Autonomous driving demands an integrated approach that encompasses perception, prediction, and planning, all while operating under strict energy constraints to enhance scalability and environmental sustainability. We present Spiking Autonomous Driving (SAD), the first unified Spiking Neural Network (SNN) to address the energy challenges faced by autonomous driving systems through its event-driven and energy-efficient nature. SAD is trained end-to-end and consists of three main modules: perception, which processes inputs from multi-view cameras to construct a spatiotemporal bird's eye view; prediction, which utilizes a novel dual-pathway with spiking neurons to forecast future states; and planning, which generates safe trajectories considering predicted occupancy, traffic rules, and ride comfort. Evaluated on the nuScenes dataset, SAD achieves competitive performance in perception, prediction, and planning tasks, while drawing upon the energy efficiency of SNNs. This work highlights the potential of neuromorphic computing to be applied to energy-efficient autonomous driving, a critical step toward sustainable and safety-critical automotive technology. Our code is available at \url{https://github.com/ridgerchu/SAD}.

翻译：自动驾驶需要一种集感知、预测与规划于一体的综合方法，同时需在严格的能量约束下运行，以提升可扩展性和环境可持续性。我们提出了脉冲自动驾驶（SAD），这是首个统一的脉冲神经网络（SNN），通过其事件驱动与高能效的特性应对自动驾驶系统面临的能量挑战。SAD采用端到端训练，包含三个主要模块：感知模块处理多视角相机输入以构建时空鸟瞰图；预测模块利用具有脉冲神经元的新型双通路来预测未来状态；规划模块则综合考虑预测的占据区域、交通规则和乘坐舒适度，生成安全轨迹。在 nuScenes 数据集上的评估表明，SAD 在感知、预测和规划任务中均取得了有竞争力的性能，同时发挥了 SNN 的能效优势。这项工作凸显了神经形态计算应用于高效能自动驾驶的潜力，这是迈向可持续且安全关键的汽车技术的关键一步。我们的代码公开于 \url{https://github.com/ridgerchu/SAD}。