不用物理模型，用神经网络预测最优驾驶策略省油
NPC: Neural Predictive Control for Fuel-Efficient Autonomous Trucks
- 用注意力机制从2万+公里数据中学习车辆与油耗关系
- 仿真和实测分别比传统方法多省2.41%和3.45%油
- 适合想提升燃油效率的自动驾驶卡车团队
燃油效率是燃油动力长途货运卡车降低成本、减少碳排放的关键。现有预测控制方法依赖车辆动力学与发动机的精确物理模型，包括重量、阻力系数及发动机的制动油耗率（BSFC）图。本文提出纯数据驱动的神经预测控制（NPC）方法，不使用任何物理模型。通过超过20,000公里历史数据训练，新提出的NVFormer利用注意力机制隐式建模车辆动力学、道路坡度、油耗与控制指令之间的关系。基于当前行程过往采样片段与基于锚点的未来数据合成，NVFormer可推断出合理油耗下的最优控制指令。无需物理模型的NPC在仿真和开放高速路测试中，分别比基线PCC方法多节省2.41%和3.45%燃油。
原文摘要 · Abstract (English)
Fuel efficiency is a crucial aspect of long-distance cargo transportation by oil-powered trucks that economize on costs and decrease carbon emissions. Current predictive control methods depend on an accurate model of vehicle dynamics and engine, including weight, drag coefficient, and the Brake-specific Fuel Consumption (BSFC) map of the engine. We propose a pure data-driven method, Neural Predictive Control (NPC), which does not use any physical model for the vehicle. After training with over 20,000 km of historical data, the novel proposed NVFormer implicitly models the relationship between vehicle dynamics, road slope, fuel consumption, and control commands using the attention mechanism. Based on the online sampled primitives from the past of the current freight trip and anchor-based future data synthesis, the NVFormer can infer optimal control command for reasonable fuel consumption. The physical model-free NPC outperforms the base PCC method with 2.41% and 3.45% more significant fuel saving in simulation and open-road highway testing, respectively.
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