为了提高信号交叉口上游区域车辆通行效率并降低油耗及排放，该文提出一种考虑驾驶人接受程度的车速二次引导策略，旨在解决人工驾驶环境下驾驶人执行引导策略不充分的问题。该研究以人工驾驶网联车辆为对象，引入驾驶人接受程度参数，改进了加速引导通过、减速引导通过及减速引导停车3种一次引导策略模型，考虑驾驶人接受程度低带来的影响，构建二次加速引导通过和二次减速引导通过模型。同时利用Matlab仿真平台搭建仿真环境，考虑车辆运行轨迹、速度、加速度、油耗及排放影响因素，对比分析无速度引导、不同驾驶人接受程度下一次引导及二次引导的效果。仿真结果表明：严格遵循一次引导策略可使更多车辆不停车通过交叉口，通行效率提升约14.3%，油耗与排放降低10%~15%；驾驶人接受程度较低对车速一次引导造成了很大的负面影响，使一次引导策略失效，车辆产生额外油耗及排放；二次引导策略能够有效缓解驾驶人接受程度低的影响，使车辆通行效率恢复至一次引导策略的95%以上，油耗与排放减少约8%~12%。

This study proposes a secondary speed guidance strategy that incorporates driver acceptance levels to enhance the traffic efficiency address and reduce fuel consumption and emissions in the upstream areas of signalized intersections. The strategy addresses the inadequate execution of guidance strategies by drivers in manual driving environments. Focusing on connected human-driven vehicles, it introduces the parameter of driver acceptance degree to refine three primary guidance models: acceleration guidance through, deceleration guidance through, and deceleration guidance to stop. To mitigate the impact of low driver acceptance, secondary acceleration guidance through and secondary deceleration guidance through models are constructed. Using the Matlab simulation platform, this study established a simulation environment that considers vehicle trajectory, speed, acceleration, fuel consumption, and emissions. Comparative analyses of no guidance, primary guidance under varying driver acceptance levels, and secondary guidance were conducted. The simulation results show that strictly following the primary guidance strategy can enable more vehicles to pass through the intersection without stopping, improving traffic efficiency by approximately 14.3%, and reducing fuel consumption and emissions by 10% to 15%. Low driver acceptance has a significant negative impact on primary speed guidance, rendering the primary guidance strategy ineffective and causing additional fuel consumption and emissions. The secondary guidance strategy can effectively alleviate the impact of low driver acceptance, restoring traffic efficiency to over 95% of that of primary guidance and reducing fuel consumption and emissions by approximately 8% to 12%.