速度更快。
All-Optical Segmentation via Diffractive Neural Networks for Autonomous Driving
- 通过光衍射在纯光学域处理图像，跳过数模转换和大算力计算。
- 在CityScapes数据集上实现有效语义分割，室内轨道与CARLA模拟中验证泛化能力。
- 适合追求超低功耗实时视觉的自动驾驶系统研发者。
语义分割和车道检测是自动驾驶系统中的关键任务。传统方法主要依赖深度神经网络（DNN），因需大量模数转换和大规模图像计算，导致高能耗，难以满足低延迟实时响应需求。衍射光学神经网络（DONNs）在数字或光电计算平台上展现出优于传统DNN的能效优势。通过光速下的光衍射实现全光学图像处理，DONNs在节省计算能耗的同时，通过全光学编码与计算减少了模数转换开销。本文提出一种新型全光学计算框架，用于自动驾驶场景下的RGB图像分割与车道检测。实验结果表明，该DONN系统在CityScapes数据集上实现了有效的图像分割。此外，我们在自定义的室内轨道数据集及CARLA仿真驾驶场景中开展车道检测案例研究，进一步评估模型在多种环境条件下的泛化能力。
原文摘要 · Abstract (English)
Semantic segmentation and lane detection are crucial tasks in autonomous driving systems. Conventional approaches predominantly rely on deep neural networks (DNNs), which incur high energy costs due to extensive analog-to-digital conversions and large-scale image computations required for low-latency, real-time responses. Diffractive optical neural networks (DONNs) have shown promising advantages over conventional DNNs on digital or optoelectronic computing platforms in energy efficiency. By performing all-optical image processing via light diffraction at the speed of light, DONNs save computation energy costs while reducing the overhead associated with analog-to-digital conversions by all-optical encoding and computing. In this work, we propose a novel all-optical computing framework for RGB image segmentation and lane detection in autonomous driving applications. Our experimental results demonstrate the effectiveness of the DONN system for image segmentation on the CityScapes dataset. Additionally, we conduct case studies on lane detection using a customized indoor track dataset and simulated driving scenarios in CARLA, where we further evaluate the model's generalizability under diverse environmental conditions.
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