Dynamic States Prediction in Autonomous Vehicles: Comparison of Three Different Methods

Li, Teng; Tian, Bin; Ai, Yunfeng; Chen, Long; Liu, Fei; Cao, Dongpu; Bian, Ning; Wang, Fei‐Yue

doi:10.1109/itsc.2019.8916969

Cited by 8 publications

(5 citation statements)

References 15 publications

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“…The goal of the agent is to search an optimal sequence of control actions based on feedback from the environment. Owing to its characteristics of self-evaluation and self-promotion, RL is widely used in many research fields [28][29][30][31][32].…”

Section: A Rl Conceptmentioning

confidence: 99%

“…5 depicts the normalized average rewards of these three methods. Based on the definition of reward function in (27), higher reward indicates driving on the preferred lane with a more efficient maneuver. It is obvious that the training stability and learning speed of DDQN are better than the other two approaches.…”

Section: A Optimality Evaluationmentioning

confidence: 99%

“…Autonomous driving (AD) enables the vehicle to engage different driving missions without a human driver [1,2]. Motivated by the enormous potentials of artificial intelligence (AI), autonomous vehicles or automated vehicles have become one of the research hotspots all over the world [3].…”

Section: Introductionmentioning

confidence: 99%

“…Fig.11shows the normalized average reward of the reference model, DQN, and DDQN methods in the testing experiment. From(27), the reward is mainly influenced by the collision conditions and vehicle speed. The average reward may not achieve the highest score (100 in this work) because the ego vehicle has to slow down sometimes to avoid a collision.…”

mentioning

confidence: 99%

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Decision-Making Strategy on Highway for Autonomous Vehicles Using Deep Reinforcement Learning

Liao

Tang

et al. 2020

IEEE Access

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Autonomous driving is a promising technology to reduce traffic accidents and improve driving efficiency. In this work, a deep reinforcement learning (DRL)-enabled decision-making policy is constructed for autonomous vehicles to address the overtaking behaviors on the highway. First, a highway driving environment is founded, wherein the ego vehicle aims to pass through the surrounding vehicles with an efficient and safe maneuver. A hierarchical control framework is presented to control these vehicles, which indicates the upper-level manages the driving decisions, and the lower-level cares about the supervision of vehicle speed and acceleration. Then, the particular DRL method named dueling deep Qnetwork (DDQN) algorithm is applied to derive the highway decision-making strategy. The exhaustive calculative procedures of deep Q-network and DDQN algorithms are discussed and compared. Finally, a series of estimation simulation experiments are conducted to evaluate the effectiveness of the proposed highway decision-making policy. The advantages of the proposed framework in convergence rate and control performance are illuminated. Simulation results reveal that the DDQN-based overtaking policy could accomplish highway driving tasks efficiently and safely. INDEX TERMS Autonomous driving, decision-making, deep reinforcement learning, dueling deep Qnetwork, deep Q-learning, overtaking policy.

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Section: A Rl Conceptmentioning

confidence: 99%

Section: A Optimality Evaluationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

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Decision-Making Strategy on Highway for Autonomous Vehicles Using Deep Reinforcement Learning

Liao

Tang

et al. 2020

IEEE Access

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“…The decision-making module serves as the central intelligence hub within the autonomous driving system [3]. Its primary responsibility is to formulate appropriate motion behaviors tailored to specific missions within a dynamic and uncertain environment.…”

Section: Introductionmentioning

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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A Prediction-Based Route Guidance Method Toward Intelligent and Green Transportation System

Zhu

Lin

2023

IEEE Internet Things J.

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Dynamic States Prediction in Autonomous Vehicles: Comparison of Three Different Methods

Cited by 8 publications

References 15 publications

Decision-Making Strategy on Highway for Autonomous Vehicles Using Deep Reinforcement Learning

Decision-Making Strategy on Highway for Autonomous Vehicles Using Deep Reinforcement Learning

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

A Prediction-Based Route Guidance Method Toward Intelligent and Green Transportation System

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