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).
@article{arxiv.2404.04040,
title = {Dynamic Risk Assessment Methodology with an LDM-based System for Parking Scenarios},
author = {Paola Natalia Cañas and Mikel García and Nerea Aranjuelo and Marcos Nieto and Aitor Iglesias and Igor Rodríguez},
journal= {arXiv preprint arXiv:2404.04040},
year = {2024}
}