Um, Daeho (Seoul National University), Yeo, Yuneil (University of California, Berkeley), Yoon, Ji Won (Chung-Ang University), Choi, jin young (Seoul National University)

Unseen Road Type Detection in Road Networks for Intelligent Transportation Systems

Scheduled for presentation during the Poster Session "Data Mining and Data Analysis" (FrAT14), Friday, September 27, 2024, 10:30−12:30, Foyer

2024 IEEE 27th International Conference on Intelligent Transportation Systems (ITSC), September 24- 27, 2024, Edmonton, Canada

This information is tentative and subject to change. Compiled on December 26, 2024

Abstract

Existing methods for road type (highway, trunk road, etc.) classification assume that every road in real environments belongs to a road type seen during training. However, there are unseen road types in real-world scenarios. Thus, unreliable classification of an unseen-type road into a seen road type can cause critical safety issues in road-related applications. In this paper, we introduce a new framework to detect unseen road types. To this end, we adopt an out-of-distribution (OOD) detection approach studied in the deep learning field. However, conventional graph-based node-level OOD detection methods cannot be directly applied to the unseen road type detection problem since roads are represented by edges in road networks. To resolve this problem, we establish a new formulation of edge-level OOD detection and propose a novel energy propagation scheme on a line graph transformed from a road network to obtain OOD scores. Experimental results on real-world road networks demonstrate the effectiveness of our method, achieving state-of-the-art performance in unseen road type detection.