混合交通中自动驾驶车辆的图式合作策略

Maximilian Flormann, Roman Henze
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引用次数: 0

摘要

在城市智能交通的背景下,车辆必须相互通信、与周围的基础设施和其他交通参与者通信。通过使用 Vehicle2X 通信,可以交换车辆的位置、驾驶动态数据或驾驶意图。这一概念可用于城市环境中的合作驾驶。基于当前的 V2X 通信标准,介绍了一种在混合交通场景中自动驾驶车辆合作驾驶的方法。起初,所有通信参与者都会通报各自的动态数据和计划轨迹,并据此计算出优先级。因此,引入了一种分散式合作算法。该算法的方法是将每个交通场景转换为有向图,在此基础上通过优化算法计算出合作问题的解决方案。该解决方案可以由不同的交通参与者分散计算,他们共享并比较各自的解决方案,以获得最优方案;也可以由单一计算单元(如智能基础设施系统)集中计算。由于通信协议在设计上不需要任何握手,因此合作参与者通过类似链式验证的方法协商合作驾驶操作。最后,所有合作参与者执行经过优化和协商的合作驾驶操作。所提出的算法在多车模拟中得到了验证。比较了从传统方法到机器学习算法等不同的优化启发式方法。根据有代表性的场景目录,评估了这些方法在模型复杂度增加时的表现。最后,在现实世界的试验场测试中对算法进行了验证。这些验证结果表明,与传统的基础设施控制方法相比,所引入的方法能提供更有效的合作策略。此外,所介绍的方法在设计上是无冲突的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Graph based Cooperation Strategies for Automated Vehicles in Mixed Traffic
In the context of urban smart mobility, vehicles have to communicate with each other, surrounding infrastructure, and other traffic participants. By using Vehicle2X communication, it is possible to exchange the vehicles’ position, driving dynamics data, or driving intention. This concept yields the use for cooperative driving in urban environments. Based on current V2X-communication standards, a methodology for cooperative driving of automated vehicles in mixed traffic scenarios is presented. Initially, all communication participants communicate their dynamic data and planned trajectory, based on which a prioritization is calculated. Therefore, a decentralized cooperation algorithm is introduced. The approach of this algorithm is that every traffic scenario is translatable to a directed graph, based in which a solution for the cooperation problem is computed via an optimization algorithm. This solution is either computed decentralized by various traffic participants, who share and compare their solutions in order to get an optimal one, or centralized by a single computation unit, such as smart infrastructure systems. The cooperation participants negotiate the cooperative driving maneuver via a chain like validation approach, since the communication protocol does not require any handshake by design. Finally, all cooperation participants carry out the optimized and negotiated cooperative driving maneuver. The presented algorithm is validated in a multi-vehicle simulation. Different optimization heuristics are compared, ranging from traditional approaches to machine learning algorithms. The methods' behavior with regard to increasing model complexities is evaluated based on a representative catalogue of scenarios. Finally, the algorithm is validated in a real world proving ground test. These validations show that the introduced methodology provides significantly more efficient cooperation strategies compared to traditional, infrastructure-controlled approaches. Additionally, the presented approach is conflict-free by design.
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