This paper describes the methodology for building a dynamic risk assessment for ADAS (Advanced Driving Assistance Systems) algorithms in parking scenarios, fusing exterior and interior perception for a better understanding of the scene and a more comprehensive risk estimation. This includes the definition of a dynamic risk methodology that depends on the situation from inside and outside the vehicle, the creation of a multi-sensor dataset of risk assessment for ADAS benchmarking purposes, and a Local Dynamic Map (LDM) that fuses data from the exterior and interior of the car to build an LDM-based Dynamic Risk Assessment System (DRAS).

翻译：本文描述了面向停车场景中ADAS（高级驾驶辅助系统）算法构建动态风险评估的方法论，通过融合外部与内部感知实现对场景的更深入理解与更全面的风险估计。具体内容包括：定义依赖于车辆内外情境的动态风险方法论、创建用于ADAS基准测试的多传感器风险评估数据集，以及构建融合车辆内外数据的局部动态地图（LDM）——基于该地图的LDM动态风险评估系统（DRAS）。