Human-vehicle interaction in safety-critical traffic environments increasingly incorporates neural sensing to infer user intent and cognitive state, yet most existing approaches either treat electroencephalography (EEG) as a static biometric credential or train task-specific decoders that ignore long-term neurodynamic trajectories, lacking mechanisms for secure user identity and continual modeling of evolving cognitive states. This work proposes a self-supervised evolutionary learning (SSEL) framework that discovers individualized neurodynamic progressions and intrinsic identity manifolds directly from continuous EEG, without external labels or predefined cognitive stage models. SSEL jointly optimizes within-stage temporal predictability, boundary contrast, cross-trial alignment, and sparse stage-specific feature weights, while a population-based evolutionary search enables direct optimization in the discrete, non-differentiable space of candidate segmentations. We validate the framework on EEG recorded from participants performing a simulated road-crossing decision task, a canonical safety-critical scenario in which perceptual assessment, risk evaluation, and decision commitment unfold over time. The learned segmentations reveal stable, person-specific stage structures and neurodynamic signatures that support authentication and anomaly detection. Compared to inference-based segmentation baselines, SSEL achieves orders-of-magnitude higher boundary contrast, substantial gains in cross-trial generalization of intention boundaries, and more interpretable, sparse stage-wise feature attributions. Beyond performance, the framework advances a progression-aware perspective on cognitive neurodynamics, where security, resilience, and personalization emerge from the intrinsic temporal structure of brain activity, with implications for next-generation smart urban and transportation infrastructures.

翻译：在安全关键的交通环境中，人车交互越来越多地采用神经传感技术来推断用户意图和认知状态。然而，现有方法大多将脑电图（EEG）视为静态的生物特征凭证，或训练忽略长期神经动态轨迹的任务特定解码器，缺乏用于安全用户身份验证和演化认知状态持续建模的机制。本研究提出一种自监督进化学习（SSEL）框架，该框架无需外部标签或预定义认知阶段模型，即可直接从连续脑电图中发现个体化的神经动态进展和内在身份流形。SSEL联合优化了阶段内时间可预测性、边界对比度、跨试次对齐以及稀疏的阶段特定特征权重，同时基于种群的进化搜索实现了在候选分割的离散、不可微空间中进行直接优化。我们在参与者执行模拟过马路决策任务（一个典型的、随时间展开的感知评估、风险权衡和决策承诺的安全关键场景）时记录的脑电数据上验证了该框架。学习得到的分割揭示了稳定的、个体特异性的阶段结构和神经动态特征，这些特征支持身份认证和异常检测。与基于推理的分割基线方法相比，SSEL实现了数量级更高的边界对比度、在意图边界的跨试次泛化方面取得显著提升，并获得了更具可解释性的稀疏阶段特征归因。除了性能优势，该框架还推进了认知神经动力学的进展感知视角，其中安全性、鲁棒性和个性化从大脑活动的内在时间结构中涌现，这对下一代智慧城市和交通基础设施具有重要启示。