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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 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

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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 rules, so many researchers are exploring learning-based approaches. (RL) has been applied in designing autonomous driving systems because of its outstanding performance on a wide variety of sequential control problems. However, poor initial performance is a major challenge to the practical implementation of an RL-based autonomous driving system. RL training requires extensive training data before the model achieves reasonable performance, making an RL-based model inapplicable in a real-world setting, particularly when data are expensive. We propose an asynchronous (ASL) method for the RL-based end-to-end autonomous driving model to address the problem of poor initial performance before training this RL-based model in real-world settings. Specifically, prior knowledge is introduced in the ASL pre-training stage by asynchronously executing multiple processes in parallel, on multiple driving demonstration data sets. After pre-training, the model is deployed on a real vehicle to be further trained by RL to adapt to the real environment and continuously break the performance limit. The presented pre-training method is evaluated on the race car simulator, TORCS (The Open Racing Car Simulator), to verify that it can be sufficiently reliable in improving the initial performance and convergence speed of an end-to-end autonomous driving model in the RL training stage. In addition, a real-vehicle verification system is built to verify the feasibility of the proposed pre-training method in a real-vehicle deployment. Simulations results show that using some demonstrations during a supervised pre-training stage allows significant improvements in initial performance and convergence speed 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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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

Abstract:

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

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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 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

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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:

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

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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

Abstract: 增强智能的基本要素：直觉推理与因果模型、记忆和知识演化；特别论述了直觉推理在复杂问题求解中的作用和基本原理，以及基于记忆与推理的视觉场景理解的认知学习网络；阐述了竞争-对抗式认知学习方法，并讨论了其在自动驾驶方面的应用

Keywords：人-机协同；混合增强智能；认知计算；直觉推理；因果模型；认知映射；视觉场景理解；自主驾驶汽车

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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

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Independent Development：The Way to Build up the Strength of a Country

Jin Lüzhong

Strategic Study of CAE 2005, Volume 7, Issue 7, Pages 1-6

Abstract:

The paper expounds the significance of independent development in the fields of politics, military affairs, economy, security, science and technology, etc. It suggests that most of industries in China be provided with the capability of independent development and international competitiveness within 10〜15 years. It analyzes the possibility for achieving this goal. The paper also suggests that the environment favorable to independent development be created in the aspects of ideology, spirit, laws and regulations, policies, system as well as dissemination and public opinion.

Keywords： independent development goal possibility

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Robot Pilot: A New Autonomous System Toward Flying Manned Aerial Vehicles Article

Zibo Jin, Daochun Li, Jinwu Xiang

Engineering 2023, Volume 27, Issue 8, Pages 242-253 doi: 10.1016/j.eng.2022.10.018

Abstract:

The robot pilot is a new concept of a robot system that pilots a manned aircraft, thereby forming a new type of unmanned aircraft system (UAS) that makes full use of the platform maturity, load capacity, and airworthiness of existing manned aircraft while greatly expanding the operation and application fields of UASs. In this research, the implementation and advantages of the robot pilot concept are discussed in
detail, and a helicopter robot pilot is proposed to fly manned helicopters. The robot manipulators are designed according to the handling characteristics of the helicopter-controlling mechanism. Based on a kinematic analysis of the robot manipulators, a direct-driving method is established for the robot flight controller to reduce the time delay and control error of the robot servo process. A supporting ground station
is built to realize different flight modes and the functional integration of the robot pilot. Finally, a prototype of the helicopter robot pilot is processed and installed in a helicopter to carry out flight tests. The test results show that the robot pilot can independently fly the helicopter to realize forward flight, backward flight, side flight, and turning flight, which verifies the effectiveness of the helicopter robot pilot.

