ColReg: A Generative Subjective Logic-Driven Crowd-AI Collaborative Regression Framework for Vehicle Damage Estimation

The vehicle damage poses a significant concern in urban transportation and public safety due to its potential to disrupt traffic flow, increase infrastructure maintenance costs, and elevate safety risks for motorists and pedestrians in our community. This paper develops a principled human-AI collaborative regression framework that effectively harnesses the collective intelligence of both AI and crowdsourced human workers to quantify the damage incurred by a vehicle following collisions, accidents, or natural events. We refer to such a problem as the collaborativevehicle damage estimation (VDE)problem. Our work is motivated by the complementary strengths of AI and human intelligence. On one hand, manual vehicle damage inspections, while thorough, are known for being time-consuming and prone to human errors. On the other hand, despite AI's efficiency and consistency, it can encounter challenges in providing accurate damage assessments due to its limitations in comprehending the complex context of vehicle damage imagery. To address such challenges, our paper focuses on leveraging crowdsourced human intelligence for contextual understanding and nuanced reasoning to assist AI in addressing the VDE problem. In particular, we developColReg, a crowd-AI collaborative regression model that establishes a virtual interaction between the crowd and AI to collectively provide accurate vehicle damage estimation under a principled subjective logic framework. Evaluation results on a real-world VDE application show that ColReg outperforms both AI-regression models and crowd-AI collaboration baselines in accurately estimating vehicle damages across various evaluation scenarios.