# Dynamic Risk Assessment Methodology with an LDM-based System for Parking Scenarios

arXiv: https://arxiv.org/abs/2404.04040 · HTML: https://mlsanity.com/paper/2404.04040/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 18 references that are in the map.
- First posted on arXiv 5 Apr 2024.

## Builds on (most cited first, 3 of 3)

- [nuScenes: A multimodal dataset for autonomous driving](https://mlsanity.com/paper/1903.11027/) (2019)
- [Center-based 3D Object Detection and Tracking](https://mlsanity.com/paper/2006.11275/) (2020)
- [6-Layer Model for a Structured Description and Categorization of Urban Traffic and Environment](https://mlsanity.com/paper/2012.06319/) (2020)

## Built on by (most cited first, 2 of 2)

- [LSM: A Comprehensive Metric for Assessing the Safety of Lane Detection Systems in Autonomous Driving](https://mlsanity.com/paper/2407.07740/) (2024)
- [Scenario-Independent Criticality Assessment and Prediction for Vulnerable Road Users in Autonomous Driving](https://mlsanity.com/paper/2609.11947/) (2026)

## Abstract (arXiv)

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).