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.

翻译：语义分割与车道线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），为实现低延迟实时响应需进行大量模数转换和大规模图像计算，导致高昂能耗。衍射光学神经网络（DONN）在能效方面展现出优于传统数字或光电计算平台上DNN的潜力。通过以光速进行衍射实现全光学图像处理，DONN在节省计算能耗的同时，借助全光学编码与计算降低了模数转换开销。本研究提出一种创新的全光学计算框架，用于自动驾驶中的RGB图像分割与车道线检测。实验结果表明，DONN系统在CityScapes数据集上具有优异的图像分割性能。此外，我们利用定制室内轨道数据集和CARLA仿真驾驶场景开展车道检测案例研究，进一步评估了模型在不同环境条件下的泛化能力。