Predictive Braking With Brake Light Detection-Field Test

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A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
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Date

2022

Department

Department of Energy and Mechanical Engineering

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Mcode

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Language

en

Pages

10

Series

IEEE Access, Volume 10, pp. 49771-49780

Abstract

Driver assistance systems, such as adaptive cruise control, are an increasing commodity in modern vehicles. Our earlier experience of radar-based adaptive cruise control has indicated repeatable abrupt behavior when approaching a stopped vehicle at high speed, which is typical for extra-urban roads. Abrupt behavior in assisted driving not only decreases the passenger trust but also reduces the comfort levels of such systems. We present a design and proof-of-concept of a machine vision-enhanced adaptive cruise controller. A machine vision-based brake light detection system was implemented and tested in order to smoothen the transition from coasting to braking and ensure speed reduction early enough. The machine vision system detects the brake lights in front, then transmits a command to the cruise controller to reduce speed. The current paper reports the speed control system design and experiments carried out to validate the system. The experiments showed the system works as designed by reducing abrupt behavior. Measurements show that brake light-assisted cruise control was able to start deceleration about three seconds earlier than a cruise controller without brake light detection. Measurements also showed increased ride comfort with the maximum deceleration and minimum jerk levels improving from 5% to 31%.

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Keywords

Brakes, Control systems, Safety, Cruise control, Radar, Detection algorithms, Cameras, Advanced driver assistance systems, automotive applications, intelligent vehicles, machine vision, MODEL, ALGORITHM, SYSTEM

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DOI

10.1109/ACCESS.2022.3173416

Citation

Pirhonen, J, Ojala, R, Kivekas, K & Tammi, K 2022, 'Predictive Braking With Brake Light Detection-Field Test', IEEE Access, vol. 10, pp. 49771-49780. https://doi.org/10.1109/ACCESS.2022.3173416

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https://urn.fi/URN:NBN:fi:aalto-202206083603

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[article-cris] Insinööritieteiden korkeakoulu / ENG