Considering that human-driven vehicles and autonomous vehicles (AVs) will coexist on roads in the future for a long time, how to merge AVs into human drivers traffic ecology and minimize the effect of AVs and their misfit with human drivers, are issues worthy of consideration. Moreover, different passengers have different needs for AVs, thus, how to provide personalized choices for different passengers is another issue for AVs. Therefore, a human-like decision making framework is designed for AVs in this paper. Different driving styles and social interaction characteristics are formulated for AVs regarding driving safety, ride comfort and travel efficiency, which are considered in the modeling process of decision making. Then, Nash equilibrium and Stackelberg game theory are applied to the noncooperative decision making. In addition, potential field method and model predictive control (MPC) are combined to deal with the motion prediction and planning for AVs, which provides predicted motion information for the decision-making module. Finally, two typical testing scenarios of lane change, i.e., merging and overtaking, are carried out to evaluate the feasibility and effectiveness of the proposed decision-making framework considering different human-like behaviors. Testing results indicate that both the two game theoretic approaches can provide reasonable human-like decision making for AVs. Compared with the Nash equilibrium approach, under the normal driving style, the cost value of decision making using the Stackelberg game theoretic approach is reduced by over 20%.

中文翻译：

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自动驾驶的类人决策：非合作博弈论方法

考虑到未来人类驾驶汽车和自动驾驶汽车（AVs）将在道路上长期共存，如何将AVs融入人类驾驶员的交通生态，最大限度地减少AVs的影响及其与人类驾驶员的不匹配，是值得研究的问题考虑。此外，不同的乘客对自动驾驶汽车的需求也不同，因此如何为不同的乘客提供个性化的选择是自动驾驶汽车面临的另一个问题。因此，本文为自动驾驶汽车设计了一个类人决策框架。自动驾驶汽车针对驾驶安全性、乘坐舒适性和出行效率制定了不同的驾驶风格和社交互动特征，并在决策建模过程中加以考虑。然后，将纳什均衡和斯塔克尔伯格博弈理论应用于非合作决策。此外，结合势场法和模型预测控制（MPC）来处理自动驾驶汽车的运动预测和规划，为决策模块提供预测的运动信息。最后，通过合并和超车两种典型的车道变换测试场景，评估所提出的决策框架考虑不同类人行为的可行性和有效性。测试结果表明，这两种博弈论方法都可以为自动驾驶汽车提供合理的类人决策。与纳什均衡方法相比，在正常驾驶方式下，使用 Stackelberg 博弈论方法的决策成本值降低了 20% 以上。进行了两个典型的车道变换测试场景，即合并和超车，以评估考虑不同类人行为的所提出的决策框架的可行性和有效性。测试结果表明，这两种博弈论方法都可以为自动驾驶汽车提供合理的类人决策。与纳什均衡方法相比，在正常驾驶方式下，使用 Stackelberg 博弈论方法的决策成本值降低了 20% 以上。进行了两个典型的车道变换测试场景，即合并和超车，以评估考虑不同类人行为的所提出的决策框架的可行性和有效性。测试结果表明，这两种博弈论方法都可以为自动驾驶汽车提供合理的类人决策。与纳什均衡方法相比，在正常驾驶方式下，使用 Stackelberg 博弈论方法的决策成本值降低了 20% 以上。