The adaptive cruise control (ACC) system has received significant attention due to traffic safety improvement, traffic throughput increment, and energy conservation. Model Predictive Control (MPC) has been successfully applied in the control of multi-objective vehicular ACC. However, as a state-feedback policy, MPC requires full state measurement. Meanwhile, the real-time performance of MPC is intractable. This paper proposes to estimate the state value and disturbance value with an extended state Kalman filter to deal with measurement uncertainty. The Kalman filter is based on an augmented state-space model which takes the disturbance term as a new state. To improve real-time performance, this paper suggests employing an explicit MPC (EMPC) based on binary search tree to move the online computational burden of MPC to offline computation by multi-parametric quadratic programming (MPQP). An improved algorithm to solve the MPQP problem offline is proposed, which is initialized discarding the requirement of parameters range, while previous methods need. In the simulated measurement process, the extended state Kalman filter can effectively reduce noise and accurately estimate the value of state and disturbance in the car-following model. Simulations in different scenarios are performed to test the effectiveness of the proposed ACC controller. Results show that the proposed EMPC for the ACC system can improve the real-time performance of the MPC with little loss of performance. On average, the EMPC via binary search is 95.8 times faster than the MPC with the same parameters as EMPC for the studied ACC system. And it has better overall performance compared with the ACC with collision avoidance (CA-ACC) method.

自适应巡航控制（ACC）系统由于交通安全的改善、交通吞吐量的增加和节能而受到了极大的关注。模型预测控制（MPC）已成功应用于多目标车辆ACC的控制。然而，作为一种状态反馈策略，MPC 需要全状态测量。同时，MPC 的实时性能是难以处理的。本文提出用扩展状态卡尔曼滤波器估计状态值和扰动值来处理测量不确定性。卡尔曼滤波器基于增强状态空间模型，该模型将干扰项作为新状态。为了提高实时性能，本文建议采用基于二叉搜索树的显式 MPC (EMPC)，通过多参数二次规划 (MPQP) 将 MPC 的在线计算负担转移到离线计算。提出了一种离线求解MPQP问题的改进算法，其初始化丢弃了参数范围的要求，而以前的方法需要。在模拟测量过程中，扩展状态卡尔曼滤波器可以有效降低噪声，准确估计跟驰模型中的状态值和扰动值。执行不同场景中的模拟以测试所提出的 ACC 控制器的有效性。结果表明，所提出的 ACC 系统 EMPC 可以在性能损失很小的情况下提高 MPC 的实时性能。平均而言，通过二分搜索的 EMPC 为 95。对于所研究的 ACC 系统，它比具有与 EMPC 相同参数的 MPC 快 8 倍。并且与带碰撞避免的ACC（CA-ACC）方法相比，它具有更好的整体性能。