机器学习模型比传统方法更精准预测电动车跟车行为。
Modeling Electric Vehicle Car-Following Behavior: Classical vs Machine Learning Approach
- 用随机森林模型基于间距、速度和跟车类型预测加速度
- 随机森林在各类跟车间距下均表现最优，误差低至0.0016
- 适合用于智能交通系统仿真与混合自动驾驶环境分析
随着电动汽车（EV）普及，理解其驾驶行为对提升交通安全和开发智能驾驶系统至关重要。本研究对比了经典模型与机器学习方法在电动车跟车行为建模中的表现。经典模型包括智能驾驶员模型（IDM）、最优速度模型（OVM）、最优相对速度模型（OVRV）及简化协同自适应巡航控制模型（CACC），机器学习方法采用随机森林回归器。基于真实世界数据集，该数据集记录了电动车在不同驾驶条件下跟随内燃机汽车的行驶情况，通过最小化预测值与实际数据之间的均方根误差（RMSE）校准经典模型参数。随机森林模型以间距、速度和跟车类型为输入，预测加速度。结果显示，随机森林在所有场景中表现最佳：中等间距下RMSE为0.0046，长间距下为0.0016，超长间距下为0.0025。在物理模型中，CACC在长间距下的性能最佳，RMSE为2.67。这些发现表明，机器学习模型在各类跟车情境下均具有显著优势，有助于模拟电动车行为并分析电动车融合环境中的混合自主交通动态。
原文摘要 · Abstract (English)
The increasing adoption of electric vehicles (EVs) necessitates an understanding of their driving behavior to enhance traffic safety and develop smart driving systems. This study compares classical and machine learning models for EV car following behavior. Classical models include the Intelligent Driver Model (IDM), Optimum Velocity Model (OVM), Optimal Velocity Relative Velocity (OVRV), and a simplified CACC model, while the machine learning approach employs a Random Forest Regressor. Using a real world dataset of an EV following an internal combustion engine (ICE) vehicle under varied driving conditions, we calibrated classical model parameters by minimizing the RMSE between predictions and real data. The Random Forest model predicts acceleration using spacing, speed, and gap type as inputs. Results demonstrate the Random Forest's superior accuracy, achieving RMSEs of 0.0046 (medium gap), 0.0016 (long gap), and 0.0025 (extra long gap). Among physics based models, CACC performed best, with an RMSE of 2.67 for long gaps. These findings highlight the machine learning model's performance across all scenarios. Such models are valuable for simulating EV behavior and analyzing mixed autonomy traffic dynamics in EV integrated environments.
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