All-Optical Segmentation via Diffractive Neural Networks for Autonomous Driving

2026-02-07

AI Summary

This work proposes an all-optical computing framework based on Diffractive Optical Neural Networks (DONNs) to address the high energy consumption and analog-to-digital conversion overhead associated with conventional deep neural networks in semantic segmentation and lane detection for autonomous driving. For the first time, DONNs are applied to autonomous driving scenarios, enabling direct optical-domain inference on RGB images for both semantic segmentation and lane detection without requiring analog-to-digital conversion, thereby achieving light-speed processing. Experimental results demonstrate that the proposed framework effectively performs semantic segmentation on the Cityscapes dataset and exhibits strong generalization across a custom indoor test track and the CARLA simulation environment, significantly reducing computational latency and energy consumption.

Technology Category

• Computer Vision: Learning & Optimization for CV
• Search and Optimization: Learning to Search
• Machine Learning: Deep Neural Architectures and Foundation Models

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Search and Retrieval-Augmented AI: Vertical and domain-specific search

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.

Problem

• semantic segmentation
• lane detection
• autonomous driving
• energy efficiency
• real-time processing

Innovation

• Diffractive Optical Neural Networks
• All-Optical Computing
• Semantic Segmentation
• Lane Detection
• Autonomous Driving