用类脑脉冲神经网络实现高效变道意图预测，部署成本大幅降低。
Deployment-friendly Lane-changing Intention Prediction Powered by Brain-inspired Spiking Neural Networks
- 基于脉冲神经网络的事件驱动编码，提升状态表示效率。
- 训练时间减少75%，内存占用降低99.9%，精度接近主流方法。
- 适合对实时性与资源敏感的自动驾驶系统部署场景。
在开放世界场景下，准确且实时地预测周边车辆的变道意图是实现安全高效自动驾驶的关键挑战。现有高性能方法因计算开销大、训练时间长、内存需求高，难以部署。本文提出一种基于类脑脉冲神经网络（SNN）的高效变道意图预测方法。利用SNN的事件驱动特性，实现车辆状态更高效的编码。在HighD和NGSIM数据集上的对比实验表明，该方法显著提升训练效率并降低部署成本，同时保持相当的预测精度。相比基线方法，训练时间减少75%，内存使用降低99.9%。结果验证了该方法在变道预测中的高效性与可靠性，展现出在安全高效自动驾驶系统中的应用潜力，尤其在缩短训练时间、降低内存消耗和加速推理方面具有明显优势。
原文摘要 · Abstract (English)
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.
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