论文标题
在交互式行为计划中建模和测试多代理流量规则
Modeling and Testing Multi-Agent Traffic Rules within Interactive Behavior Planning
论文作者
论文摘要
自动驾驶汽车需要遵守与人类遵循的相同规则。这些流量规则中的一些可能取决于多个代理或时间。尤其是在与交互密集的交通参与者的情况下，在计划期间需要考虑与其他代理商的互动。为了研究如何建模多机构和时间依赖的交通规则，需要一个框架将行为限制为规划期间规则符合规则的行动，并最终可以评估这些规则的满意度。这项工作提出了一种模拟互动行为计划的交通规则并测试交通规则对碰撞，进度或规则违规等指标的分析的方法。交互式行为计划问题被提出为动态游戏，并使用蒙特卡洛树搜索解决，为此我们为将与历史有关的流量规则集成到决策树中的新方法。为了研究规则的效果，我们将其视为一个多目标问题，并将放松的词典订购应用于矢量奖励。我们在合并方案中演示了我们的方法。我们评估建模和将流量规则结合到模拟中最终合规性的效果。我们表明，通过我们的方法，可以实现互动行为计划，同时可以实现复杂的交通规则。向前迈进，这为我们提供了一个通用框架，以正式化自动驾驶汽车的交通规则。
Autonomous vehicles need to abide by the same rules that humans follow. Some of these traffic rules may depend on multiple agents or time. Especially in situations with traffic participants that interact densely, the interactions with other agents need to be accounted for during planning. To study how multi-agent and time-dependent traffic rules shall be modeled, a framework is needed that restricts the behavior to rule-conformant actions during planning, and that can eventually evaluate the satisfaction of these rules. This work presents a method to model the conformance to traffic rules for interactive behavior planning and to test the ramifications of the traffic rule formulations on metrics such as collision, progress, or rule violations. The interactive behavior planning problem is formulated as a dynamic game and solved using Monte Carlo Tree Search, for which we contribute a new method to integrate history-dependent traffic rules into a decision tree. To study the effect of the rules, we treat it as a multi-objective problem and apply a relaxed lexicographical ordering to the vectorized rewards. We demonstrate our approach in a merging scenario. We evaluate the effect of modeling and combining traffic rules to the eventual compliance in simulation. We show that with our approach, interactive behavior planning while satisfying even complex traffic rules can be achieved. Moving forward, this gives us a generic framework to formalize traffic rules for autonomous vehicles.
