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Thoughts and Suggestions on Autonomous Driving Map Policy

Liu Jingnan, Dong Yang, Zhan Jiao, Gao Kefu

Strategic Study of CAE 2019, Volume 21, Issue 3,   Pages 92-97 doi: 10.15302/J-SSCAE-2019.03.004

Abstract:

As a key infrastructure to realize autonomous driving, autonomous drivingmap is crucial to the commercial development of the autonomous driving field in China.liability and insurance issues, as well as autonomous driving map related test specifications and testdriving vehicles in China: formulating an autonomous driving map management mode, allowing pilot applicationoptimizing the review process, as well as establishing a national-level autonomous driving map platform

Keywords： autonomous driving map     autonomous driving regulation     autonomous driving policy    

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Evolutionary Decision-Making and Planning for Autonomous Driving Based on Safe and Rational Exploration

Kang Yuan,Yanjun Huang,Shuo Yang,Zewei Zhou,Yulei Wang,Dongpu Cao,Hong Chen,

Engineering doi: 10.1016/j.eng.2023.03.018

Abstract: Decision-making and motion planning are extremely important in autonomous driving to ensure safe drivingThis study proposes an online evolutionary decision-making and motion planning framework for autonomousdriving based on a hybrid data- and model-driven method.decision-making module based on deep reinforcement learning (DRL) is developed to pursue a rational drivingFinally, two principles of safety and rationality for the self-evolution of autonomous driving are proposed

Keywords： Autonomous driving     Decision-making     Motion planning     Deep reinforcement learning     Model predictive control    

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A Probabilistic Architecture of Long-Term Vehicle Trajectory Prediction for Autonomous Driving Article

Jinxin Liu, Yugong Luo, Zhihua Zhong, Keqiang Li, Heye Huang, Hui Xiong

Engineering 2022, Volume 19, Issue 12,   Pages 228-239 doi: 10.1016/j.eng.2021.12.020

Abstract: long-term trajectory forecasting of surrounding vehicles is one of the indispensable preconditions for autonomousvehicles (AVs) to accomplish reasonable behavioral decisions and guarantee driving safety.integrated probabilistic architecture for long-term vehicle trajectory prediction, which consists of a drivingThe DIM is designed and employed to accurately infer the potential driving intention based on a dynamiceffectiveness of our novel approach is demonstrated by conducting experiments on a public naturalistic driving

Keywords： Autonomous driving     Dynamic Bayesian network     Driving intention recognition     Gaussian process     Vehicle    

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Towards the Unified Principles for Level 5 Autonomous Vehicles Article

Jianqiang Wang, Heye Huang, Keqiang Li, Jun Li

Engineering 2021, Volume 7, Issue 9,   Pages 1313-1325 doi: 10.1016/j.eng.2020.10.018

Abstract:

The rapid advance of autonomous vehicles (AVs) has motivated new perspectives and potential challengesCurrently, driving assistance systems of Level 3 and below have been widely produced, and several applicationsdriving.By exploring the physical mechanisms behind high-level autonomous driving systems and analyzing the essenceof driving, we put forward a coordinated and balanced framework based on the brain–cerebellum&

Keywords： Autonomous vehicle     Principle of least action     Driving safety field     Autonomous learning     Basic paradigm    

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Autonomous Driving in the iCity—HD Maps as a Key Challenge of the Automotive Industry Perspective

Heiko G. Seif, Xiaolong Hu

Engineering 2016, Volume 2, Issue 2,   Pages 159-162 doi: 10.1016/J.ENG.2016.02.010

Abstract:

This article provides in-depth insights into the necessary technologies for automated driving in futureEspecially the challenges for the application of HD maps as core technology for automated driving are

Keywords： Autonomous driving     Traffic infrastructure     iCity     Car-to-X communication     Connected vehicle     HD maps    

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Toward Human-in-the-loop AI: Enhancing Deep Reinforcement Learning Via Real-time Human Guidance for AutonomousDriving Article

Jingda Wu, Zhiyu Huang, Zhongxu Hu, Chen Lv

Engineering 2023, Volume 21, Issue 2,   Pages 75-91 doi: 10.1016/j.eng.2022.05.017

Abstract: human-guidance-based (Hug)-deep reinforcement learning (DRL) method is developed for policy training in an end-to-end autonomousdriving case.

Keywords： Human-in-the-loop AI     Deep reinforcement learning     Human guidance     Autonomous driving    

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Semantic Consistency and Correctness Verification of Digital Traffic Rules

Lei Wan,Changjun Wang,Daxin Luo,Hang Liu,Sha Ma,Weichao Hu,

Engineering doi: 10.1016/j.eng.2023.04.016

Abstract: The consensus of the automotive industry and traffic management authorities is that autonomous vehiclesdigital methods, natural language traffic rules can be translated into machine language and used by autonomousrules utilizing metric temporal logic (MTL) can be easily incorporated into simulation platforms and autonomousdriving systems (ADS).

