论文标题
在混合交通环境中，以脑为脑启发的建模和决策
Brain-Inspired Modelling and Decision-making for Human-Like Autonomous Driving in Mixed Traffic Environment
论文作者
论文摘要
在本文中，一个类似人类的驾驶框架是为自动驾驶汽车（AV）设计的，旨在使AV更好地整合到人类驾驶的运输生态中，并消除人类驾驶员对自动驾驶的误解和不相容。基于对现实世界交互数据集的分析，使用模糊推理方法建立了驱动攻击性估计模型。然后，设计了一种将大脑情感学习电路模型（BELCM）与两点预览模型整合在一起的人类驾驶模型。在类似人类的车道变化决策算法中，成本函数的设计经过全面设计，考虑推动安全性和旅行效率。根据成本函数和多构造，动态游戏算法应用于建模AV和人驾驶员之间的交互和决策。此外，为了确保AV的车道变化安全性，为碰撞风险评估而建立了人造潜在的现场模型。最后，通过在驱动模拟器上进行的人类在环实验中评估所提出的算法，结果证明了该方法的可行性和有效性。
In this paper, a human-like driving framework is designed for autonomous vehicles (AVs), which aims to make AVs better integrate into the transportation ecology of human driving and eliminate the misunderstanding and incompatibility of human drivers to autonomous driving. Based on the analysis of the real world INTERACTION dataset, a driving aggressiveness estimation model is established with the fuzzy inference approach. Then, a human-like driving model, which integrates the brain emotional learning circuit model (BELCM) with the two-point preview model, is designed. In the human-like lane-change decision-making algorithm, the cost function is designed comprehensively considering driving safety and travel efficiency. Based on the cost function and multi-constraint, the dynamic game algorithm is applied to modelling the interaction and decision making between AV and human driver. Additionally, to guarantee the lane-change safety of AVs, an artificial potential field model is built for collision risk assessment. Finally, the proposed algorithm is evaluated through human-in-the-loop experiments on a driving simulator, and the results demonstrated the feasibility and effectiveness of the proposed method.
