Adaptive Cruise Control tuned by Genetic Algorithm for Safe Distance of Automated Vehicle

Dhidik Prastiyanto,Esa Apriaskar, Rosdiazli Ibrahim, A Rumanda, A A Manaf, Ismayani Amelia

IOP conference series(2023)

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Abstract The importance of safety in transportation has forced developments in automated vehicles. Many control methods have been introduced to ensure safe distance between automated vehicle and another car. This article presents an adaptive cruise control whose parameters are tuned by genetic algorithm for the case study of safe distance in automated vehicle. Two genetic algorithms with two different objective functions, i.e., integral square error (ISE) and integral absolute error (IAE), were evaluated. The results show that genetic algorithms could work well using both functions indicated by the obtained best fitness values through generations. The optimal controller parameter obtained by them also seems to be similar each other, including the distance, velocity and acceleration response of the automated vehicle. It is only the standard deviation that differs them. After 20 running tests, IAE produced 0.0288, 0.0439, and 0.0134 in standard deviation for verr_gain, xerr_gain, vx_gain, respectively, while ISE got 0.0169, 0.0755, and 0.0101. From the total values of standard deviation, we can conclude that GA with IAE performs slightly better since it has smaller value indicating that it has better repeatability.
adaptive cruise control,automated vehicle,genetic algorithm,safe distance
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