Reinterpreting Safety Thresholds as Neuron Spiking Thresholds
This work provides an incremental improvement in aligning objective safety measures with subjective human safety perception for automated driving systems.
This paper addresses the limitation of fixed thresholds in Surrogate Safety Measures (SSMs) for evaluating traffic risk in automated driving by proposing a biologically inspired reinterpretation using spiking thresholds of leaky integrate-and-fire (LIF) neurons. The resulting spiking neural network (SNN), trained on human braking onset data from a controlled car-following experiment, qualitatively aligns with braking behavior across scenarios and captures reactions not explained by simple threshold crossings.
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.