Search Results for author:
Found 5 papers, 0 papers with code
PEM: Perception Error Model for Virtual Testing of Autonomous Vehicles
In this article, we define Perception Error Models (PEM), a virtual simulation component that can enable the analysis of the impact of perception errors on AV safety, without the need to model the sensors themselves.
CoPEM: Cooperative Perception Error Models for Autonomous Driving
In this paper, we introduce the notion of Cooperative Perception Error Models (coPEMs) towards achieving an effective and efficient integration of V2X solutions within a virtual test environment.
ViSTA: a Framework for Virtual Scenario-based Testing of Autonomous Vehicles
In this paper, we present ViSTA, a framework for Virtual Scenario-based Testing of Autonomous Vehicles (AV), developed as part of the 2021 IEEE Autonomous Test Driving AI Test Challenge.
Vehicle Ego-Lane Estimation with Sensor Failure Modeling
We present a probabilistic ego-lane estimation algorithm for highway-like scenarios that is designed to increase the accuracy of the ego-lane estimate, which can be obtained relying only on a noisy line detector and tracker.
Modeling Perception Errors towards Robust Decision Making in Autonomous Vehicles
Sensing and Perception (S&P) is a crucial component of an autonomous system (such as a robot), especially when deployed in highly dynamic environments where it is required to react to unexpected situations.
