# Reinterpreting Safety Thresholds as Neuron Spiking Thresholds

arXiv: https://arxiv.org/abs/2605.30368 · HTML: https://mlsanity.com/paper/2605.30368/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 27 references that are in the map.
- First posted on arXiv 18 May 2026.

## Builds on (most cited first, 10 of 10)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [CARLA: An Open Urban Driving Simulator](https://mlsanity.com/paper/1711.03938/) (2017)
- [Surrogate Gradient Learning in Spiking Neural Networks](https://mlsanity.com/paper/1901.09948/) (2019)
- [The highD Dataset: A Drone Dataset of Naturalistic Vehicle Trajectories on German Highways for Validation of Highly Automated Driving Systems](https://mlsanity.com/paper/1810.05642/) (2018)
- [Training Spiking Neural Networks Using Lessons From Deep Learning](https://mlsanity.com/paper/2109.12894/) (2021)
- [Spiking Neural Networks for Early Prediction in Human Robot Collaboration](https://mlsanity.com/paper/1807.11096/) (2018)
- [Implementation of Road Safety Perception in Autonomous Vehicles in a Lane Change Scenario](https://mlsanity.com/paper/2211.01113/) (2022)
- [Method for Comparison of Surrogate Safety Measures in Multi-Vehicle Scenarios](https://mlsanity.com/paper/2304.08998/) (2023)
- [Integrating Naturalistic Insights in Objective Multi-Vehicle Safety Framework](https://mlsanity.com/paper/2408.09769/) (2024)
- [A LiDAR-Driven Fallback Longitudinal Controller for Safer Following in Sudden Braking Scenarios](https://mlsanity.com/paper/2509.16642/) (2025)

## Abstract (arXiv)

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.