A Brain-Inspired Decision-Making Linear Neural Network and Its Application in Automatic Drive

Sun, Tianjun; Gao, Zhenhai; Gao, Fei; Wang, Ziyang; Chen, Siyan; Zhao, Kehan

doi:10.3390/s21030794

Cited by 8 publications

(3 citation statements)

References 18 publications

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“…Furthermore, the fusion should automatically adapt to a dynamic environment so that integrated intelligence can continuously evolve with updates in human knowledge. Thus, selfevolving integrated intelligence is critical for handling dynamic scenarios so that the tasks and data can change rapidly [139].…”

Section: Human-machine Integrationmentioning

confidence: 99%

Intelligent Computing: The Latest Advances, Challenges, and Future

Zhu

et al. 2023

Intell Comput

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Computing is a critical driving force in the development of human civilization. In recent years, we have witnessed the emergence of intelligent computing, a new computing paradigm that is reshaping traditional computing and promoting digital revolution in the era of big data, artificial intelligence, and internet of things with new computing theories, architectures, methods, systems, and applications. Intelligent computing has greatly broadened the scope of computing, extending it from traditional computing on data to increasingly diverse computing paradigms such as perceptual intelligence, cognitive intelligence, autonomous intelligence, and human–computer fusion intelligence. Intelligence and computing have undergone paths of different evolution and development for a long time but have become increasingly intertwined in recent years: Intelligent computing is not only intelligence oriented but also intelligence driven. Such cross-fertilization has prompted the emergence and rapid advancement of intelligent computing. Intelligent computing is still in its infancy, and an abundance of innovations in the theories, systems, and applications of intelligent computing is expected to occur soon. We present the first comprehensive survey of literature on intelligent computing, covering its theory fundamentals, the technological fusion of intelligence and computing, important applications, challenges, and future perspectives. We believe that this survey is highly timely and will provide a comprehensive reference and cast valuable insights into intelligent computing for academic and industrial researchers and practitioners.

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Section: Human-machine Integrationmentioning

confidence: 99%

Intelligent Computing: The Latest Advances, Challenges, and Future

Zhu

et al. 2023

Intell Comput

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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]. A human-like longitudinal driving model for AVs is established, by using reinforcement learning (RL) [24].…”

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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“…Leaning-based approaches have been widely applied to the human-like driving and decision making [17]- [19]. Based on reinforcement learning (RL), a human-like longitudinal driver model is established for AVs [20].…”

Section: Introductionmentioning

confidence: 99%

Brain-Inspired Modelling and Decision-making for Human-Like Autonomous Driving in Mixed Traffic Environment

Hang¹,

Zhang²,

Lv³

2022

Preprint

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In this paper, a human-like driving framework is designed for autonomous vehicles (AVs), which aims to make AVs better integrate into the transportation ecology of human driving and eliminate the misunderstanding and incompatibility of human drivers to autonomous driving. Based on the analysis of the real world INTERACTION dataset, a driving aggressiveness estimation model is established with the fuzzy inference approach. Then, a human-like driving model, which integrates the brain emotional learning circuit model (BELCM) with the two-point preview model, is designed. In the human-like lanechange decision-making algorithm, the cost function is designed comprehensively considering driving safety and travel efficiency. Based on the cost function and multi-constraint, the dynamic game algorithm is applied to modelling the interaction and decision making between AV and human driver. Additionally, to guarantee the lane-change safety of AVs, an artificial potential field model is built for collision risk assessment. Finally, the proposed algorithm is evaluated through human-in-the-loop experiments on a driving simulator, and the results demonstrated the feasibility and effectiveness of the proposed method.

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A Brain-Inspired Decision-Making Linear Neural Network and Its Application in Automatic Drive

Cited by 8 publications

References 18 publications

Intelligent Computing: The Latest Advances, Challenges, and Future

Intelligent Computing: The Latest Advances, Challenges, and Future

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

Brain-Inspired Modelling and Decision-making for Human-Like Autonomous Driving in Mixed Traffic Environment

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