Open AccessPosted Content
AirSim: High-Fidelity Visual and Physical Simulation for Autonomous Vehicles
TL;DR: A new simulator built on Unreal Engine that offers physically and visually realistic simulations for autonomous vehicles in real world and that is designed from the ground up to be extensible to accommodate new types of vehicles, hardware platforms and software protocols.
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Abstract: Developing and testing algorithms for autonomous vehicles in real world is an expensive and time consuming process. Also, in order to utilize recent advances in machine intelligence and deep learning we need to collect a large amount of annotated training data in a variety of conditions and environments. We present a new simulator built on Unreal Engine that offers physically and visually realistic simulations for both of these goals. Our simulator includes a physics engine that can operate at a high frequency for real-time hardware-in-the-loop (HITL) simulations with support for popular protocols (e.g. MavLink). The simulator is designed from the ground up to be extensible to accommodate new types of vehicles, hardware platforms and software protocols. In addition, the modular design enables various components to be easily usable independently in other projects. We demonstrate the simulator by first implementing a quadrotor as an autonomous vehicle and then experimentally comparing the software components with real-world flights.
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Citations
Milestones in Autonomous Driving and Intelligent Vehicles: Survey of Surveys
Long Chen,Yuchen Li,Chao Huang,Bai Qi Li,Yang Xing,Daxin Tian,Li Li,Zhongxu Hu,Xiaoxiang Na,Zixuan Li,Siyu Teng,Chen Lv,Jinjun Wang,Dongpu Cao,Nanning Zheng,Fei-Yue Wang +15 more
TL;DR: In this article , the authors present a survey of surveys for autonomous driving and intelligent vehicles (IVs) that reviews the history, summarizes the milestones, and provides the perspectives, ethics, and future research directions.
190
Motion Planning for Autonomous Driving: The State of the Art and Future Perspectives
Siyu Teng,Xue Min Hu,Peng Deng,Bai Qi Li,Yuchen Li,Yunfeng Ai,Dongsheng Yang,Lingxi Li,Zhe Xuanyuan,Fenghua Zhu,Long Chen +10 more
TL;DR: In this paper , state-of-the-art motion planning methods for intelligent vehicles, including pipeline planning and end-to-end planning methods, are reviewed to highlight their strengths and limitations.
189
•Posted Content
SMARTS: Scalable Multi-Agent Reinforcement Learning Training School for Autonomous Driving.
Ming Zhou,Jun Luo,Julian Villela,Yaodong Yang,David Rusu,Jiayu Miao,Weinan Zhang,Montgomery Alban,Iman Fadakar,Zheng Chen,Aurora Chongxi Huang,Ying Wen,Kimia Hassanzadeh,Daniel Graves,Dong Chen,Zhengbang Zhu,Nhat M. Nguyen,Mohamed A. Elsayed,Kun Shao,Sanjeevan Ahilan,Baokuan Zhang,Jiannan Wu,Zhengang Fu,Kasra Rezaee,Peyman Yadmellat,Mohsen Rohani,Nicolas Perez Nieves,Yihan Ni,Seyedershad Banijamali,Alexander Imani Cowen-Rivers,Zheng Tian,Daniel Palenicek,Haitham Bou-Ammar,Hongbo Zhang,Wulong Liu,Jianye Hao,Jun Wang +36 more
TL;DR: The design goals of SMARTS (Scalable Multi-Agent RL Training School) are described, its basic architecture and its key features are explained, and its use is illustrated through concrete multi-agent experiments on interactive scenarios.
A Curriculum Domain Adaptation Approach to the Semantic Segmentation of Urban Scenes
TL;DR: In this article, a curriculum-style learning approach is proposed to minimize the domain gap in urban scene semantic segmentation by solving easy tasks first to infer necessary properties about the target domain; in particular, the first task is to learn global label distributions over images and local distributions over landmark superpixels.
177
Multi-Agent Connected Autonomous Driving using Deep Reinforcement Learning
Palanisamy Praveen
- 19 Jul 2020
TL;DR: In this paper, the authors proposed the use of Partially Observable Markov Games (POSG) for formulating the connected autonomous driving problems with realistic assumptions, and provided a taxonomy of multi-agent learning environments based on the nature of tasks, nature of agents and the environment.
169
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