Development of a Human-Like Learning Frame for Data-Driven Adaptive Control Algorithm of Automated Driving

Oh, Kwangseok; Oh, Sechan; Lee, Jongmin; Yi, Kyongsu

doi:10.23919/iccas52745.2021.9649954

Cited by 1 publication

(1 citation statement)

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“…The data-driven human-like learning algorithm does not need any system information, such as a mathematical model and system parameters [19]. This method has been widely used in human-like driving and decision making [20][21][22], especially the deep neural network approach, usually combined with reinforcement and imitation learning, has been used to model various driving tasks in different environments [23].…”

Section: Introductionmentioning

confidence: 99%

Human-like Decision Making for Autonomous Vehicles at the Intersection Using Inverse Reinforcement Learning

Zheng

Yang

et al. 2022

Sensors

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With the rapid development of autonomous driving technology, both self-driven and human-driven vehicles will share roads in the future and complex information exchange among vehicles will be required. Therefore, autonomous vehicles need to behave as similar to human drivers as possible, to ensure that their behavior can be effectively understood by the drivers of other vehicles and be more in line with the cognition of humans on driving behavior. Therefore, this paper studies the evaluation function of human drivers, using the method of inverse reinforcement learning, aiming for the learned behavior to better imitate the behavior of human drivers. At the same time, this paper proposes a semi-Markov model, to extract the intentions of surrounding related vehicles and divides them into defensive and cooperative, leading the vehicle to adopt a reasonable response to different types of driving scenarios.

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