With the development of autonomous driving, it is becoming increasingly common for autonomous vehicles (AVs) and human-driven vehicles (HVs) to travel on the same roads. Existing single-vehicle planning algorithms on board struggle to handle sophisticated social interactions in the real world. Decisions made by these methods are difficult to understand for humans, raising the risk of crashes and making them unlikely to be applied in practice. Moreover, vehicle flows produced by open-source traffic simulators suffer from being overly conservative and lacking behavioral diversity. We propose a hierarchical multi-vehicle decision-making and planning framework with several advantages. The framework jointly makes decisions for all vehicles within the flow and reacts promptly to the dynamic environment through a high-frequency planning module. The decision module produces interpretable action sequences that can explicitly communicate self-intent to the surrounding HVs. We also present the cooperation factor and trajectory weight set, bringing diversity to autonomous vehicles in traffic at both the social and individual levels. The superiority of our proposed framework is validated through experiments with multiple scenarios, and the diverse behaviors in the generated vehicle trajectories are demonstrated through closed-loop simulations.

翻译：随着自动驾驶技术的发展，自动驾驶车辆与人类驾驶车辆在同一条道路上行驶的情况日益普遍。现有的车载单车辆规划算法难以应对真实世界中复杂的社交交互。这些方法做出的决策对人类来说难以理解，增加了碰撞风险，且难以在实际中应用。此外，开源交通模拟器生成的车流存在过于保守且缺乏行为多样性的问题。我们提出了一种具有多重优势的分层多车决策与规划框架。该框架联合为车流中的所有车辆做出决策，并通过高频规划模块对动态环境做出及时响应。决策模块生成可解释的动作序列，能够明确地向周边人类驾驶车辆传达自身意图。我们还提出了协作因子和轨迹权重集，在社会层面和个体层面为交通中的自动驾驶车辆带来多样性。通过多场景实验验证了所提出框架的优越性，并通过闭环仿真展示了生成车辆轨迹中的多样化行为。