Keywords： Helicopter Robot pilot Flight control Unmanned system

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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

Abstract:

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

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Safety Risks of Self-driving Vehicle: Identification and Measurement

Dou Wenyue, Hu Ping, Wei Ping, Zheng Nanning

Strategic Study of CAE 2021, Volume 23, Issue 6, Pages 167-177 doi: 10.15302/J-SSCAE-2021.06.016

Abstract:

Self-driving vehicle is a hot application of artificial intelligence, and the identification and measurement of its safety risks has become an urgent research topic in the field of artificial intelligence safety. In this study, we collect qualitative information through case interviews and identify the key elements of safety risks using the qualitative research method and the grounded theory. Further, we propose for the first time a six-element frame for the safety risks of self-driving vehicle. These elements include single vehicle safety, networking safety, technological level, legal policies, public opinion, and industrial risks. Subsequently, we design a questionnaire and conduct two online questionnaires surveys to measure the safety risk elements. To cope with future safety risks of self-driving vehicle, enterprises should strengthen the research and manufacturing of key components, increase investment in information security, participate in the formulation of industry standards and regulations, and maintain a sustainable development. The government should strengthen the supervision over self-driving vehicle tests, improve regulations and standards, and guide talent training. Consumers should keep good driving habits and maintain rational regarding self-driving vehicle.

Keywords： self-driving vehicle safety risk risk identification risk measurement

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Efficient normalization for quantitative evaluation of the driving behavior using a gated auto-encoder Research Articles

Xin HE, Zhe ZHANG, Li XU, Jiapei YU,xinhe_ee@zju.edu.cn,xupower@zju.edu.cn

Frontiers of Information Technology & Electronic Engineering 2022, Volume 23, Issue 3, Pages 452-462 doi: 10.1631/FITEE.2000667

Abstract: is important for a fair evaluation of the driving style. The longitudinal control of a vehicle is investigated in this study. The task can be considered as mapping of the in a different environment to the uniform condition. Unlike the model-based approach as in previous work, where a necessary driver model is employed to conduct the driving cycle test, the approach we propose directly normalizes the using an auto-encoder (AE) when following a standard speed profile. To ensure a positive correlation between the vehicle speed and , a gate constraint is imposed in between the encoder and decoder to form a gated AE (gAE). This approach is model-free and efficient. The proposed approach is tested for consistency with the model-based approach and for its applications to of the and fuel consumption analysis. Simulations are conducted to verify the effectiveness of the proposed scheme.

Keywords： Driving behavior Normalization Gated auto-encoder Quantitative evaluation

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European Union Puts Teeth in Right to Repair

Sean O´Neill

Engineering 2021, Volume 7, Issue 9, Pages 1197-1198 doi: 10.1016/j.eng.2021.07.007

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Studies on Autonomous Navigation Techniques for Navigation Constellations

Shuai Ping,Qu Guangji,Chen Zhonggui

Strategic Study of CAE 2006, Volume 8, Issue 3, Pages 22-30

Abstract:

If the autonomous navigation techniques are applied to a navigation constellation, the number of the ground control stations, the message injecting frequency from the ground stations to satellites and system maintenance cost may be reduced significantly. Meanwhile, the integrity of navigation messages can be monitored in the real-time mode and the survivability of the navigation system also enhanced. The autonomous navigation will be gradually a main research subject for the next generation navigation satellite system. Firstly, the information processing flow of the autonomous navigation constellation is described systematically in this paper. Secondly, the key techniques to realize the autonomous navigation for the navigation constellation, including the long-term prediction of the ephemerides and clock parameters, establishment and maintenance for the cross measurement and communication links, autonomous time synchronization, autonomous ephemeris updating, robust filtering, establishment of the constellation rotation models, long-term prediction of the earth rotation and polar drift parameters, are presented as an important part of the paper. Moreover, the approaches to actualize the key techniques are analyzed and the relative mathematic models are also demonstrated in detail. Finally, the autonomous time synchronization and ephemeris updating are simulated. It is clearly shown from the simulated results that by processing the inter-satellite measurement data with the on-board Kalman filters and updating continually the clock parameters and ephemerides, the time synchronization and ephemeris updating among the satellites can be implemented autonomously and highly-accurately. Therefore, the reasonability and feasibility of the information processing flow and key technical algorithms for the autonomous navigation constellation are validated preliminarily.

Keywords： navigation constellation autonomous navigation information processing flow system simulation

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

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Title Author Date Type Operation

Towards the Unified Principles for Level 5 Autonomous Vehicles

Jianqiang Wang, Heye Huang, Keqiang Li, Jun Li

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

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

混合-增强智能：协作与认知

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