CARJAN实现城市交通场景的智能交互式生成与仿真
CARJAN: Agent-Based Generation and Simulation of Traffic Scenarios with AJAN
- 基于AJAN框架与CARLA模拟器，支持多类型智能体建模
- 通过SPARQL行为树实现动态场景中智能体决策与交互
- 提供可视化界面，便于交通场景的创建与维护
在城市交通场景中，针对行人、骑行者和自动驾驶车辆等不同类型的交互智能体进行友好建模与虚拟仿真仍具挑战。本文提出CARJAN，一种基于多智能体工程框架AJAN与驾驶模拟器CARLA的半自动化交通场景生成与仿真工具。CARJAN提供可视化用户界面，用于交通场景布局的建模、存储与维护，并在CARLA中利用基于SPARQL的行为树实现智能体在动态场景中的决策与交互。该工具首次实现了在CARLA中对交互式、智能型交通场景的集成化生成与仿真。
原文摘要 · Abstract (English)
User-friendly modeling and virtual simulation of urban traffic scenarios with different types of interacting agents such as pedestrians, cyclists and autonomous vehicles remains a challenge. We present CARJAN, a novel tool for semi-automated generation and simulation of such scenarios based on the multi-agent engineering framework AJAN and the driving simulator CARLA. CARJAN provides a visual user interface for the modeling, storage and maintenance of traffic scenario layouts, and leverages SPARQL Behavior Tree-based decision-making and interactions for agents in dynamic scenario simulations in CARLA. CARJAN provides a first integrated approach for interactive, intelligent agent-based generation and simulation of virtual traffic scenarios in CARLA.
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