Driving Tasks Transfer Using Deep Reinforcement Learning for Decision-Making of Autonomous Vehicles in Unsignalized Intersection

Shu, Hong; Li, Teng; Mu, Xingyu; Cao, Dongpu

doi:10.1109/tvt.2021.3121985

Cited by 47 publications

(16 citation statements)

References 28 publications

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“…According to different driving behaviors (e.g., lane change, acceleration or deceleration) or tasks (e.g., overtaking or ramp merging) in existing related studies, RL based decision making of autonomous vehicles can roughly be divided into three categories: longitudinal, lateral and coordinated decision making [9]. RL based longitudinal decision-making methods generally adopt RL algorithm to determine the speed modes of autonomous vehicles, such as keeping, acceleration and deceleration [11], [16], [17], [18].…”

Section: Related Workmentioning

confidence: 99%

Robust Lane Change Decision Making for Autonomous Vehicles: An Observation Adversarial Reinforcement Learning Approach

Yang

et al. 2023

IEEE Trans. Intell. Veh.

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Section: Related Workmentioning

confidence: 99%

Robust Lane Change Decision Making for Autonomous Vehicles: An Observation Adversarial Reinforcement Learning Approach

Yang

et al. 2023

IEEE Trans. Intell. Veh.

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“…2) Learning-based approach: Another decision approach for SVI is learning-based or data driven, which learns human decision-making behaviors from the driving data of either naturalistic or simulated driving [11]- [15]. For example, Yuan 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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“…However, the interactions between vehicles were neglected, which means that some interrelated intentions like crossing, yielding, lane changing and car following were ignored. In [27], a transfer deep reinforcement learning (RL) framework was constructed, where the decision-making policy learned from one driving task at an intersection was transferred in another driving mission. However, the studied scenario was simple where few participants were involved.…”

Section: Literature Reviewmentioning

confidence: 99%

Autonomous vehicles’ intended cooperative motion planning for unprotected turning at intersections

Zhou

Zhang

et al. 2022

IET Intelligent Trans Sys

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Turning behaviour is one of the most challenging driving manoeuvres that take place at intersections. Autonomous vehicles (AVs) are often overly conservative in these scenarios as they compromised to others’ behaviour to realize behavioural consistency. This paper proposes an intended cooperative motion planning (ICMP) that can actively predict the intentions of interacting participants. This helps achieve socially compliant cooperation and enables each vehicle to converge upon a set of consistent behaviours. The ICMP framework divides the integrated driving process into two related modules: prediction and planning. These modules are integrated using the mechanism of intended cooperation, which first introduces the criteria for a cooperative response and then maps them in the motion planning space. The prediction module applies a Gaussian mixture model to learn human drivers’ behaviour to determine conflict points, which helps to narrow down the solution spaces. In the planning module, paths are represented by quartic Bézier curves and speed is modelled as a polynomial function. Optimizations can then be used that maximize the efficiency and smoothness for path planning, and comfort and efficiency for speed planning. The test results show that ICMP‐based AVs had consistent interactions with other vehicles. Moreover, when compared with a potential field‐based method, the ICMP‐based method had better performance in terms of safety, comfort and efficiency, especially when interacting with multiple oncoming vehicles.

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Driving Tasks Transfer Using Deep Reinforcement Learning for Decision-Making of Autonomous Vehicles in Unsignalized Intersection

Cited by 47 publications

References 28 publications

Robust Lane Change Decision Making for Autonomous Vehicles: An Observation Adversarial Reinforcement Learning Approach

Robust Lane Change Decision Making for Autonomous Vehicles: An Observation Adversarial Reinforcement Learning Approach

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

Autonomous vehicles’ intended cooperative motion planning for unprotected turning at intersections

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