To support the testing of AVs, CETRAN has created a guideline for the evaluation of complex multi agent test scenarios presented in this report. This allows for a clear structured manner in evaluating complexity elements based on the corresponding difficulties an AV might encounter in Singapore traffic. This study aims to understand the source of complexity for AVs from traffic hazard, by breaking down the difficulties on AV capabilities as perception, situation awareness and decision-making. Guidelines created through this study are composed by a list of elements to be considered in the future as selection criteria to evaluate complexity of scenarios to support AV behaviour assessment. This study is intended to be a guide to understand the sources of complexity for Avs and can be used to challenge the risk management ability of autonomous vehicles in a scenario-based test approach or traffic situations faced on road trials. The report includes the usage of the guidelines created as application to evaluate the complexity of a set of 5 real events that occur on Singapore roads from Resembler webtool which is a database of real human accidents/incidents. Four scenarios were also designed for creation in simulation by the CETRAN team, applying the guidelines for complexity elements created in this work, to illustrate the difficulties an ADS could experience with such scenarios.

翻译：为支持自动驾驶汽车（AV）测试，新加坡陆路交通管理局卓越研究与测试中心（CETRAN）制定了本报告所述的复杂多智能体测试场景评估指南。该指南提供了一种清晰结构化的方法，用于基于自动驾驶汽车在新加坡交通环境中可能遇到的相应困难来评估复杂性要素。本研究旨在通过从感知、态势感知和决策制定三个维度分解自动驾驶汽车能力所面临的困难，理解交通危险中自动驾驶汽车复杂性的来源。通过本研究制定的指南由一系列要素组成，这些要素未来将作为选择标准用于评估场景复杂性，以支持自动驾驶汽车行为评估。本研究旨在作为理解自动驾驶汽车复杂性来源的指导，可用于在基于场景的测试方法或实际道路试验中挑战自动驾驶汽车的风险管理能力。本报告还包括使用所制定的指南对Resembler网络工具（真实人类事故/事件数据库）中记录的5个新加坡道路真实事件进行复杂性评估的应用实例。CETRAN团队还依据本工作中创建的复杂性要素指南设计了四个仿真场景，以说明自动驾驶系统（ADS）在此类场景中可能遇到的困难。