Evaluating Model Mismatch Impacting CACC Controllers in Mixed

• 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.

Ämnesord

TEKNIK OCH TEKNOLOGIER  -- Maskinteknik -- Farkostteknik (hsv//swe)

ENGINEERING AND TECHNOLOGY  -- Mechanical Engineering -- Vehicle Engineering (hsv//eng)

Nyckelord

Behavioral research

Intelligent vehicle highway systems

Model predictive control

Predictive control systems

Vehicles

Automated vehicles

Control inputs

Driving simulator

Evaluating models

Human behaviors

Market penetration

Proof of concept

Transportation system

Controllers

914 Road: ITS och vehicle technology

841 Road: Road user behaviour

Publikations- och innehållstyp

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