Multisource Data Fusion and Algorithm Comparison in Understanding Driving Intent

DOI

10.2991/978-94-6463-986-5_58How to use a DOI?

Keywords

Driving Intention; Multi-Source Data Fusion; Multi-Modal Fusion; Federated Learning; Autonomous Driving

Abstract

This study proposes an integrated framework for detecting a vehicle’s driving intention using multi-source data fusion, thereby addressing the limitations of relying on single data sources in complex traffic scenarios. Environmental perception data (other cars, pedestrians, traffic lights) are processed via YOLOv8 and BEV to extract fine-grained spatial features; vehicle motion data (speed, acceleration, steering angle) are denoised with Kalman filters to eliminate sensor drift and road unevenness interference; driver behavior insights from eye-tracking (gaze points, blink frequency) and HMI (button operations, voice commands) derive regularized features reflecting attention and intentions. The framework evaluates three algorithms: rule-driven methods offer interpretability but low accuracy, statistical learning boosts precision but depends on manual features, and deep learning achieves high accuracy yet faces latency and black box issues. Single-modality approaches lack adaptability, while multi-modal fusion reaches high accuracy but raises costs. To balance trade-offs, it suggests cross-modal lightweight integration (reducing overhead) and federated learning (protecting privacy) for algorithm selection and autonomous driving deployment, guiding practical use.

Copyright

© 2026 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Download article (PDF)

TY  - CONF
AU  - Yuqi Luo
PY  - 2026
DA  - 2026/02/18
TI  - Multisource Data Fusion and Algorithm Comparison in Understanding Driving Intent
BT  - Proceedings of the 2025 International Conference on Electronics, Electrical and Grid Technology (ICEEGT 2025)
PB  - Atlantis Press
SP  - 569
EP  - 577
SN  - 2352-5401
UR  - https://doi.org/10.2991/978-94-6463-986-5_58
DO  - 10.2991/978-94-6463-986-5_58
ID  - Luo2026
ER  -