Akkumula: Evidence accumulation driver models with Spiking Neural Networks

• URL: http://arxiv.org/abs/2505.05489v1
• Date: Wed, 30 Apr 2025 10:03:11 GMT
• Title: Akkumula: Evidence accumulation driver models with Spiking Neural Networks
• Authors: Alberto Morando,
• Abstract summary: This paper introduces Akkumula, an evidence accumulation modelling framework built using deep learning techniques.<n>The core of the library is based on Spiking Neural Networks, whose operation mimic the evidence accumulation process in the biological brain.<n>The model fits well the time course of vehicle control based on vehicle sensor data.
• Score: 1.6988773509268102
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Processes of evidence accumulation for motor control contribute to the ecological validity of driver models. According to established theories of cognition, drivers make control adjustments when a process of accumulation of perceptual inputs reaches a decision boundary. Unfortunately, there is not a standard way for building such models, limiting their use. Current implementations are hand-crafted, lack adaptability, and rely on inefficient optimization techniques that do not scale well with large datasets. This paper introduces Akkumula, an evidence accumulation modelling framework built using deep learning techniques to leverage established coding libraries, gradient optimization, and large batch training. The core of the library is based on Spiking Neural Networks, whose operation mimic the evidence accumulation process in the biological brain. The model was tested on data collected during a test-track experiment. Results are promising. The model fits well the time course of vehicle control (brake, accelerate, steering) based on vehicle sensor data. The perceptual inputs are extracted by a dedicated neural network, increasing the context-awareness of the model in dynamic scenarios. Akkumula integrates with existing machine learning architectures, benefits from continuous advancements in deep learning, efficiently processes large datasets, adapts to diverse driving scenarios, and maintains a degree of transparency in its core mechanisms.

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