Employing Severity of Injury to Contextualize Complex Risk Mitigation Scenarios - Archive ouverte HAL Access content directly
Conference Papers Year :

Employing Severity of Injury to Contextualize Complex Risk Mitigation Scenarios

(1) , (1) , (1) , (2) , (3) , (3)
1
2
3

Abstract

Risk mitigation is an important element to consider in risk evaluation. Safety features have helped to decrease the death ratio over the years. However, to date, each driver assistance system works on a single domain of operation. The problem remains in how to use perception to contextualize the scene to fully minimize the collision severity in a complex emergency scenario. Up to now, works on cost maps have consider simple contextualized object in mitigation scenarios. For instance, the use of binary allowed/forbidden zones or, a fixed weight to each type of object in the scene. Our work employs the risk of injury issued by accidentology to each class of object present in the scene. Each class of object presents an injury probability with respect to the impact speed and ethical/economical/political factors. The method generates a cost map containing a collision probability along with to the risk of injury. It dynamically contextualizes the objects, since the risk of injury depends on the characteristics of the scene. Simulation and dataset results validate that changing the referred parameters alters the context and evaluation of the scene. Then, the proposed method allows a better assessment of the surroundings by creating a dynamic navigation cost map for complex scenarios.
Fichier principal
Vignette du fichier
paperRiskAssessment_IV2020.pdf (2.96 Mo) Télécharger le fichier
Origin : Files produced by the author(s)
Loading...

Dates and versions

hal-02945325 , version 1 (22-09-2020)

Identifiers

  • HAL Id : hal-02945325 , version 1

Cite

Luiz Alberto Serafim Guardini, Anne Spalanzani, Christian Laugier, Philippe Martinet, Anh-Lam Do, et al.. Employing Severity of Injury to Contextualize Complex Risk Mitigation Scenarios. IV 2020 – 31st IEEE Intelligent Vehicles Symposium, Oct 2020, Las Vegas / Virtual, United States. pp.1-7. ⟨hal-02945325⟩
97 View
198 Download

Share

Gmail Facebook Twitter LinkedIn More