混行环境下CACC系统驾乘舒适性优化控制

为提升协同式自适应巡航（cooperative adaptive cruise control，CACC）系统在由自动网联汽车（connected automated vehicle，CAV）和人工驾驶汽车（manual vehicle，MV）构成的混行交通流下的驾乘舒适性，提出考虑驾乘舒适性的双层控制策略（dual-layer control strategy considering ride comfort，RC-DCS）. 上层控制器从宏观角度出发，采用两状态空间模型调整跟车间距及车速，并利用代价函数改善车队的整体稳定性和舒适性；下层控制器从微观角度出发，优化单车的油门和制动踏板切换逻辑，稳定实际加速度输出，降低车辆频繁加减速引起的自身俯仰. 试验结果表明:RC-DCS在跟随MV工况中跟车间距误差和加速度分别降低了72.44%和24.87%；在MV插入CACC车队工况中通过增大跟车时距0.4 s以减少加速度波动；在跟车、紧急制动、旁车切入3种典型工况中，单车加速度标准差分别降低了9.6%、10.4%、2.9%. 

Optimal Control for Ride Comfort of Cooperative Adaptive Cruise Control System Under Mixed Traffic Flow

To improve the ride comfort of cooperative adaptive cruise control (CACC) system under the mixed traffic flow that comprises connected automated vehicle (CAV) and manual vehicle (MV), a dual-layer control strategy considering ride comfort (RC-DCS) is proposed. From a macro perspective, the upper controller adopts a two-state space model to adjust the following distance and speed, and improve the overall stability and comfort of the fleet by the use of the cost function. From a microscopic perspective, the lower controller optimizes the logic of switching the throttle and brake pedal of a single vehicle, and stabilizes its actual acceleration output, thereby reducing the pitch caused by frequent acceleration and deceleration. The experimental results show that, the RC-DCS can reduce the following distance error and acceleration by 72.44% and 24.87% respectively in following MV condition. In the condition of MV cut-in CACC fleet, the acceleration fluctuation is reduced by increasing the following headway of 0.4 s. In the three typical conditions of vehicle following, emergency braking and cut-in, the standard deviation of the single-vehicle acceleration is reduced by 9.6%, 10.4% and 2.9%, respectively.