Interaction-Aware Decision-Making for Autonomous Vehicles

Chen, Yongli; Li, Shen; Yang, Kai; Cao, Dongpu; Lin, Xianke

doi:10.1109/tte.2023.3240454

Cited by 14 publications

(9 citation statements)

References 34 publications

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“…Lopez et al [14] addressed the lane-changing decision problem in autonomous driving vehicles by formulating a standard form of multi-game for the intelligent agent. Chen et al [15], based on predicted pedestrian cooperation, use game theory to output cooperative optimal actions, balancing safety risks and efficiency gains. Pae et al [16] introduced a Gaussian potential field model based on obstacle uncertainty and applied it in an MPC decision model, achieving safe and efficient obstacle avoidance.…”

Section: Related Workmentioning

confidence: 99%

A Comparative Analysis of Deep Reinforcement Learning-Enabled Freeway Decision-Making for Automated Vehicles

Liu,

Yang,

Xiao

et al. 2024

IEEE Access

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In application, advanced autonomous driving technologies still face numerous challenges. Deep Reinforcement Learning (DRL) has emerged as a widespread and effective approach to address artificial intelligence challenges, due to its substantial potential for autonomous learning and self-improvement. In this study, four DRL algorithms-Deep Q-Learning (DQN), along with its enhanced algorithm, Double DQL, Dueling DQL, and Priority Replay DQL(PR-DQN), are employed to address decision-making challenges for autonomous vehicles on highways, with a comprehensive comparative analysis conducted. The decisionmaking model is constructed as a Markov Decision Process, guided by specially designed reward functions, enabling the target vehicle to learn safe and efficient decision-making strategies through multiple environmental explorations. Through the analysis and discussion of a series of experimental results, the feasibility of DRL-based decision strategies is demonstrated. Finally, through comparing the experimental outcomes of different algorithms, the connection between autonomous driving results and the inherent learning features of these DRL technologies is analyzed.

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Section: Related Workmentioning

confidence: 99%

A Comparative Analysis of Deep Reinforcement Learning-Enabled Freeway Decision-Making for Automated Vehicles

Liu,

Yang,

Xiao

et al. 2024

IEEE Access

Self Cite

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“…To enhance the dynamic and interactive nature of crossing decisions, game theoretical models were used to model crossing decisions when negotiating the right of way with AVs. [92,124] Researchers also attempted to use pedestrian perception theories or models to design AV decision-making strategies. For instance, ref.…”

Section: Av-involved Applicationsmentioning

confidence: 99%

Section: Human Behaviour Studies On Interactionsmentioning

confidence: 99%

“…[98,121] Moreover, SF and CA models have also been embedded in AV decision modules to represent pedestrian crossing behaviour and guide AV decisions in interactions with pedestrians. [122][123][124] Furthermore, crossing decision models have also been applied in AV research. For example, ref.…”

Section: Av-involved Applicationsmentioning

confidence: 99%

“…To enhance the dynamic and interactive nature of crossing decisions, game theoretical models were used to model crossing decisions when negotiating the right of way with AVs. [ 92,124 ]…”

Section: Human Behaviour Studies On Interactionsmentioning

confidence: 99%

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Social Interaction‐Aware Dynamical Models and Decision‐Making for Autonomous Vehicles

Crosato,

Tian,

Shum

et al. 2023

Advanced Intelligent Systems

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Interaction‐aware autonomous driving (IAAD) is a rapidly growing field of research that focuses on the development of autonomous vehicles (AVs) that are capable of interacting safely and efficiently with human road users. This is a challenging task, as it requires the AV to be able to understand and predict the behaviour of human road users. In this literature review, the current state of IAAD research is surveyed. Commencing with an examination of terminology, attention is drawn to challenges and existing models employed for modeling the behaviour of drivers and pedestrians. Next, a comprehensive review is conducted on various techniques proposed for interaction modeling, encompassing cognitive methods, machine‐learning approaches, and game‐theoretic methods. The conclusion is reached through a discussion of potential advantages and risks associated with IAAD, along with the illumination of pivotal research inquiries necessitating future exploration.

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