Deployment-friendly Lane-changing Intention Prediction Powered by Brain-inspired Spiking Neural Networks

TL;DR

This paper introduces a brain-inspired Spiking Neural Network approach for lane-changing intention prediction in autonomous driving, significantly reducing training time and memory usage while maintaining high accuracy.

Contribution

The paper presents a novel SNN-based method that improves deployment efficiency for lane-changing prediction in autonomous vehicles, outperforming existing methods in training speed and memory efficiency.

Findings

Reduces training time by 75%

Decreases memory usage by 99.9%

Maintains comparable prediction accuracy

Abstract

Accurate and real-time prediction of surrounding vehicles' lane-changing intentions is a critical challenge in deploying safe and efficient autonomous driving systems in open-world scenarios. Existing high-performing methods remain hard to deploy due to their high computational cost, long training times, and excessive memory requirements. Here, we propose an efficient lane-changing intention prediction approach based on brain-inspired Spiking Neural Networks (SNN). By leveraging the event-driven nature of SNN, the proposed approach enables us to encode the vehicle's states in a more efficient manner. Comparison experiments conducted on HighD and NGSIM datasets demonstrate that our method significantly improves training efficiency and reduces deployment costs while maintaining comparable prediction accuracy. Particularly, compared to the baseline, our approach reduces training time by 75% and memory usage by 99.9%. These results validate the efficiency and reliability of our method in lane-changing predictions, highlighting its potential for safe and efficient autonomous driving systems while offering significant advantages in deployment, including reduced training time, lower memory usage, and faster inference.