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汽车安全与节能学报

汽车安全与节能学报 ›› 2026, Vol. 17 ›› Issue (1): 130-139.DOI: 10.3969/j.issn.1674-8484.2026.01.014

• 智能驾驶与智慧交通 • 上一篇    下一篇

混合交通下智能网联车辆预见性协同自适应巡航控制

韩东明1(), 程思哲1, 王金湘1,*(), 刘亚辉2, 殷国栋1   

  1. 1.东南大学 机械工程学院，南京 211189，中国
    2.清华大学 车辆与运载学院，北京 100084，中国
  • 收稿日期:2025-08-11 修回日期:2025-12-17 出版日期:2026-02-28 发布日期:2026-03-19
  • 通讯作者: 王金湘，教授。E-mail：wangjx@seu.edu.cn。
  • 作者简介:韩东明（1998—），男（汉），四川，博士研究生。E-mail：hdmseu@seu.edu.cn。
  • 基金资助:
    国家自然科学基金(52372410);国家自然科学基金(52072073);智能绿色车辆与交通全国重点实验室开放基金(KFY2415)

Predictive cooperative-adaptive cruise-control for the intelligent- connected vehicles in the mixed traffic

HAN Dongming1(), CHENG Sizhe1, WANG Jinxiang1,*(), LIU Yahui2, YIN Guodong1   

  1. 1. School of Mechanical Engineering, Southeast University, Nanjing 211189, China
    2. School of Vehicle and Mobility, Tsinghua University, Beijing 100084, China
  • Received:2025-08-11 Revised:2025-12-17 Online:2026-02-28 Published:2026-03-19

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摘要：

提出了一种基于物理信息神经网络（PINN）的预见性协同自适应巡航控制（CACC）策略，设计了人工驾驶车辆纵向行为预测算法，以便应对包含智能网联车辆的混合交通环境中人工驾驶车辆（HDV）的行为不确定性。基于PINN建立了人工驾驶车辆行为预测模型，将带微分方程约束的优化问题转化为神经网络的参数拟合问题。将人工驾驶车辆的状态预测结果作为参考输入，采用带软约束的模型预测控制方法（MPC），设计了混合交通下的预见性协同自适应巡航控制器PINN-MPC。在HighD数据集上进行了仿真验证。结果表明：与不包含物理信息的神经网络相比，本PINN策略在3 s预测时域内的加速度预测精度提升了28.9%。因而，本巡航控制策略改善了车辆的行驶安全性和舒适性。

关键词: 混合交通, 智能网联车辆, 人工驾驶车辆（HDV）, 协同自适应巡航控制（CACC）, 物理信息神经网络（PINN）

Abstract:

A predictive Cooperative Adaptive Cruise Control (CACC) method was proposed based on the Physics-Informed Neural Network (PINN) and a longitudinal behavior prediction algorithm for human-driven vehicles (HDV) was developed, to address the behavioral uncertainty of HDV in mixed traffic environments including intelligent-connected vehicles. The PINN-based HDV behavior prediction model was constructed, in which an optimization problem with differential-equation constraints was transformed into a neural network parameter fitting problem. The predicted states of HDV were used as reference inputs to design a predictive CACC controller for mixed traffic based on Model Predictive Control (MPC) with soft constraints, referred to as PINN-MPC. The proposed controller was validated through simulations on the HighD dataset. The results show that the PINN controller with physical information improves the acceleration prediction accuracy by 28.9% within a 3 s prediction horizon compared with neural networks without physical information. Therefore, the proposed cruise control strategy enhances both driving safety and ride comfort.

Key words: mixed traffic, intelligent-connected vehicles, human-driven vehicles (HDV), cooperative adaptive cruise control (CACC), physics-informed neural network (PINN)

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