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Toward Trustworthy Decision-Making for Autonomous Vehicles: A Robust Reinforcement Learning Approach

Xiangkun He,Wenhui Huang,Chen Lv,

Engineering doi: 10.1016/j.eng.2023.10.005

Abstract: While autonomous vehicles are vital components of intelligent transportation systems, ensuring the trustworthinessof decision-making remains a substantial challenge in realizing autonomous driving.Therefore, we present a novel robust reinforcement learning approach with safety guarantees to attaintrustworthy decision-making for autonomous vehicles.These results indicate that the autonomous driving agent can make trustworthy decisions and drastically

Keywords： Autonomous vehicle Decision-making Reinforcement learning Adversarial attack Safety guarantee

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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 challengesautomation level and vehicle intelligence, these systems
can be further advanced towards fully autonomousBy exploring the physical mechanisms behind high-level autonomous driving systems and analyzing the essencemode relying on the crow inference and parrot imitation approach, we explore the research paradigm of autonomouslearning and prior knowledge to realize the characteristics of self-learning, self-adaptation, and self-transcendence

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

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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 autonomousFirst, a data-driven decision-making module based on deep reinforcement learning (DRL) is developed toFinally, 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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Toward Human-in-the-loop AI: Enhancing Deep Reinforcement Learning Via Real-time Human Guidance for Autonomous 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:

Due to its limited intelligence and abilities, machine learning is currentlytraining loop of artificial intelligence (AI), leveraging human intelligence to further advance machine learningIn this study, a real-time human-guidance-based (Hug)-deep reinforcement learning (DRL) method is developedfor policy training in an end-to-end autonomous driving case.validated by human-in-the-loop experiments with 40 subjects and compared with other state-of-the-art learning

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

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Stochastic pedestrian avoidance for autonomous vehicles using hybrid reinforcement learning Research Article

Huiqian LI, Jin HUANG, Zhong CAO, Diange YANG, Zhihua ZHONG,lihq20@mails.tsinghua.edu.cn,huangjin@tsinghua.edu.cn,caoc15@mails.tsinghua.edu.cn,ydg@tsinghua.edu.cn

Frontiers of Information Technology & Electronic Engineering 2023, Volume 24, Issue 1, Pages 131-140 doi: 10.1631/FITEE.2200128

Abstract: Classical avoidance strategies cannot handle uncertainty, and learning-based methods lack performanceThe method integrates the rule-based strategy and reinforcement learning strategy.

Keywords： Pedestrian Hybrid reinforcement learning Autonomous vehicles Decision-making

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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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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: visual 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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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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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-coupledrules, so many researchers are exploring learning-based approaches.(RL) has been applied in designing autonomous driving systems because of its outstanding performanceHowever, poor initial performance is a major challenge to the practical implementation of an RL-based autonomousWe propose an asynchronous (ASL) method for the RL-based end-to-end autonomous driving model to address

Keywords：自主驾驶；自动驾驶车辆；强化学习；监督学习

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Extended model predictive control scheme for smooth path following of autonomous vehicles

Frontiers of Mechanical Engineering 2022, Volume 17, Issue 1, Pages 4-4 doi: 10.1007/s11465-021-0660-4

Abstract: presents an extended model predictive control (MPC) scheme for implementing optimal path following of autonomousconstraints, which can improve the path following quality for better ride comfort and road availability of autonomous

Keywords： autonomous vehicles vehicle dynamic modeling model predictive control path following optimization

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Tall Buildings with Dynamic Facade Under Winds Article

Fei Ding, Ahsan Kareem

Engineering 2020, Volume 6, Issue 12, Pages 1443-1453 doi: 10.1016/j.eng.2020.07.020

Abstract: To leap beyond the static shape optimization, autonomous dynamic morphing of the building shape is advanced

Keywords： Tall buildings Aerodynamic shape tailoring Autonomous morphing Cyber–physical system Computationaldesign Surrogate modeling Machine learning

