Intelligent-driving technologies play crucial roles in reducing road-traffic accidents and ensuring more convenience while driving. One of the significant challenges in developing an intelligent vehicle is how to operate it safely without causing fear in other human drivers. This paper presents a new behavioral decision-making model to achieve both safety and high efficiency and also to reduce the adverse effect of autonomous vehicles on the other road users while driving. Moreover, we attempt to adapt the model for human drivers so that users can understand, adapt, and utilize intelligent-driving technologies. Furthermore, this paper proposes a combined spring model for assessing driving risk. Thus, we analyze some driving characteristics of drivers and choose “safety” and “high efficiency” as the two main factors that are pursued by drivers while driving. Based on the principle of least action, a multiobjective optimization cost function is established for the decision-making model. Finally, we design six unprotected left-turn scenarios at a T-intersection and three unprotected left-turn scenarios at a standard two-lane intersection for applying simulation algorithm and provide a decision-making map for developing intelligent-driving technologies. Based on the principle of least action, this paper demonstrates that optimization theory can give insight into drivers’ behavior and can also contribute to the development of intelligent-driving algorithms. The experimental results reveal that the behavioral decision-making model can always avoid collision accidents on the premise of ensuring certain efficiency, and it can achieve 97.01%, 94.52%, 96.67%, 91.18%, 101.27%, 83.33%, 102.94%, 103.03%, and 105.77% of time to intersection's maximum pass rate in the considered nine scenarios.

中文翻译：

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基于驾驶风险评估的智能汽车行为决策模型

智能驾驶技术在减少道路交通事故和确保驾驶时更加便利方面发挥着至关重要的作用。开发智能车辆的重大挑战之一是如何安全地操作它而不会引起其他人类驾驶员的恐惧。本文提出了一种新的行为决策模型，以实现安全和高效，并减少自动驾驶汽车在驾驶时对其他道路使用者的不利影响。此外，我们尝试为人类驾驶员调整模型，以便用户能够理解、适应和利用智能驾驶技术。此外，本文提出了一种用于评估驾驶风险的组合弹簧模型。因此，我们分析了驾驶员的一些驾驶特性，选择“安全”和“高效”作为驾驶员在驾驶时所追求的两个主要因素。基于最小作用量原则，为决策模型建立了多目标优化成本函数。最后，我们设计了六个丁字路口无保护左转场景和三个标准双车道交叉路口无保护左转场景，用于应用仿真算法，并为开发智能驾驶技术提供决策图。本文基于最小作用量原理，论证了优化理论可以深入了解驾驶员的行为，也有助于智能驾驶算法的发展。