提出一套高效生成自动驾驶仿真地图的流程，降低开发门槛。
A Workflow for Map Creation in Autonomous Vehicle Simulations
- 设计专用工作流，简化3D地图生成步骤
- 成功构建安大略理工学院停车场的高精度3D地图
- 可扩展性强，适合多仿真平台使用
自动驾驶技术快速发展，推动了对大规模仿真测试的需求。精准且可适配的地图是定位、路径规划和场景测试的基础。然而，为CARLA等仿真器创建可用地图常需大量计算资源，现有流程灵活性差。本文提出一种定制化工作流，显著简化地图生成过程，并以安大略理工学院停车场为例，成功构建了3D仿真地图。未来将集成SLAM技术，优化跨平台兼容性，并改进经纬度处理机制，进一步提升地图生成精度。
原文摘要 · Abstract (English)
The fast development of technology and artificial intelligence has significantly advanced Autonomous Vehicle (AV) research, emphasizing the need for extensive simulation testing. Accurate and adaptable maps are critical in AV development, serving as the foundation for localization, path planning, and scenario testing. However, creating simulation-ready maps is often difficult and resource-intensive, especially with simulators like CARLA (CAR Learning to Act). Many existing workflows require significant computational resources or rely on specific simulators, limiting flexibility for developers. This paper presents a custom workflow to streamline map creation for AV development, demonstrated through the generation of a 3D map of a parking lot at Ontario Tech University. Future work will focus on incorporating SLAM technologies, optimizing the workflow for broader simulator compatibility, and exploring more flexible handling of latitude and longitude values to enhance map generation accuracy.
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