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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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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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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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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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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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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 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    

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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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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: Ensuring the safety of s is essential and challenging when are involved. Classical avoidance strategies cannot handle uncertainty, and learning-based methods lack performance guarantees. In this paper we propose a (HRL) approach for to safely interact with s behaving uncertainly. The method integrates the rule-based strategy and reinforcement learning strategy. The confidence of both strategies is evaluated using the data recorded in the training process. Then we design an activation function to select the final policy with higher confidence. In this way, we can guarantee that the final policy performance is not worse than that of the rule-based policy. To demonstrate the effectiveness of the proposed method, we validate it in simulation using an accelerated testing technique to generate stochastic s. The results indicate that it increases the success rate for avoidance to 98.8%, compared with 94.4% of the baseline method.

Keywords： Pedestrian     Hybrid reinforcement learning     Autonomous vehicles     Decision-making    

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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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A double-layered nonlinear model predictive control based control algorithm for local trajectory planning for automated trucks under uncertain road adhesion coefficient conditions Research Articles

Hong-chao Wang, Wei-wei Zhang, Xun-cheng Wu, Hao-tian Cao, Qiao-ming Gao, Su-yun Luo,17721336541@163.com,zwwsues@163.com,longxd2714@163.com,yjs_liqing@163.com,mosxsues@163.com,ly18362885604@163.com

Frontiers of Information Technology & Electronic Engineering 2020, Volume 21, Issue 7,   Pages 963-1118 doi: 10.1631/FITEE.1900185

Abstract: We present a double-layered control algorithm to plan the local trajectory for s equipped with four hub motors. The main layer of the proposed control algorithm consists of a main layer (MLN-MPC) controller and a secondary layer nonlinear MPC (SLN-MPC) controller. The MLN-MPC controller is applied to plan a dynamically feasible trajectory, and the SLN-MPC controller is designed to limit the of wheels within a stable zone to avoid the tire excessively slipping during traction. Overall, this is a closed-loop control system. Under the off-line co-simulation environments of AMESim, Simulink, dSPACE, and TruckSim, a dynamically feasible trajectory with collision avoidance operation can be generated using the proposed method, and the longitudinal wheel slip can be constrained within a stable zone so that the driving safety of the truck can be ensured under uncertain road surface conditions. In addition, the stability and robustness of the method are verified by adding a driver model to evaluate the application in the real world. Furthermore, simulation results show that there is lower computational cost compared with the conventional PID-based control method.

Keywords： Automated truck     Trajectory planning     Nonlinear model predictive control     Longitudinal slip    

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A Co-Point Mapping-Based Approach to Drivable Area Detection for Self-Driving Cars Article

Ziyi Liu,Siyu Yu,Nanning Zheng

Engineering 2018, Volume 4, Issue 4,   Pages 479-490 doi: 10.1016/j.eng.2018.07.010

Abstract:

The randomness and complexity of urban traffic scenes make it a difficult task for self-driving cars to detect drivable areas. Inspired by human driving behaviors, we propose a novel method of drivable area detection for self-driving cars based on fusing pixel information from a monocular camera with spatial information from a light detection and ranging (LIDAR) scanner. Similar to the bijection of collineation, a new concept called co-point mapping, which is a bijection that maps points from the LIDAR scanner to points on the edge of the image segmentation, is introduced in the proposed method. Our method positions candidate drivable areas through self-learning models based on the initial drivable areas that are obtained by fusing obstacle information with superpixels. In addition, a fusion of four features is applied in order to achieve a more robust performance. In particular, a feature called drivable degree (DD) is proposed to characterize the drivable degree of the LIDAR points. After the initial drivable area is characterized by the features obtained through self-learning, a Bayesian framework is utilized to calculate the final probability map of the drivable area. Our approach introduces no common hypothesis and requires no training steps; yet it yields a state-of-art performance when tested on the ROAD-KITTI benchmark. Experimental results demonstrate that the proposed method is a general and efficient approach for detecting drivable area.

Keywords： Drivable area     Self-driving     Data fusion     Co-point mapping    

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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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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 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    

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Fuzzy Control on Vehicle Motion Based on Subjective-objectiveJudgment of Driving Tenseness

Chen Xuemei and Gao Li

Strategic Study of CAE 2007, Volume 9, Issue 1,   Pages 53-57

Abstract:

It is very important whether a driver can give correct decision and precise operation in emergency． So，it is necessary to judge the emergency degree of environment and provide control algorithm of vehicle motion for ensuring the lives and properties’safety．The emergency degree is firstly given based on relative distance， velocity and drivers’characteristics．Then the control algorithm of vehicle motion based on fuzzy logic is established and is simulated with Simulink．The results show that the higher the emergency degree，the bigger the maximum deceleration is used to control the vehicle．The results also show that the drivers’characteristics have obvious effect on the braking operation．The fuzzy logic is valid to control vehicle’s deceleration．

Keywords： 　driver behavior；emergency；fuzzy logic；safety    

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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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Title Author Date Type Operation

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

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

Fully Self-driving Future Hits the Brakes

Chris Palmer

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 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

A Hardware Platform Framework for an Intelligent Vehicle Based on a Driving Brain

Deyi Li,Hongbo Gao

Journal Article

A double-layered nonlinear model predictive control based control algorithm for local trajectory planning for automated trucks under uncertain road adhesion coefficient conditions

Hong-chao Wang, Wei-wei Zhang, Xun-cheng Wu, Hao-tian Cao, Qiao-ming Gao, Su-yun Luo,17721336541@163.com,zwwsues@163.com,longxd2714@163.com,yjs_liqing@163.com,mosxsues@163.com,ly18362885604@163.com

Journal Article

A Co-Point Mapping-Based Approach to Drivable Area Detection for Self-Driving Cars

Ziyi Liu,Siyu Yu,Nanning Zheng

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 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

Fuzzy Control on Vehicle Motion Based on Subjective-objectiveJudgment of Driving Tenseness

Chen Xuemei and Gao Li

Journal Article

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

Zibo Jin, Daochun Li, Jinwu Xiang

Journal Article