Enhancing Game-Theoretic Autonomous Car Racing Using Control Barrier Functions
Enhancing Game-Theoretic Autonomous Car Racing Using Control Barrier Functions
复制标题
使用控制屏障功能增强博弈论自动赛车
DOI：
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复制
发表时间：
2020
期刊：
影响因子：
--
通讯作者：
M. Egerstedt
中科院分区：
文献类型：
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作者：
Gennaro Notomista;Mingyu Wang;M. Schwager;M. Egerstedt
在本文中，我们考虑了一个双人赛车游戏，其中必须控制自动自我车辆与对手车辆进行比赛，对手车辆要么是自动驾驶的，要么是人类驾驶的。该方法基于灵敏度增强纳什均衡寻求（SENNA）方法，采用迭代最优响应算法对两车比赛中的轨迹进行优化。这种方法利用了自我和对手车辆之间的相互作用，这些相互作用通过避碰约束发生。这种博弈论的控制方法取决于自我车辆具有准确的模型和对对手车辆状态的正确认识。然而，当无法获得对手车辆的准确模型或对其状态的估计被噪声破坏时，该方法的性能可能会受到影响。出于这个原因，我们通过使用控制屏障函数强制允许鲁棒安全（PROST）条件来增强SENNA算法。目标是成功超车或保持在对手车辆的前面，即使后者的信息并不完全可用。塞纳和普罗斯特之间成功的协同作用——与两位同名f1车手之间引人注目的竞争相对立——通过广泛的模拟实验得到了证明。
In this paper, we consider a two-player racing game, where an autonomous ego vehicle has to be controlled to race against an opponent vehicle, which is either autonomous or human-driven. The approach to control the ego vehicle is based on a Sensitivity-ENhanced NAsh equilibrium seeking (SENNA) method, which uses an iterated best response algorithm in order to optimize for a trajectory in a two-car racing game. This method exploits the interactions between the ego and the opponent vehicle that take place through a collision avoidance constraint. This game-theoretic control method hinges on the ego vehicle having an accurate model and correct knowledge of the state of the opponent vehicle. However, when an accurate model for the opponent vehicle is not available, or the estimation of its state is corrupted by noise, the performance of the approach might be compromised. For this reason, we augment the SENNA algorithm by enforcing Permissive RObust SafeTy (PROST) conditions using control barrier functions. The objective is to successfully overtake or to remain in the front of the opponent vehicle, even when the information about the latter is not fully available. The successful synergy between SENNA and PROST—antithetical to the notable rivalry between the two namesake Formula 1 drivers—is demonstrated through extensive simulated experiments.
使用数据驱动模型的博弈论合作变道
DOI：
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发表时间：
2018
期刊：
Intelligent Robot Systems (IROS
影响因子：
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作者：
Ding, Guohui;Aghli, Sina;Heckman, Christoffer;Chen, Lijun
通讯作者：
Chen, Lijun
绿色选择：学习并影响人类在共享道路上的决策
DOI：
10.1109/cdc40024.2019.9030169
发表时间：
2019
期刊：
IEEE Conference on Decision and Control (CDC
影响因子：
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作者：
Biyik, Erdem;Lazar, Daniel A.;Sadigh, Dorsa;Pedarsani, Ramtin
通讯作者：
Pedarsani, Ramtin