Keywords： Autonomous driving     Traffic rules     Digitization     Formalization     Verification    

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A Flexible Multi-Layer Map Model Designed for Lane-Level Route Planning in Autonomous Vehicles Article

Kun Jiang, Diange Yang, Chaoran Liu, Tao Zhang, Zhongyang Xiao

Engineering 2019, Volume 5, Issue 2,   Pages 305-318 doi: 10.1016/j.eng.2018.11.032

Abstract:

An increasing number of drivers are relying on digital map navigation systems in vehicles or mobilephones to select optimal driving routes in order to save time and improve safety.navigation systems to more applications, two fundamental problems must be resolved: the lane-level mapproposes a novel seven-layer map structure, called as Tsinghua map model, which is able to support autonomousdriving in a flexible and efficient way.

Keywords： Lane-level     Route planning     Tsinghua map model     Travel cost model    

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Enhanced Autonomous Exploration and Mapping of an Unknown Environment with the Fusion of Dual RGB-D Sensors Article

Ningbo Yu, Shirong Wang

Engineering 2019, Volume 5, Issue 1,   Pages 164-172 doi: 10.1016/j.eng.2018.11.014

Abstract:

The autonomous exploration and mapping of an unknown environment is useful in a wide range of applicationsIn this paper, we present a systematic approach with dual RGB-D sensors to achieve the autonomous explorationWith the synchronized and processed RGB-D data, location points were generated and a 3D point cloud mapand 2D grid map were incrementally built.Partial map simulation and global frontier search methods were combined for autonomous exploration, and

Keywords： Autonomous exploration     Red/green/blue-depth     Sensor fusion     Point cloud     Partial map simulation     Global    

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Large-Scale Vehicle Platooning: Advances and Challenges in Scheduling and Planning Techniques Review

Jing Hou, Guang Chen, Jin Huang, Yingjun Qiao, Lu Xiong, Fuxi Wen, Alois Knoll, Changjun Jiang

Engineering 2023, Volume 28, Issue 9,   Pages 26-48 doi: 10.1016/j.eng.2023.01.012

Abstract: The gradual maturation of platoon control technology is enabling vehicle platoons to achieve basic driving

Keywords： Autonomous vehicle platoon     Autonomous driving     Connected and automated vehicles     Scheduling and planning    

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Spatiotemporal evolution and driving factors for GHG emissions of aluminum industry in China

Frontiers in Energy 2023, Volume 17, Issue 2,   Pages 294-305 doi: 10.1007/s11708-022-0819-7

Abstract: Decomposition analysis is also performed to uncover the driving factors of GHG emission generated from

Keywords： aluminum     material flow analysis     GHG (greenhouse gas) emissions     LMDI (logarithmic mean divisa index)    

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An autonomous miniature wheeled robot based on visual feedback control

CHEN Haichu

Frontiers of Mechanical Engineering 2007, Volume 2, Issue 2,   Pages 197-200 doi: 10.1007/s11465-007-0033-7

Abstract: micro-motors, a novel omni-direction miniature wheeled robot is designed on the basis of the bi-corner drivingvisual navigation and control system allow the robot to navigate and track the target and to accomplish autonomous

Keywords： measuring     distance     autonomous locomotion     advantage     navigation    

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Embedding expert demonstrations into clustering buffer for effective deep reinforcement learning Research Article

Shihmin WANG, Binqi ZHAO, Zhengfeng ZHANG, Junping ZHANG, Jian PU

Frontiers of Information Technology & Electronic Engineering 2023, Volume 24, Issue 11,   Pages 1541-1556 doi: 10.1631/FITEE.2300084

Abstract: As one of the most fundamental topics in (RL), is essential to the deployment of deep RL algorithms. Unlike most existing exploration methods that sample an action from different types of posterior distributions, we focus on the policy and propose an efficient selective sampling approach to improve by modeling the internal hierarchy of the environment. Specifically, we first employ in the policy to generate an action candidate set. Then we introduce a clustering buffer for modeling the internal hierarchy, which consists of on-policy data, off-policy data, and expert data to evaluate actions from the clusters in the action candidate set in the exploration stage. In this way, our approach is able to take advantage of the supervision information in the expert demonstration data. Experiments on six different continuous locomotion environments demonstrate superior performance and faster convergence of selective sampling. In particular, on the LGSVL task, our method can reduce the number of convergence steps by 46.7% and the convergence time by 28.5%. Furthermore, our code is open-source for reproducibility. The code is available at https://github.com/Shihwin/SelectiveSampling.

