用脉冲神经网络模拟驾驶决策过程，让模型更真实可解释。
Akkumula: Evidence accumulation driver models with Spiking Neural Networks
- 用脉冲神经网络建模驾驶中的证据累积过程
- 在测试轨道数据上复现了刹车、加速、转向的时间轨迹
- 兼容现有模型架构，适合自动驾驶系统开发
证据累积过程能让驾驶模型更贴近真实行为，解释驾驶员如何根据感知输入和决策边界调整动作。当前缺乏统一建模方法，现有手段多为手工设计，难以适应且计算效率低。本文提出Akkumula框架，利用脉冲神经网络与深度学习技术实现证据累积建模。在测试轨道实验数据上，模型成功复现了刹车、加速和转向的时序动态。该框架可集成到现有机器学习架构中，支持大规模数据训练，能适应不同驾驶场景，同时保持内部逻辑相对透明。
原文摘要 · Abstract (English)
Processes of evidence accumulation can make driver models more realistic, by explaining how drivers adjust their actions based on perceptual inputs and decision boundaries. The absence of a standard modelling approach limits their adoption; existing methods are hand-crafted, hard to adapt, and computationally inefficient. This paper presents Akkumula, an evidence accumulation modelling framework that uses Spiking Neural Networks and other deep learning techniques. Tested on data from a test-track experiment, the model can reproduce the time course of braking, accelerating, and steering. Akkumula integrates with existing machine learning architectures, scales to large datasets, adapts to different driving scenarios, and keeps its internal logic relatively transparent.
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