Dynamic Risk Assessment Methodology with an LDM-based System for Parking Scenarios
This addresses safety improvements for ADAS users in parking, but it is incremental as it builds on existing risk assessment methods with sensor fusion.
The paper tackles dynamic risk assessment for ADAS in parking scenarios by fusing exterior and interior perception, resulting in a more comprehensive risk estimation system and a multi-sensor dataset for benchmarking.
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).