Keywords： Reinforcement learning     Sample efficiency     Sampling process     Clustering methods     Autonomous driving    

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Pre-training with asynchronous supervised learning for reinforcement learning based autonomous driving Research Articles

Yunpeng Wang, Kunxian Zheng, Daxin Tian, Xuting Duan, Jianshan Zhou,ypwang@buaa.edu.cn,zhengkunxian@buaa.edu.cn,dtian@buaa.edu.cn,duanxuting@buaa.edu.cn

Frontiers of Information Technology & Electronic Engineering 2021, Volume 22, Issue 5,   Pages 615-766 doi: 10.1631/FITEE.1900637

Abstract: Rule-based autonomous driving systems may suffer from increased complexity with large-scale inter-coupled(RL) has been applied in designing autonomous driving systems because of its outstanding performancedriving system.We propose an asynchronous (ASL) method for the RL-based end-to-end autonomous driving model to addressdriving model in the RL training stage.

Keywords： 自主驾驶；自动驾驶车辆；强化学习；监督学习    

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Toward autonomous mining: design and development of an unmanned electric shovel via point cloud-based

Frontiers of Mechanical Engineering 2022, Volume 17, Issue 3, doi: 10.1007/s11465-022-0686-2

Abstract: In the field of autonomous excavation, environmental perception and excavation trajectory planning areBased on optimal trajectory command, the UES performs autonomous excavation.

Keywords： autonomous excavation     unmanned electric shovel     point cloud     excavation trajectory planning    

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Title Author Date Type Operation

Thoughts and Suggestions on Autonomous Driving Map Policy

Liu Jingnan, Dong Yang, Zhan Jiao, Gao Kefu

Journal Article

Evolutionary Decision-Making and Planning for Autonomous Driving Based on Safe and Rational Exploration

Kang Yuan,Yanjun Huang,Shuo Yang,Zewei Zhou,Yulei Wang,Dongpu Cao,Hong Chen,

Journal Article

A Probabilistic Architecture of Long-Term Vehicle Trajectory Prediction for Autonomous Driving

Jinxin Liu, Yugong Luo, Zhihua Zhong, Keqiang Li, Heye Huang, Hui Xiong

Journal Article

Towards the Unified Principles for Level 5 Autonomous Vehicles

Jianqiang Wang, Heye Huang, Keqiang Li, Jun Li

Journal Article

Autonomous Driving in the iCity—HD Maps as a Key Challenge of the Automotive Industry

Heiko G. Seif, Xiaolong Hu

Journal Article

Toward Human-in-the-loop AI: Enhancing Deep Reinforcement Learning Via Real-time Human Guidance for AutonomousDriving

Jingda Wu, Zhiyu Huang, Zhongxu Hu, Chen Lv

Journal Article

Semantic Consistency and Correctness Verification of Digital Traffic Rules

Lei Wan,Changjun Wang,Daxin Luo,Hang Liu,Sha Ma,Weichao Hu,

Journal Article

A Flexible Multi-Layer Map Model Designed for Lane-Level Route Planning in Autonomous Vehicles

Kun Jiang, Diange Yang, Chaoran Liu, Tao Zhang, Zhongyang Xiao

Journal Article

Enhanced Autonomous Exploration and Mapping of an Unknown Environment with the Fusion of Dual RGB-D Sensors

Ningbo Yu, Shirong Wang

Journal Article

Large-Scale Vehicle Platooning: Advances and Challenges in Scheduling and Planning Techniques

Jing Hou, Guang Chen, Jin Huang, Yingjun Qiao, Lu Xiong, Fuxi Wen, Alois Knoll, Changjun Jiang

Journal Article

Spatiotemporal evolution and driving factors for GHG emissions of aluminum industry in China

Journal Article

An autonomous miniature wheeled robot based on visual feedback control

CHEN Haichu

Journal Article

Embedding expert demonstrations into clustering buffer for effective deep reinforcement learning

Shihmin WANG, Binqi ZHAO, Zhengfeng ZHANG, Junping ZHANG, Jian PU

Journal Article

Pre-training with asynchronous supervised learning for reinforcement learning based autonomous driving

Yunpeng Wang, Kunxian Zheng, Daxin Tian, Xuting Duan, Jianshan Zhou,ypwang@buaa.edu.cn,zhengkunxian@buaa.edu.cn,dtian@buaa.edu.cn,duanxuting@buaa.edu.cn

Journal Article

Toward autonomous mining: design and development of an unmanned electric shovel via point cloud-based

Journal Article