Surrogate Safety Measures (SSMs) are extensively utilised in the evaluation of traffic risk in automated driving contexts. However, the majority of SSM-based evaluations employ fixed thresholds that fail to capture the human response to sustained borderline conditions or the reaction to brief, high-risk peaks. The present work proposes a biologically inspired reinterpretation of SSM thresholds. This is modelled as spiking thresholds of leaky integrate-and-fire (LIF) neurons, with multiple SSM inputs combined into a spiking neural network (SNN). The SNN is trained to emit spikes that are aligned with human braking onsets. The training data was recorded in a controlled car-following experiment using the 3D-CoAutoSim platform with CARLA/Unreal and a 6-DOF motion platform, where induced critical events were generated. The results demonstrate that the learned spiking activity qualitatively aligns with braking behaviour across scenarios and captures reactions that are not consistently explained by threshold crossings alone. Analysis across participants further indicates that learned input thresholds remain relatively consistent, while learned decay factors encode different temporal sensitivities for the SSMs. The findings of this study indicate that spiking dynamics may serve as a mechanism to facilitate the convergence of objective SSMs with subjective human safety perception.

翻译：替代安全度量（SSM）在自动驾驶环境下的交通风险评估中被广泛应用。然而，大多数基于SSM的评估采用固定阈值，未能捕捉人类对持续临界状态的反应或对短暂高风险峰值的响应。本研究提出一种受生物学启发的SSM阈值重新解读。该模型以漏积分放电（LIF）神经元的放电阈值为基础，将多个SSM输入整合为脉冲神经网络（SNN）。该SNN通过训练生成与人类制动起始时刻对齐的脉冲信号。训练数据基于3D-CoAutoSim平台（集成CARLA/Unreal引擎与6自由度运动平台）在受控跟车实验中记录，其中诱导产生关键事件。结果表明，学习到的脉冲活动在多种场景下与制动行为定性一致，并能捕捉到单独阈值穿越无法解释的反应。跨参与者分析进一步表明，学习到的输入阈值保持相对一致，而学习到的衰减因子编码了SSM的不同时间敏感性。本研究发现提示，脉冲动力学可作为促进客观SSM与主观人类安全感知相融合的机制。