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Evaluating Model Mismatch Impacting CACC Controllers in Mixed Traffic using a Driving Simulator

Abstract : 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.
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Submitted on : Thursday, July 19, 2018 - 12:26:40 PM
Last modification on : Thursday, August 16, 2018 - 4:27:21 PM
Long-term archiving on: : Saturday, October 20, 2018 - 5:16:32 PM

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Maytheewat Aramrattana, Raj Haresh Patel, Cristofer Englund, Jérôme Härri, Jonas Jansson, et al.. Evaluating Model Mismatch Impacting CACC Controllers in Mixed Traffic using a Driving Simulator. 29th IEEE Intelligent Vehicles Symposium (IV 2018), Jun 2018, Changshu, China. ⟨hal-01844399⟩

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