Saved in:
| Main Authors: | , |
|---|---|
| Format: | Preprint |
| Published: |
2026
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2603.22729 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866908908952485888 |
|---|---|
| author | Elayan, Mohammad Kontar, Wissam |
| author_facet | Elayan, Mohammad Kontar, Wissam |
| contents | Driver heterogeneity is often reduced to labels or discrete regimes, compressing what is inherently dynamic into static categories. We introduce quantum-inspired representation that models each driver as an evolving latent state, presented as a density matrix with structured mathematical properties. Behavioral observations are embedded via non-linear Random Fourier Features, while state evolution blends temporal persistence of behavior with context-dependent profile activation. We evaluate our approach on empirical driving data, Third Generation Simulation Data (TGSIM), showing how driving profiles are extracted and analyzed. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_22729 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Behavioral Heterogeneity as Quantum-Inspired Representation Elayan, Mohammad Kontar, Wissam Machine Learning Multiagent Systems Methodology Driver heterogeneity is often reduced to labels or discrete regimes, compressing what is inherently dynamic into static categories. We introduce quantum-inspired representation that models each driver as an evolving latent state, presented as a density matrix with structured mathematical properties. Behavioral observations are embedded via non-linear Random Fourier Features, while state evolution blends temporal persistence of behavior with context-dependent profile activation. We evaluate our approach on empirical driving data, Third Generation Simulation Data (TGSIM), showing how driving profiles are extracted and analyzed. |
| title | Behavioral Heterogeneity as Quantum-Inspired Representation |
| topic | Machine Learning Multiagent Systems Methodology |
| url | https://arxiv.org/abs/2603.22729 |