Unsupervised Collaborative Domain Adaptation for Driving Scene Parsing

2026-06-01

AI Summary

This work addresses the limited generalization capability of autonomous driving scene parsing models under source-free and pixel-level annotation settings. To tackle this challenge, we propose an Unsupervised Collaborative Domain Adaptation (UCDA) framework that, for the first time in a source-free scenario, leverages multiple pre-trained source models to collaboratively transfer complementary knowledge into a unified target model without accessing any original source data. UCDA achieves effective complementary supervision and knowledge distillation through a class prototype memory bank for aligning prediction confidence, cross-model reliability assessment, and positive-negative consistency constraints. Extensive experiments demonstrate that UCDA significantly improves scene parsing accuracy and environmental robustness on multiple public driving datasets and real-world vehicle platforms.

Abstract

Reliable driving scene parsing is a fundamental capability for autonomous vehicles operating in open and dynamic driving environments. However, adapting perception models to new deployment domains remains challenging because pixel-level annotations are expensive to obtain, while source-domain data are often inaccessible due to privacy, security, or ownership constraints. Existing source-free unsupervised domain adaptation methods typically rely on a single pre-trained source model, which makes the adapted perception system vulnerable to source-specific biases and limits its robustness under diverse road layouts, illumination conditions, weather patterns, and traffic conditions. This article presents an unsupervised collaborative domain adaptation (UCDA) framework for driving scene parsing in a source-free setting, which transfers complementary knowledge from multiple pre-trained source models to a unified target model without accessing any original source samples. To compare predictions from independently trained models, UCDA constructs a class-level prototype memory bank and estimates cross-model prediction reliability through prototype similarity, reducing the effect of inconsistent confidence scales across source models. Based on the resulting complementary supervision, UCDA adopts a two-stage transfer strategy: multiple source models are first refined on unlabeled target-domain driving data through collaborative optimization with positive and negative consistency constraints, and their validated expertise is then distilled into a single deployable target model. Comprehensive evaluations on public driving-scene datasets and real-world data collected from an autonomous vehicle platform demonstrate that UCDA effectively consolidates complementary multi-source knowledge, improving target-domain scene parsing reliability and generalization across diverse driving environments.

Problem

• unsupervised domain adaptation
• driving scene parsing
• source-free
• multi-source knowledge
• autonomous driving

Innovation

• unsupervised collaborative domain adaptation
• source-free
• prototype memory bank
• multi-source knowledge distillation
• driving scene parsing