车辆多模式多目标自适应巡航控制

为增强量产ACC对前车驾驶意图的预判与自适应能力，发展了一种多目标自适应巡航控制算法，建立闭环纵向跟驰模型。基于模型预测控制理论，综合协调巡航过程中驾驶员期望响应、跟驰安全性、车辆自身物理限制等控制目标，并引入松弛向量以确保滚动在线优化存在可行解。采用待优化目标与控制输入权重调校以及控制器工作域边界松弛的策略，将ACC系统划分出6种工作模式，同时采用模糊推理与加速度加权平均策略，以实现工作模式最佳匹配与平稳过渡。仿真结果表明，多模式设计策略与多目标控制算法能够一定程度上提升ACC系统的适应性与友好性。

Multi-Objective Adaptive Cruise Control with Multi-Mode Strategy

In order to prejudge driving intention of preceding car and enhance the adaptability of adaptive cruise control (ACC) against complex traffic scenarios simultaneously, a multi-objective adaptive cruise control algorithm considering multi-mode switching strategy is proposed. Based on the model predictive control (MPC) framework, it is hopeful to comprehensively coordinate various conflicting objectives such as driver's desired response, rear-end safety, and vehicular physical limits. Meanwhile, the slack variable vector is introduced to deal with non-feasible solution owing to hard constraints during online optimization. The desired response as well as constraint boundary varies with traffic scenarios, and consequently multiple ACC modes are designed by means of slightly adjusting weights of control objectives and system input, constraint boundary as well as slack relaxation. Further, we can obtain relatively suitable mode as well as smooth transition by fuzzy inference and the weighted average method. The simulations show that under emergent traffic scenarios, the multi-objective control algorithm together with multi-mode switching strategy is able to achieve good expectation during the car-following.