Artificial potential fields (APF) and reinforcement learning (RL) are two common methods for the intelligent decision of autonomous vehicles. The process of vehicle driving includes the constraints of vehicle dynamics, traffic rules, road conditions, and other traffic vehicles, which are quite complex. The existing APF methods perform inadequately since they consider only limited factors and their effects. As such, it is difficult to adapt to increasingly complex traffic environments. In this paper, we propose a new concept, compound traffic field (CTF). The concept makes use of field theory to model various traffic environments based on the physical properties and traffic rules, besides, introduces the concept of the force correction field to reveal the interaction between the vehicle and the surrounding environment during driving. Moreover, an intelligent decision method and a co-simulation platform are established based on combining RL and CTF. The method has passed the tests in various scenarios built by PreScan and compared with the Conventional APF and modeless algorithm. For solving intelligent decision problems in the complex environment provides an applicable field model and its application method.

人工势场（APF）和强化学习（RL）是自动驾驶汽车智能决策的两种常用方法。车辆行驶的过程包括车辆动力学、交通规则、路况等交通车辆的约束，是相当复杂的。现有的 APF 方法表现不佳，因为它们只考虑了有限的因素及其影响。因此，很难适应日益复杂的交通环境。在本文中，我们提出了一个新概念，复合交通场（CTF）。该概念利用场理论根据物理特性和交通规则对各种交通环境进行建模，并引入力修正场的概念来揭示车辆在行驶过程中与周围环境的相互作用。而且，基于RL和CTF相结合的智能决策方法和协同仿真平台建立。该方法通过了PreScan构建的各种场景的测试，并与传统的APF和无模式算法进行了比较。为解决复杂环境下的智能决策问题提供了适用的领域模型及其应用方法。