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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
Keywords: 自主驾驶;自动驾驶车辆;强化学习;监督学习
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
Due to its limited intelligence and abilities, machine learning is currently unable to handle various situations thus cannot completely replace humans in real-world applications. Because humans exhibit robustness and adaptability in complex scenarios, it is crucial to introduce humans into the training loop of artificial intelligence (AI), leveraging human intelligence to further advance machine learning algorithms. In this study, a real-time human-guidance-based (Hug)-deep reinforcement learning (DRL) method is developed for policy training in an end-to-end autonomous driving case. With our newly designed mechanism for control transfer between humans and automation, humans are able to intervene and correct the agent's unreasonable actions in real time when necessary during the model training process. Based on this human-in-the-loop guidance mechanism, an improved actor-critic architecture with modified policy and value networks is developed. The fast convergence of the proposed Hug-DRL allows real-time human guidance actions to be fused into the agent's training loop, further improving the efficiency and performance of DRL. The developed method is validated by human-in-the-loop experiments with 40 subjects and compared with other state-of-the-art learning approaches. The results suggest that the proposed method can effectively enhance the training efficiency and performance of the DRL algorithm under human guidance without imposing specific requirements on participants' expertise or experience.
Keywords: Human-in-the-loop AI Deep reinforcement learning Human guidance Autonomous driving
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
Keywords: Pedestrian Hybrid reinforcement learning Autonomous vehicles Decision-making
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
The rapid advance of autonomous vehicles (AVs) has motivated new perspectives and potential challenges for existing modes of transportation. Currently, driving assistance systems of Level 3 and below have been widely produced, and several applications of Level 4 systems to specific situations have also been gradually developed. By improving the automation level and vehicle intelligence, these systems
can be further advanced towards fully autonomous driving. However, general development concepts for Level 5 AVs remain unclear, and the existing methods employed in the development processes of Levels 0–4 have been mainly based on task-driven function development related to specific scenarios. Therefore, it is difficult to identify the problems encountered by high-level AVs. The essential logical
and physical mechanisms of vehicles have hindered further progression towards Level 5 systems. By exploring the physical mechanisms behind high-level autonomous driving systems and analyzing the essence of driving, we put forward a coordinated and balanced framework based on the brain–cerebellum–organ concept through reasoning and deduction. Based on a mixed mode relying on the crow inference and parrot imitation approach, we explore the research paradigm of autonomous learning and prior knowledge to realize the characteristics of self-learning, self-adaptation, and self-transcendence for AVs. From a systematic, unified, and balanced point of view and based on least action principles and unified safety field concepts, we aim to provide a novel research concept and develop an effective approach for the research and development of high-level AVs, specifically at Level 5.
Keywords: Autonomous vehicle Principle of least action Driving safety field Autonomous learning Basic paradigm
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
Keywords: Reinforcement learning Sample efficiency Sampling process Clustering methods Autonomous driving
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
In mixed and dynamic traffic environments, accurate long-term trajectory forecasting of surrounding vehicles is one of the indispensable preconditions for autonomous vehicles (AVs) to accomplish reasonable behavioral decisions and guarantee driving safety. In this paper, we propose an integrated probabilistic architecture for long-term vehicle trajectory prediction, which consists of a driving inference model (DIM) and a trajectory prediction model (TPM). The DIM is designed and employed to accurately infer the potential driving intention based on a dynamic Bayesian network. The proposed DIM incorporates the basic traffic rules and multivariate vehicle motion information. To further improve the prediction accuracy and realize uncertainty estimation, we develop a Gaussian process (GP)-based TPM, considering both the short-term prediction results of the vehicle model and the driving motion characteristics. Afterward, the effectiveness of our novel approach is demonstrated by conducting experiments on a public naturalistic driving dataset under lane-changing scenarios. The superior performance on the task of long-term trajectory prediction is presented and verified by comparing with other advanced methods.
Keywords: Autonomous driving Dynamic Bayesian network Driving intention recognition Gaussian process Vehicle trajectory prediction
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
As a key infrastructure to realize autonomous driving, autonomous driving map is crucial to the commercial development of the autonomous driving field in China. Currently, constrained by laws and regulations related to ground mapping, map application, and supervision, the commercialization of autonomous driving maps in China is lagging behind. This paper focuses on the main policy and regulatory issues faced in the development, application, and management of autonomous driving maps in China, i.e., encryption of autonomous driving maps, limitations on geographic information expression, qualifications for geographic information collection and the process for map review, accident liability and insurance issues, as well as autonomous driving map related test specifications and test scenario issues. Meanwhile, combining the development trends of domestic and international autonomous driving fields, this paper proposes four suggestions for accelerating the development and commercialization of autonomous driving vehicles in China: formulating an autonomous driving map management mode, allowing pilot application and orderly opening of autonomous driving maps, appropriately opening up corporate authorization and optimizing the review process, as well as establishing a national-level autonomous driving map platform.
