This paper describes Ariel Team's autonomous racing controller for the Indy Autonomous Challenge (IAC) simulation race \cite{INDY}. IAC is the first multi-vehicle autonomous head-to-head competition, reaching speeds of 300 km/h along an oval track, modeled after the Indianapolis Motor Speedway (IMS). Our racing controller attempts to maximize progress along the track while avoiding collisions with opponent vehicles and obeying the race rules. To this end, the racing controller first computes a race line offline. Then, it repeatedly computes online a small set of dynamically feasible maneuver candidates, each tested for collision with the opponent vehicles. Finally, it selects the maneuver that maximizes progress along the track, taking into account the race line. The maneuver candidates, as well as the predicted trajectories of the opponent vehicles, are approximated using a point mass model. Despite the simplicity of this racing controller, it managed to drive competitively and with no collision with any of the opponent vehicles in the IAC final simulation race.
翻译:本文描述Ariel团队在印地安自治挑战(IAC)模拟赛赛中的自动赛跑控制器。 IAC是第一个在奥瓦尔赛道上达到每小时300公里/小时速度的多车辆自动头对头比赛,以印第安纳波利斯汽车赛道(IMS)为模型。我们的赛跑控制器试图在赛道上取得最大进展,同时避免与对手车辆的碰撞并遵守种族规则。为此,赛跑控制器首先计算出一条离线的赛线。然后,它反复在网上计算出一小组动态可行的机动选手,每个选手都经过与对手车辆碰撞的测试。最后,它选择了在赛道上取得最大进展的动作,同时考虑到赛道。机动选手以及对手车辆的预计轨迹都使用点质量模型进行近似。 尽管赛车控制器很简单,但它设法在最后模拟赛道中以竞争方式驾驶,没有与任何对手车辆碰撞。