Abstract
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.
Abstract (translated)
语义分割和车道检测是自主驾驶系统中的关键任务。传统的处理方法主要依赖于深度神经网络（DNN），由于需要进行大量的模数转换以及大规模的图像计算以实现低延迟、实时响应，因此能耗较高。衍射光学神经网络（DONN）在能源效率方面相对于传统数字或光电计算平台上的DNN表现出显著的优势。通过利用光速下的光衍射来进行全光学图像处理，DONN可以节省计算能量成本，并通过全光学编码和计算减少与模数转换相关的开销。 在这项工作中，我们提出了一种新的全光学计算框架，用于自主驾驶应用中的RGB图像分割和车道检测。我们的实验结果展示了DONN系统在CityScapes数据集上进行图像分割的有效性。此外，我们还使用定制的室内赛道数据集和CARLA中模拟的驾驶场景进行了案例研究，进一步评估了模型在各种环境条件下的泛化能力。
URL
https://arxiv.org/abs/2602.07717
