Intention Prediction and Mixed Strategy Nash Equilibrium-Based Decision-Making Framework for Autonomous Driving in Uncontrolled Intersection

Nan, Jiangfeng; Yang, Shun Kun; Zheng, Bowen

doi:10.1109/tvt.2022.3186976

Cited by 24 publications

(3 citation statements)

References 28 publications

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“…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]. Wang 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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“…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]. Wang 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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show abstract

“…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]. Wang et al suggested a game-based interaction decision algorithm that realizes courtesy behaviours by incorporating goal fulfillment, safety and courtesy loss into game payoffs [20].…”

Section: Introductionmentioning

confidence: 99%

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

Li,

Zhang,

Liu

2024

IEEE Trans. Intell. Transport. Syst.

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“…The Ideal Driving Model (IDM) [5] has been a very popular car-following model for micro-traffic simulation and was repurposed into a Generalized Intelligent Driver Model (GIDM) [6] to enable basic uncontrolled intersection flow simulation for comfort and safety studies. More complex methodologies make use of the Markov decision process [7], as well as Game theory which was demonstrated to simulate realistic behavior at uncontrolled intersections within manageable complexity [8,9]. More conventional optimal control methodologies using Model Predictive Control [10][11][12] making use of ADAS sensors have been developed.…”

Section: Introductionmentioning

confidence: 99%

Neuroevolution Application to Collaborative and Heuristics-Based Connected and Autonomous Vehicle Cohort Simulation at Uncontrolled Intersection

Jacquelin

Bae

Chen

et al. 2023

Eng

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Artificial intelligence is gaining tremendous attractiveness and showing great success in solving various problems, such as simplifying optimal control derivation. This work focuses on the application of Neuroevolution to the control of Connected and Autonomous Vehicle (CAV) cohorts operating at uncontrolled intersections. The proposed method implementation’s simplicity, thanks to the inclusion of heuristics and effective real-time performance are demonstrated. The resulting architecture achieves nearly ideal operating conditions in keeping the average speeds close to the speed limit. It achieves twice as high mean speed throughput as a controlled intersection, hence enabling lower travel time and mitigating energy inefficiencies from stop-and-go vehicle dynamics. Low deviation from the road speed limit is hence continuously sustained for cohorts of at most 50 m long. This limitation can be mitigated with additional lanes that the cohorts can split into. The concept also allows the testing and implementation of fast-turning lanes by simply replicating and reconnecting the control architecture at each new road crossing, enabling high scalability for complex road network analysis. The controller is also successfully validated within a high-fidelity vehicle dynamic environment, showing its potential for driverless vehicle control in addition to offering a new traffic control simulation model for future autonomous operation studies.

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Intention Prediction and Mixed Strategy Nash Equilibrium-Based Decision-Making Framework for Autonomous Driving in Uncontrolled Intersection

Cited by 24 publications

References 28 publications

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

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

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

Neuroevolution Application to Collaborative and Heuristics-Based Connected and Autonomous Vehicle Cohort Simulation at Uncontrolled Intersection

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