论文标题
改善混合流量的安全性：一种基于学习的模型预测性控制，用于自动和人类驱动的车辆排
Improving safety in mixed traffic: A learning-based model predictive control for autonomous and human-driven vehicle platooning
论文作者
论文摘要
随着自动驾驶汽车（AV）在公共道路上变得越来越普遍，因此不可避免地会在混合交通中与人类驱动的车辆（HVS）的互动。这需要AVS处理HVS不可预测性质的新控制策略。这项研究的重点是由AV和HVS组成的混合车辆排成的安全控制，尤其是在纵向驾驶的情况下。我们介绍了一个新型模型，该模型将常规的第一原理模型与高斯工艺（GP）基于机器学习的模型相结合，以更好地预测HV行为。我们的结果表明，与单独使用第一原理模型相比，均方根误差的预测HV速度有显着改善，均方根误差降低了35.64％。我们制定了一种名为GP-MPC的新控制策略，该策略使用拟议的HV模型，用于混合排中的车辆之间的更安全的距离管理。 GP-MPC策略有效地利用了GP模型评估不确定性的能力，从而大大提高了具有挑战性的交通情况（例如紧急制动场景）的安全性。在模拟中，GP-MPC策略的表现优于基线MPC方法，在混合交通中提供了更好的安全性和更有效的车辆运动。
As autonomous vehicles (AVs) become more common on public roads, their interaction with human-driven vehicles (HVs) in mixed traffic is inevitable. This requires new control strategies for AVs to handle the unpredictable nature of HVs. This study focused on safe control in mixed-vehicle platoons consisting of both AVs and HVs, particularly during longitudinal car-following scenarios. We introduce a novel model that combines a conventional first-principles model with a Gaussian process (GP) machine learning-based model to better predict HV behavior. Our results showed a significant improvement in predicting HV speed, with a 35.64% reduction in the root mean square error compared with the use of the first-principles model alone. We developed a new control strategy called GP-MPC, which uses the proposed HV model for safer distance management between vehicles in the mixed platoon. The GP-MPC strategy effectively utilizes the capacity of the GP model to assess uncertainties, thereby significantly enhancing safety in challenging traffic scenarios, such as emergency braking scenarios. In simulations, the GP-MPC strategy outperformed the baseline MPC method, offering better safety and more efficient vehicle movement in mixed traffic.
