Cooperation-Aware Reinforcement Learning for Merging in Dense Traffic

Bouton, Maxime; Nakhaei, Alireza; Fujimura, Kikuo; Kochenderfer, Mykel J.

doi:10.1109/itsc.2019.8916924

Cited by 97 publications

(67 citation statements)

References 21 publications

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“…The results are consistent with current empirical data showing that 3D audiovisual artifacts in the virtual learning environment served as rich resources for collaboration by students. [7,34].…”

Section: Discussionmentioning

confidence: 99%

Effectiveness of Second Life Virtual Learning Environment for Language Training in Hospitality and Tourism

Vu¹,

Linh²,

Van³

et al. 2021

Advances in Social Science, Education and Humanities Research

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Virtual Learning Environment (VLE) systems have been widely used in higher education as an effective e-learning platform. With its large user community and virtual facilities in various disciplines, including education, recreation, tourism, business, etc. Second Life (SL) has become one of the most dominant VLE systems for hospitality and tourism training. With better student devices and a faster internet connection, language education in Vietnam recently saw soaring interest in using SL for training, especially in an ESP area like English for hospitality and tourism. However, there is still very little research on the effectiveness of SL VLE for language training in hospitality and tourism. This fifteen-week quasi-experimental study was conducted on two classes (n = 81) in order to investigate the effectiveness of SL VLE for language training in hospitality and tourism with a focus on speaking skills. The instruments included English speaking tests, an attitudinal questionnaire, and a semi-structured interview. The findings of the study indicated that language training activities in SL positively contributed to students' language proficiency progress. Besides, students have positive perceptions about the use of SL VLE in their training. It is recommended that language training programs in hospitality and tourism make more substantial use of virtual restaurants, hotels, resorts, and entertainment places in SL to enrich students' learning experience. Hospitality and tourism training institutions should spend resources on formal SL VLE training for teachers and students and build their SL facilities.

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Section: Discussionmentioning

confidence: 99%

Effectiveness of Second Life Virtual Learning Environment for Language Training in Hospitality and Tourism

Vu¹,

Linh²,

Van³

et al. 2021

Advances in Social Science, Education and Humanities Research

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show abstract

“…11), where the ego vehicle needs to find the acceptable gap between two vehicles to get on the highway. In the simplest approach, it is eligible to learn the longitudinal control, where the agent reaches this position, as can be seen in [19], [58], [91]. Other papers, like [82] use full steering and acceleration control.…”

Section: Mergingmentioning

confidence: 99%

“…An exciting addition can be examined in [19], where the surrounding vehicles act differently, as there are cooperative and non-cooperative drivers among them. They trained their agents with the knowledge about cooperative behavior, and also compared the results with three differently built MTCS planners.…”

Section: Mergingmentioning

confidence: 99%

Survey of Deep Reinforcement Learning for Motion Planning of Autonomous Vehicles

Aradi

2022

IEEE Trans. Intell. Transport. Syst.

343

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Academic research in the field of autonomous vehicles has reached high popularity in recent years related to several topics as sensor technologies, V2X communications, safety, security, decision making, control, and even legal and standardization rules. Besides classic control design approaches, Artificial Intelligence and Machine Learning methods are present in almost all of these fields. Another part of research focuses on different layers of Motion Planning, such as strategic decisions, trajectory planning, and control. A wide range of techniques in Machine Learning itself have been developed, and this article describes one of these fields, Deep Reinforcement Learning (DRL). The paper provides insight into the hierarchical motion planning problem and describes the basics of DRL. The main elements of designing such a system are the modeling of the environment, the modeling abstractions, the description of the state and the perception models, the appropriate rewarding, and the realization of the underlying neural network. The paper describes vehicle models, simulation possibilities and computational requirements. Strategic decisions on different layers and the observation models, e.g., continuous and discrete state representations, grid-based, and camera-based solutions are presented. The paper surveys the state-of-art solutions systematized by the different tasks and levels of autonomous driving, such as carfollowing, lane-keeping, trajectory following, merging, or driving in dense traffic. Finally, open questions and future challenges are discussed.

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“…Policy Gradient. Since, there are major gaps between simulated and real environments that make it difficult to train models, DRL works very well in closed environments like video games, but it is difficult to apply to real-world environments [74]- [76].…”

Section: ) Reinforcement Learningmentioning

confidence: 99%

Detecting Human Driver Inattentive and Aggressive Driving Behavior Using Deep Learning: Recent Advances, Requirements and Open Challenges

2020

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Human drivers have different driving styles, experiences, and emotions due to unique driving characteristics, exhibiting their own driving behaviors and habits. Various research efforts have approached the problem of detecting abnormal human driver behavior with the aid of capturing and analyzing the face of driver and vehicle dynamics via image and video processing but the traditional methods are not capable of capturing complex temporal features of driving behaviors. However, with the advent of deep learning algorithms, a significant amount of research has also been conducted to predict and analyze driver's behavior or action related information using neural network algorithms. In this paper, we contribute to first classify and discuss Human Driver Inattentive Driving Behavior (HIDB) into two major categories, Driver Distraction (DD), Driver Fatigue (DF), or Drowsiness (DFD). Then we discuss the causes and effects of another human risky driving behavior called Aggressive Driving behavior (ADB). Aggressive driving Behavior (ADB) is a broad group of dangerous and aggressive driving styles that lead to severe accidents. Human abnormal driving behaviors DD, DFD, and ADB are affected by various factors including driver experience/inexperience of driving, age, and gender or illness. The study of the effects of these factors that may lead to deterioration in the driving skills and performance of a human driver is out of the scope of this paper. After describing the background of deep learning and its algorithms, we present an in-depth investigation of most recent deep learning-based systems, algorithms, and techniques for the detection of Distraction, Fatigue/Drowsiness, and Aggressiveness of a human driver. We attempt to achieve a comprehensive understanding of HIADB detection by presenting a detailed comparative analysis of all the recent techniques. Moreover, we highlight the fundamental requirements. Finally, we present and discuss some significant and essential open research challenges as future directions.

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Cooperation-Aware Reinforcement Learning for Merging in Dense Traffic

Cited by 97 publications

References 21 publications

Effectiveness of Second Life Virtual Learning Environment for Language Training in Hospitality and Tourism

Effectiveness of Second Life Virtual Learning Environment for Language Training in Hospitality and Tourism

Survey of Deep Reinforcement Learning for Motion Planning of Autonomous Vehicles

Detecting Human Driver Inattentive and Aggressive Driving Behavior Using Deep Learning: Recent Advances, Requirements and Open Challenges

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