AIBA：自动驾驶行为仲裁的AI模型
AIBA: An AI Model for Behavior Arbitration in Autonomous Driving
摘要：
在动态变化的交通中驾驶对于自动驾驶汽车来说是一项极具挑战性的任务，尤其是在拥挤的城市道路上。无人驾驶汽车的人工智能（AI）系统必须能够在不同的驾驶策略之间进行仲裁，以便根据可理解的交通场景模型正确规划汽车的路径。提出了一种用于自动驾驶的人工智能行为仲裁算法。该方法被称为AIBA（AI行为仲裁），分为两个阶段：（i）人类驾驶场景描述和理解以及（ii）形式化建模。场景的描述是通过模仿人类认知模型来实现的，而建模部分是基于一种近似于人类驾驶员理解过程的形式化表示。形式化表示的优点是系统的功能安全性可以通过分析来推断。该算法的性能进行了评估虚拟试驾（VTD），一个全面的交通模拟器，并在GridSim，车辆运动学引擎的原型。
英文摘要：
Driving in dynamically changing traffic is a highly challenging task for autonomous vehicles, especially in crowded urban roadways. The Artificial Intelligence (AI) system of a driverless car must be able to arbitrate between different driving strategies in order to properly plan the car's path, based on an understandable traffic scene model. In this paper, an AI behavior arbitration algorithm for Autonomous Driving (AD) is proposed. The method, coined AIBA (AI Behavior Arbitration), has been developed in two stages: (i) human driving scene description and understanding and (ii) formal modelling. The description of the scene is achieved by mimicking a human cognition model, while the modelling part is based on a formal representation which approximates the human driver understanding process. The advantage of the formal representation is that the functional safety of the system can be analytically inferred. The performance of the algorithm has been evaluated in Virtual Test Drive (VTD), a comprehensive traffic simulator, and in GridSim, a vehicle kinematics engine for prototypes.
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