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

TL;DR

This paper introduces a novel spiking transformer-like architecture that efficiently fuses multi-modal sensory data for real-time autonomous vehicle decision-making, addressing computational constraints of traditional transformers.

Contribution

It presents a new spiking neural network architecture that reduces computational cost while maintaining high performance in multi-modal autonomous driving tasks.

Findings

Effective multi-modal fusion in real-time scenarios

Reduced computational cost compared to traditional transformers

High performance in highway environment decision tasks

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.