Keywords: autonomous driving map autonomous driving regulation autonomous driving policy
A self-supervised method for treatment recommendation in sepsis Research Articles
Sihan Zhu, Jian Pu,jianpu@fudan.edu.cn
Frontiers of Information Technology & Electronic Engineering 2021, Volume 22, Issue 7, Pages 926-939 doi: 10.1631/FITEE.2000127
Keywords: 治疗推荐;脓毒症;自监督学习;强化学习;电子病历
Fully Self-driving Future Hits the Brakes
Chris Palmer
Engineering 2023, Volume 26, Issue 7, Pages 6-8 doi: 10.1016/j.eng.2023.05.002
Representation learning via a semi-supervised stacked distance autoencoder for image classification Research Articles
Liang Hou, Xiao-yi Luo, Zi-yang Wang, Jun Liang,jliang@zju.edu.cn
Frontiers of Information Technology & Electronic Engineering 2020, Volume 21, Issue 7, Pages 963-1118 doi: 10.1631/FITEE.1900116
Keywords: 自动编码器;图像分类;半监督学习;神经网络
Learning to select pseudo labels: a semi-supervised method for named entity recognition Research Articles
Zhen-zhen Li, Da-wei Feng, Dong-sheng Li, Xi-cheng Lu,lizhenzhen14@nudt.edu.cn,davyfeng.c@gmail.com,dsli@nudt.edu.cn,xclu@nudt.edu.cn
Frontiers of Information Technology & Electronic Engineering 2020, Volume 21, Issue 6, Pages 809-962 doi: 10.1631/FITEE.1800743
Keywords: 命名实体识别;无标注数据;深度学习;半监督学习方法
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
This article provides in-depth insights into the necessary technologies for automated driving in future cities. State of science is reflected from different perspectives such as in-car computing and data management, road side infrastructure, and cloud solutions. Especially the challenges for the application of HD maps as core technology for automated driving are depicted in this article.
Keywords: Autonomous driving Traffic infrastructure iCity Car-to-X communication Connected vehicle HD maps
A Hardware Platform Framework for an Intelligent Vehicle Based on a Driving Brain Article
Deyi Li,Hongbo Gao
Engineering 2018, Volume 4, Issue 4, Pages 464-470 doi: 10.1016/j.eng.2018.07.015
The type, model, quantity, and location of sensors installed on the intelligent vehicle test platform are different, resulting in different sensor information processing modules. The driving map used in intelligent vehicle test platform has no uniform standard, which leads to different granularity of driving map information. The sensor information processing module is directly associated with the driving map information and decision-making module, which leads to the interface of intelligent driving system software module has no uniform standard. Based on the software and hardware architecture of intelligent vehicle, the sensor information and driving map information are processed by using the formal language of driving cognition to form a driving situation graph cluster and output to a decision-making module, and the output result of the decision-making module is shown as a cognitive arrow cluster, so that the whole process of intelligent driving from perception to decision-making is completed. The formalization of driving cognition reduces the influence of sensor type, model, quantity, and location on the whole software architecture, which makes the software architecture portable on different intelligent driving hardware platforms.
Keywords: Driving brain Intelligent driving Hardware platform framework
Interactive medical image segmentation with self-adaptive confidence calibration
沈楚云,李文浩,徐琪森,胡斌,金博,蔡海滨,朱凤平,李郁欣,王祥丰
Frontiers of Information Technology & Electronic Engineering 2023, Volume 24, Issue 9, Pages 1332-1348 doi: 10.1631/FITEE.2200299
Keywords: Medical image segmentation Interactive segmentation Multi-agent reinforcement learning Confidence learning Semi-supervised learning
NGAT: attention in breadth and depth exploration for semi-supervised graph representation learning Research Articles
Jianke HU, Yin ZHANG,yinzh@zju.edu.cn
Frontiers of Information Technology & Electronic Engineering 2022, Volume 23, Issue 3, Pages 409-421 doi: 10.1631/FITEE.2000657
Keywords: Graph learning Semi-supervised learning Node classification Attention
Title Author Date Type Operation
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 Human-in-the-loop AI: Enhancing Deep Reinforcement Learning Via Real-time Human Guidance for Autonomous Driving
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
Towards the Unified Principles for Level 5 Autonomous Vehicles
Jianqiang Wang, Heye Huang, Keqiang Li, Jun Li
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
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
Thoughts and Suggestions on Autonomous Driving Map Policy
Liu Jingnan, Dong Yang, Zhan Jiao, Gao Kefu
Journal Article
A self-supervised method for treatment recommendation in sepsis
Sihan Zhu, Jian Pu,jianpu@fudan.edu.cn
Journal Article
Representation learning via a semi-supervised stacked distance autoencoder for image classification
Liang Hou, Xiao-yi Luo, Zi-yang Wang, Jun Liang,jliang@zju.edu.cn
Journal Article
Learning to select pseudo labels: a semi-supervised method for named entity recognition
Zhen-zhen Li, Da-wei Feng, Dong-sheng Li, Xi-cheng Lu,lizhenzhen14@nudt.edu.cn,davyfeng.c@gmail.com,dsli@nudt.edu.cn,xclu@nudt.edu.cn
Journal Article
Autonomous Driving in the iCity—HD Maps as a Key Challenge of the Automotive Industry
Heiko G. Seif, Xiaolong Hu
Journal Article
A Hardware Platform Framework for an Intelligent Vehicle Based on a Driving Brain
Deyi Li,Hongbo Gao
Journal Article
Interactive medical image segmentation with self-adaptive confidence calibration
沈楚云,李文浩,徐琪森,胡斌,金博,蔡海滨,朱凤平,李郁欣,王祥丰
Journal Article