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arXiv · 2512.01882

New Spiking Architecture for Multi-Modal Decision-Making in Autonomous Vehicles

Aref Ghoreishee·Abhishek Mishra·Lifeng Zhou·John Walsh·Nagarajan Kandasamy
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Abstract

This work proposes an end-to-end multi-modal reinforcement learning framework for high-level decision-making in autonomous vehicles. The framework integrates heterogeneous sensory input, including camera images, LiDAR point clouds, and vehicle heading information, through a cross-attention transformer-based perception module. Although transformers have become the backbone of modern multi-modal architectures, their high computational cost limits their deployment in resource-constrained edge environments. To overcome this challenge, we propose a spiking temporal-aware transformer-like architecture that uses ternary spiking neurons for computationally efficient multi-modal fusion. Comprehensive evaluations across multiple tasks in the Highway Environment demonstrate the effectiveness and efficiency of the proposed approach for real-time autonomous decision-making.

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Aref Ghoreishee, Abhishek Mishra, Lifeng Zhou, John Walsh, Nagarajan Kandasamy. 2025-12-01. New Spiking Architecture for Multi-Modal Decision-Making in Autonomous Vehicles. https://arxiv.org/abs/2512.01882

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