Autonomous overtaking at high speeds is a challenging multi-agent robotics research problem. The high-speed and close proximity situations that arise in multi-agent autonomous racing require designing algorithms that trade off aggressive overtaking maneuvers and minimize the risk of collision with the opponent. In this paper, we study a special case of multi-agent autonomous race, called the head-to-head autonomous race, that requires two racecars with similar performance envelopes. We present a mathematical formulation of an overtake and position defense in this head-to-head autonomous racing scenario, and we introduce the Automaton Referencing Guided Overtake System (ARGOS) framework that supervises the execution of an overtake or position defense maneuver depending on the current role of the racecar. The ARGOS framework works by decomposing complex overtake and position-defense maneuvers into sequential and temporal submaneuvers that are individually managed and supervised by a network of automatons. We verify the properties of the ARGOS framework using model-checking and demonstrate results from multiple simulations, which show that the framework meets the desired specifications. The ARGOS framework performs similar to what can be observed from real-world human-driven motor sport racing.

翻译：摘要：高速条件下的自动驾驶超车是一个具有挑战性的多智能体机器人研究问题。多智能体自动驾驶赛车中产生的高速与近距离接触场景，要求设计在激进超车策略与最小化碰撞风险之间取得平衡的算法。本文研究了多智能体赛车的一个特例——头对头自动驾驶赛车，该场景需要两辆性能相近的赛车进行对抗。我们提出了头对头自动驾驶赛车场景中超车与位置防守的数学建模，并介绍了自动机引用引导超车系统（ARGOS）框架，该框架根据赛车的当前角色监督超车或位置防守策略的执行。ARGOS框架通过将复杂的超车与位置防守机动分解为序列化与时序化的子机动，由自动机网络分别管理与监督。我们使用模型检验验证了ARGOS框架的特性，并通过多次仿真实验展示了该框架满足预期规范。ARGOS框架的表现与真实世界中人类驾驶的赛车运动可观察到的结果具有高度相似性。