A Three-Level Game-Theoretic Decision-Making Framework for Autonomous Vehicles

Liu, Mushuang; Wan, Yan; Lewis, Frank L.; Nageshrao, Subramanya; Filev, Dimitar

doi:10.1109/tits.2022.3172926

Cited by 19 publications

(7 citation statements)

References 30 publications

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“…developed a threelevel intersection decision framework, i.e. action space filter, 2-player game and safety check in each level, in which a neural network is used to tune the game parameters and thus can help the decision algorithm to adaptively interact with drivers of different styles [18]. Nan et.al.…”

Section: A Literature Reviewmentioning

confidence: 99%

Autonomous Driving Decision Algorithm for Complex Multi-vehicle Interactions: an Efficient Approach Based on Global Sorting and Local Gaming

Li¹,

Zhang²,

Liu³

2023

Preprint

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<p>For autonomous driving, it is important to develop safe and efficient decision algorithms to handle multi-vehicle interactions. Game theory is suitable to manage the interactive driving decision modelling, however, common approaches of multi-player game formulation is computationally complex for dynamic and intense interactions. To overcome this challenge, a global sorting-local gaming framework, namely GLOSO-LOGA, is proposed to solve the intersection interaction problem for autonomous driving, which can comprehensively consider the advantages of multi-vehicle collaboration and single-vehicle intelligence approaches. Further, an interaction disturbance function is used to quantify the impact of indirect interactions on ego vehicle. Then simulations with single and multiple conflict areas settings in a four-armed intersection are carried out. Simulations and human-in-the-loop simulator experiments show that the proposed algorithm can improve the interaction safety in intensively interactive driving scenarios even in complex and urgent interaction cases. The proposed framework may be potentially applied in handling autonomous vehicle's dynamic interactions with multiple road users.</p>

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Section: A Literature Reviewmentioning

confidence: 99%

Autonomous Driving Decision Algorithm for Complex Multi-vehicle Interactions: an Efficient Approach Based on Global Sorting and Local Gaming

Li¹,

Zhang²,

Liu³

2023

Preprint

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“… It provides very fitting characteristics for realizing the solutions of complex and urban driving, specifically for short-term, highly dynamic, and L4/L5 platooning. The approaches such as cooperative GT for collaboration are researched extensively and publications may be found e.g., [ 65 , 66 , 67 , 68 ]. …”

Section: The Analyses Of Decision-making Relevant Solutions For Auton...mentioning

confidence: 99%

“…The approaches such as cooperative GT for collaboration are researched extensively and publications may be found e.g., [ 65 , 66 , 67 , 68 ].…”

Section: The Analyses Of Decision-making Relevant Solutions For Auton...mentioning

confidence: 99%

How Do Autonomous Vehicles Decide?

Malik

Khan

El-Sayed

et al. 2022

Sensors

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The advancement in sensor technologies, mobile network technologies, and artificial intelligence has pushed the boundaries of different verticals, e.g., eHealth and autonomous driving. Statistics show that more than one million people are killed in traffic accidents yearly, where the vast majority of the accidents are caused by human negligence. Higher-level autonomous driving has great potential to enhance road safety and traffic efficiency. One of the most crucial links to building an autonomous system is the task of decision-making. The ability of a vehicle to make robust decisions on its own by anticipating and evaluating future outcomes is what makes it intelligent. Planning and decision-making technology in autonomous driving becomes even more challenging, due to the diversity of the dynamic environments the vehicle operates in, the uncertainty in the sensor information, and the complex interaction with other road participants. A significant amount of research has been carried out toward deploying autonomous vehicles to solve plenty of issues, however, how to deal with the high-level decision-making in a complex, uncertain, and urban environment is a comparatively less explored area. This paper provides an analysis of decision-making solutions approaches for autonomous driving. Various categories of approaches are analyzed with a comparison to classical decision-making approaches. Following, a crucial range of research gaps and open challenges have been highlighted that need to be addressed before higher-level autonomous vehicles hit the roads. We believe this survey will contribute to the research of decision-making methods for autonomous vehicles in the future by equipping the researchers with an overview of decision-making technology, its potential solution approaches, and challenges.

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“…Liu et al developed a three-level intersection decision framework, i.e. action space filter, 2-player game and safety check in each level, in which a neural network is used to tune the game parameters and thus can help the decision algorithm to adaptively interact with drivers of different styles [18]. Nan et al proposed a two-vehicle intersection decision algorithm by combining a probabilistic driving intention prediction module and a mixed-strategy game formulation [19].…”

Section: Introductionmentioning

confidence: 99%