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arXiv 1709.02557cs.LOcs.MA
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一个控制自动驾驶汽车的理性主体:实现与形式化验证

A Rational Agent Controlling an Autonomous Vehicle: Implementation and Formal Verification

Lucas E. R. Fernandes, Vinicius Custodio, Gleifer V. Alves, Michael Fisher

摘要:

在我们的道路上开发和部署自动驾驶汽车(AV)不仅在不久的将来是现实的,而且还可以带来显着的好处。特别是,它可以潜在地解决与车辆和交通有关的几个问题,例如:(i)可能减少交通拥堵,从而提高燃油经济性和减少驾驶员不活动;(ii)可能减少事故数量,假设AV可以最小化经常导致交通事故的人为错误;以及(iii)增加停车的便利性,特别是当人们考虑到共享AV的潜力时。为了部署AV,必须在硬件和软件方面完成一些重要步骤。正如预期的那样,软件组件在复杂的AV系统中起着关键作用,因此,至少为了安全起见,我们应该评估这些组件的正确性。在本文中,我们关注的是负责AV中的决策的高级软件组件。我们打算建立一个AV能够导航,避障,障碍物选择(当碰撞是不可避免的)和车辆恢复等,使用理性的代理。为了实现这一目标,我们建立了以下阶段。首先,代理计划和操作已在Gwendolen代理程序设计语言中实现。第二,我们用Java语言构建了一个模拟的汽车环境。第三,我们通过LTL公式正式指定了一些所需的代理属性,然后使用AJPF验证工具对其进行正式验证。最后,在MCAPL框架内(包括前几个阶段使用的所有工具),我们已经获得了我们的AV代理在其特定行为方面的形式化验证。例如,负责选择具有低潜在损害的障碍物而不是较高损害障碍物(如果可能)的代理计划可以在MCAPL内正式验证。我们必须强调的是,我们目前的方法的主要目标在于代理计划的形式化验证,而不是评估现实世界的应用。出于这个原因,我们使用了一个简单的矩阵表示关于我们的代理所使用的环境。

英文摘要:

The development and deployment of Autonomous Vehicles (AVs) on our roads is not only realistic in the near future but can also bring significant benefits. In particular, it can potentially solve several problems relating to vehicles and traffic, for instance: (i) possible reduction of traffic congestion, with the consequence of improved fuel economy and reduced driver inactivity; (ii) possible reduction in the number of accidents, assuming that an AV can minimise the human errors that often cause traffic accidents; and (iii) increased ease of parking, especially when one considers the potential for shared AVs. In order to deploy an AV there are significant steps that must be completed in terms of hardware and software. As expected, software components play a key role in the complex AV system and so, at least for safety, we should assess the correctness of these components. In this paper, we are concerned with the high-level software component(s) responsible for the decisions in an AV. We intend to model an AV capable of navigation; obstacle avoidance; obstacle selection (when a crash is unavoidable) and vehicle recovery, etc, using a rational agent. To achieve this, we have established the following stages. First, the agent plans and actions have been implemented within the Gwendolen agent programming language. Second, we have built a simulated automotive environment in the Java language. Third, we have formally specified some of the required agent properties through LTL formulae, which are then formally verified with the AJPF verification tool. Finally, within the MCAPL framework (which comprises all the tools used in previous stages) we have obtained formal verification of our AV agent in terms of its specific behaviours. For example, the agent plans responsible for selecting an obstacle with low potential damage, instead of a higher damage obstacle (when possible) can be formally verified within MCAPL. We must emphasise that the major goal (of our present approach) lies in the formal verification of agent plans, rather than evaluating real-world applications. For this reason we utilised a simple matrix representation concerning the environment used by our agent.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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