Route2Vec: Enabling Eficient Use of Driving Context through Contextualized Route Representations

Abstract

Understanding how vehicle occupants experience their journey is key to designing adaptive in-car systems. The environments they This work is licensed under a Creative Commons Attribution International 4.0 License. MuC ’25, Chemnitz, Germany © 2025 Copyright held by the owner/author(s). ACM ISBN 979-8-4007-1582-2/25/08 https://doi.org/10.1145/3743049.3743056 encounter, ranging from road types and trafc patterns to weather conditions, shape their mental and emotional states during a ride. Yet, leveraging this contextual information remains a challenge due to its heterogeneous nature, comprising diverse data types, such as categorical, numerical, and boolean values of various scales. We introduce Route2Vec, an attention-based framework that encodes variable-length sequences of route context into compact, semantically meaningful embeddings using a self-supervised learning pipeline. These fxed-size representations allow for efcient comparisons between diferent driving situations using common similarity metrics such as Euclidean distance. Through linear probing and MuC ’25, August 31–September 03, 2025, Chemnitz, Germany Philipp Hallgarten, Thomas Kosch, Tobias Grosse-Puppendahl, and Enkelejda Kasneci qualitative analysis of the embedding space, we show that Route2Vec reliably captures salient, route-specifc characteristics. Route2Vec simplifes context-aware in-vehicle interaction by enabling designers to rapidly prototype intelligent in-vehicle interfaces. We make our trained models and code1 publicly available to foster research in this area.