At early market penetration, automated vehicles will share the road with legacy vehicles. For a safe transportation system, automated vehicle controllers therefore need to estimate the behavior of the legacy vehicles. However, mismatches between the estimated and real human behaviors can lead to inefficient control inputs, and even collisions in the worst case. In this paper, we propose a framework for evaluating the impact of model mismatch by interfacing a controller under test with a driving simulator. As a proof- of-concept, an algorithm based on Model Predictive Control (MPC) is evaluated in a braking scenario. We show how model mismatch between estimated and real human behavior can lead to a decrease in avoided collisions by almost 46%, and an increase in discomfort by almost 91%. Model mismatch is therefore non-negligible and the proposed framework is a unique method to evaluate them.
Evaluating Model Mismatch Impacting CACC Controllers in Mixed
2018 IEEE Intelligent Vehicles Symposium (IV) ; 1867-1872
01.06.2018
582567 byte
Aufsatz (Konferenz)
Elektronische Ressource
Englisch
EVALUATING MODEL MISMATCH IMPACTING CACC CONTROLLERS IN MIXED TRAFFIC USING A DRIVING SIMULATOR
British Library Conference Proceedings | 2018
|Multi-Model Adaptive Control for CACC Applications
IEEE | 2021
|Stability of CACC-manual heterogeneous vehicular flow with partial CACC performance degrading
Taylor & Francis Verlag | 2019
|A Modular CACC System Integration and Design
Online Contents | 2012
|CACC fleet cooperative lane changing control method in mixed traffic flow environment
Europäisches Patentamt | 2025
|