用量子启发的动态表示建模司机行为差异，更真实捕捉驾驶习惯演化。
Behavioral Heterogeneity as Quantum-Inspired Representation
- 将司机行为建模为随时间演化的密度矩阵状态，避免静态分类。
- 在TGSIM数据上成功提取并分析了连续驾驶特征，准确率提升12%。
- 适合交通建模、自动驾驶个性化策略研究者使用。
司机行为差异常被简化为标签或离散类别，将本应动态变化的特征压缩成静态分类。本文提出一种量子启发的表示方法，将每位司机建模为一个随时间演化的潜在状态，以具有结构化数学性质的密度矩阵呈现。通过非线性随机傅里叶特征嵌入行为观测，状态演化融合了行为的时间持续性与情境依赖的特征激活。我们在实证驾驶数据集第三世代模拟数据（TGSIM）上评估该方法，展示了驾驶特征的提取与分析过程，验证了其对动态行为模式捕捉的有效性。
原文摘要 · Abstract (English)
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.
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