Driver Personalized Lane Change Behavior Analysis Based on Attention Deep Embedding Clustering China FAW Group Corporation R and D Center: First Automobile

Format:

Book

Conference/Event

Author/Creator:

Dong, Haomin, author.

Contributor:

Han, Jiayi

Li, Lun

Tian, Jiaxiao

Wang, Wei

Wang, Yue

Yue, Yi

Conference Name:

SAE 2025 Intelligent and Connected Vehicles Symposium (2025-09-19 : Shanghai, China)

Language:

English

Physical Description:

1 online resource cm

Place of Publication:

Warrendale, PA SAE International 2025

Summary:

The characteristic representation and in-depth understanding of driver personalized driving behavior are fundamental to achieving human-like autonomous driving, enhancing the rationality of autonomous driving decisions, and meeting passengers' personalized needs. [ADDED]Personalized driving behavior refers to individual-specific patterns in vehicle operation that emerge from drivers' unique combinations of skills, risk tolerance, and habitual responses.However, current research lacks consideration of cluster analysis in the feature representation stage and ignores the time-varying contribution degree of time series values to low-dimensional features, which inhibits further utilization and development. This study adopts deep embedding clustering method and introduces attention mechanism to investigate driver personalized high-speed lane change behavior.[ADDED] Using a comprehensive driving simulator platform, we collected 15-channel time series data from 12 drivers performing 216 lane changes across 18 controlled scenarios. The research establishes a joint optimization framework that simultaneously learns feature representation and cluster assignment through a variable joint objective function. Results show that compared with baseline methods, the features characterized by this method are closest to the original data after reconstruction. The clustering results demonstrate high intra-cluster compactness, large inter-cluster distances, and clear cluster shapes with significant differences among categories, facilitating personalized driving behavior classification. Model validation on multi-channel temporal classification datasets confirms the efficiency of the proposed model and effectiveness of attention mechanism integration with deep embedding clustering. This study reveals the superiority of attention deep embedding clustering method in the field of driver personalized driving behavior analysis and provides new directions for future research in intelligent vehicle development

Notes:

Vendor supplied data

Publisher Number:

2025-01-7347

Access Restriction:

Restricted for use by site license