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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.driving maps in China, i.e., encryption of autonomous driving maps, limitations on geographic informationMeanwhile, combining the development trends of domestic and international autonomous driving fields,and orderly opening of autonomous driving maps, appropriately opening up corporate authorization and

Keywords： autonomous driving map autonomous driving regulation autonomous driving policy

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General Optimal Trajectory Planning: Enabling Autonomous Vehicles with the Principle of Least Action

Heye Huang,Yicong Liu,Jinxin Liu,Qisong Yang,Jianqiang Wang,David Abbink,Arkady Zgonnikov,

Engineering doi: 10.1016/j.eng.2023.10.001

Abstract: This study presents a general optimal trajectory planning (GOTP) framework for autonomous vehicles (AVs

Keywords： Autonomous vehicle Trajectory planning Multi-performance objectives Principle of least action

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Current situation and development of wind power in China

BAO Nengsheng, NI Weidou

Frontiers in Energy 2007, Volume 1, Issue 4, Pages 371-383 doi: 10.1007/s11708-007-0056-4

Abstract: Many regions such as Xinjiang Uygur Autonomous Region, Inner Mongolia Autonomous Region and southeast

Keywords： Xinjiang current development Mongolia Autonomous southeast coastal Autonomous

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Longitudinal and lateral slip control of autonomous wheeled mobile robot for trajectory tracking

Hamza KHAN,Jamshed IQBAL,Khelifa BAIZID,Teresa ZIELINSKA

Frontiers of Information Technology & Electronic Engineering 2015, Volume 16, Issue 2, Pages 166-172 doi: 10.1631/FITEE.1400183

Abstract: This research formulates a path-following control problem subjected to wheel slippage and skid and solves it using a logic-based control scheme for a wheeled mobile robot (WMR). The novelty of the proposed scheme lies in its methodology that considers both longitudinal and lateral slip components. Based on the derived slip model, the controller for longitudinal motion slip has been synthesized. Various control parameters have been studied to investigate their effects on the performance of the controller resulting in selection of their optimum values. The designed controller for lateral slip or skid is based on the proposed side friction model and skid check condition. Considering a car-like WMR, simulation results demonstrate the effectiveness of the proposed control scheme. The robot successfully followed the desired circular trajectory in the presence of wheel slippage and skid. This research finds its potential in various applications involving WMR navigation and control.

Keywords： Robot modeling Robot navigation Slip and skid control Wheeled mobile robots

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Title Author Date Type Operation

Toward Trustworthy Decision-Making for Autonomous Vehicles: A Robust Reinforcement Learning Approach

Xiangkun He,Wenhui Huang,Chen Lv,

Journal Article

Towards the Unified Principles for Level 5 Autonomous Vehicles

Jianqiang Wang, Heye Huang, Keqiang Li, Jun Li

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

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

Jingda Wu, Zhiyu Huang, Zhongxu Hu, Chen Lv

Journal Article

Stochastic pedestrian avoidance for autonomous vehicles using hybrid reinforcement learning

Huiqian LI, Jin HUANG, Zhong CAO, Diange YANG, Zhihua ZHONG,lihq20@mails.tsinghua.edu.cn,huangjin@tsinghua.edu.cn,caoc15@mails.tsinghua.edu.cn,ydg@tsinghua.edu.cn

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

An autonomous miniature wheeled robot based on visual feedback control

CHEN Haichu

Journal Article

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

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

Extended model predictive control scheme for smooth path following of autonomous vehicles

Journal Article

Tall Buildings with Dynamic Facade Under Winds

Fei Ding, Ahsan Kareem

Journal Article

Thoughts and Suggestions on Autonomous Driving Map Policy

Liu Jingnan, Dong Yang, Zhan Jiao, Gao Kefu

Journal Article

General Optimal Trajectory Planning: Enabling Autonomous Vehicles with the Principle of Least Action

Heye Huang,Yicong Liu,Jinxin Liu,Qisong Yang,Jianqiang Wang,David Abbink,Arkady Zgonnikov,

Journal Article