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.

翻译：证据累积过程能够解释驾驶员如何根据感知输入和决策边界调整其行为，从而使驾驶员模型更具真实性。由于缺乏标准建模方法，现有模型的应用受到限制；现有方法多为手工构建、难以适应且计算效率低下。本文提出Akkumula——一个利用脉冲神经网络及其他深度学习技术的证据累积建模框架。通过在试验场数据上的测试，该模型能够复现刹车、加速和转向的时间过程。Akkumula可与现有机器学习架构集成，能扩展至大规模数据集，适应不同驾驶场景，并保持其内部逻辑相对透明。