Adapted Risk Indicator For Autonomous Driving System With Uncertainties and Multi-Dimensional Configurations Modeling
Résumé
This paper addresses the proposal of a new multidimensional risk estimator using spatial uncertainty modeling for automated vehicles risk assessment. This risk estimator, on which relies AV decision-making, is based on an extended version of the distance of Gruyer (DG). This estimator provides an answer and a solution to the risk assessment needed as a part of a generic and extended architecture dedicated to the building of a generic driving meta-model usable for multimodal driving behavior simulation (personal vehicle, connected vehicle, connected and automated vehicles and autonomous vehicle). The proposed estimators have been tested, evaluated, and analyzed on a set of representative highway scenarios with three key performance indicators. Results show that the proposed risk estimator (RIMUM) is more realistic, extended DG more reversible. Future works will concentrate on improving their prediction capacity which is lower than reference estimators.
Mots clés
Considerable Uncertainty
Probabilistic Method
Vehicle Type
Multidimensional Model
Obstacle Avoidance
Vehicle Dynamics
Collision Probability
Time Headway
Definition Of Risk
Dynamic Bayesian Network
Global Architecture
Fatality Risk
Collision Risk
Emergency Braking
Roundabout
Advanced Driver Assistance Systems
Covariance Matrix
Autonomous Vehicles
Autonomous Driving Systems
Multidimensional Configuration
Risk Assessment
Risk Estimates
Model Uncertainty
Predictive Capacity
Key Performance Indicators
Automated Vehicles
Dynamic Relationship
Probability Function
Probability Estimates
Severe Risk