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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
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
Building Chinese Brand Parts System to Consolidate the Foundation of Automobile Power
Li Kaiguo,Deng Xiaozhi,Shen Bin and Wu Shengnan
Strategic Study of CAE 2018, Volume 20, Issue 1, Pages 133-138 doi: 10.15302/J-SSCAE-2018.01.019
The automobile parts industry is the foundation of automobile power and is the main driving force promoting the development of the automobile industry. All automobile power countries have a strong parts industry. Now China is in a critical period of growing from a large automobile to automobile power, but it is difficult for the development of the parts industry to support the development of the automobile industry. Through the analysis of problems and opportunities for the development of China’s parts industry, we suggest building a complete independent parts system through strengthening the industrial base, expanding the scale of independent support, and cultivating leading companies to consolidate the development foundation and support the development of automobile power.
Keywords： automobile power component system independent suggestion
The Way in Development of China's Auto Industry: Cooperation & Self-Reliance
Guo Konghui
Strategic Study of CAE 2004, Volume 6, Issue 8, Pages 17-20
The paper gives an observation and review of serious arguments on the way in development of China's auto industry, presents some points on the way out for the auto industry, and explains that China will be able to march on the sustainable development path through balancing win-win cooperation against selfreliance.
Keywords： china's auto industry way in development cooperation & self-reliance
混合-增强智能：协作与认知 Review
南宁 郑,子熠 刘,鹏举 任,永强 马,仕韬 陈,思雨 余,建儒 薛,霸东 陈,飞跃 王
Frontiers of Information Technology & Electronic Engineering 2017, Volume 18, Issue 2, Pages 153-179 doi: 10.1631/FITEE.1700053
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
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
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
Developing China's Auto Industry Through the “CHERY” Pattern
Jin Lüzhong
Strategic Study of CAE 2004, Volume 6, Issue 9, Pages 9-13
Only a few years since its founding, CHERY has not only developed 4 new style cars, part of which are exported, through its efforts, but also been the first car enterprise that establishes car joint venture abroad. That is the road for developing China's auto industry. The government should support such auto enterprises as CHERY, GEELY and HAFEI, which take the road of self-reliance development and proprietary innovation. Preferential policies should be given to them so that they can grow bigger and stronger.
Keywords： auto industry the CHERY pattern self-reliance development preferential policy
Research on Development Strategy of Unmanned Driving Technology
Yang Yanming, Gao Zenggui, Zhang Zilong, Shen Yue, Wang Linjun
Strategic Study of CAE 2018, Volume 20, Issue 6, Pages 101-104 doi: 10.15302/J-SSCAE-2018.06.016
Unmanned driving technology is considered as a highly disruptive technology in the field of vehicle engineering. By means of expert interviews, comparative analysis and literature research, this paper analyses the development trend and stage of unmanned driving technology, and evaluates its potential values and social impacts. It also proposes to promote the development of unmanned driving technology by strengthening the research on automobile wire control technology, energy power technology and driving cognitive technology.
Keywords： disruptive technology unmanned driving technology automotive wire control technology energy power technology driving cognitive technology
Development Strategies for Improving Manufacturing Quality in China’s Automobile Industry
Lin Zhongqin,Zhao Yixi and Pan Ershun
Strategic Study of CAE 2018, Volume 20, Issue 1, Pages 45-51 doi: 10.15302/J-SSCAE-2018.01.007
This paper analyzes the problems faced by the automobile manufacturing industry in China, including low quality and efficiency, weak competitiveness, insufficient high-mid-level supply ability, pressures due to highly competitive overseas brands and less approved status of independent brands, and the worrying situation about independent auto parts brands. Subsequently, the long-term bottlenecks that beset the development of the automobile industry in China are summarized, such as the weak foundations of good quality technology and manufacturing, as well as the lack of core technology, innovation, and a generic research platform. This paper puts forward the guiding ideology and phase-wise goals to improve the manufacturing quality from the perspective of developing new energy and intelligently connected vehicles, implementing automotive quality improvement projects, and other development strategies.
Keywords： automobile manufacturing industry quality independent brands development strategy
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
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 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
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
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
An increasing number of drivers are relying on digital map navigation systems in vehicles or mobile phones to select optimal driving routes in order to save time and improve safety. In the near future, digital map navigation systems are expected to play more important roles in transportation systems. In order to extend current navigation systems to more applications, two fundamental problems must be resolved: the lane-level map model and lane-level route planning. This study proposes solutions to both problems. The current limitation of the lane-level map model is not its accuracy but its flexibility; this study proposes a novel seven-layer map structure, called as Tsinghua map model, which is able to support autonomous driving in a flexible and efficient way. For lane-level route planning, we propose a hierarchical route-searching algorithm to accelerate the planning process, even in the presence of complicated lane networks. In addition, we model the travel costs allocated for lane-level road networks by analyzing vehicle maneuvers in traversing lanes, changing lanes, and turning at intersections. Tests were performed on both a grid network and a real lane-level road network to demonstrate the validity and efficiency of the proposed algorithm.
Keywords： Lane-level Route planning Tsinghua map model Travel cost model
Title Author Date Type Operation
Towards the Unified Principles for Level 5 Autonomous Vehicles
Jianqiang Wang, Heye Huang, Keqiang Li, Jun Li
Journal Article
Building Chinese Brand Parts System to Consolidate the Foundation of Automobile Power
Li Kaiguo,Deng Xiaozhi,Shen Bin and Wu Shengnan
Journal Article
The Way in Development of China's Auto Industry: Cooperation & Self-Reliance
Guo Konghui
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
Autonomous Driving in the iCity—HD Maps as a Key Challenge of the Automotive Industry
Heiko G. Seif, Xiaolong Hu
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
Research on Development Strategy of Unmanned Driving Technology
Yang Yanming, Gao Zenggui, Zhang Zilong, Shen Yue, Wang Linjun
Journal Article
Development Strategies for Improving Manufacturing Quality in China’s Automobile Industry
Lin Zhongqin,Zhao Yixi and Pan Ershun
Journal Article
A Hardware Platform Framework for an Intelligent Vehicle Based on a Driving Brain
Deyi Li,Hongbo Gao
Journal Article
Thoughts and Suggestions on Autonomous Driving Map Policy
Liu Jingnan, Dong Yang, Zhan Jiao, Gao Kefu
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
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