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Your search keyword '"Wei, Junqing"' showing total 152 results
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152 results on '"Wei, Junqing"'

1. VistaScenario: Interaction Scenario Engineering for Vehicles with Intelligent Systems for Transport Automation

Author

Chang, Cheng, Zhang, Jiawei, Ge, Jingwei, Zhang, Zuo, Wei, Junqing, Li, Li, and Wang, Fei-Yue

Subjects
Computer Science - Software Engineering
Abstract

Intelligent vehicles and autonomous driving systems rely on scenario engineering for intelligence and index (I&I), calibration and certification (C&C), and verification and validation (V&V). To extract and index scenarios, various vehicle interactions are worthy of much attention, and deserve refined descriptions and labels. However, existing methods cannot cope well with the problem of scenario classification and labeling with vehicle interactions as the core. In this paper, we propose VistaScenario framework to conduct interaction scenario engineering for vehicles with intelligent systems for transport automation. Based on the summarized basic types of vehicle interactions, we slice scenario data stream into a series of segments via spatiotemporal scenario evolution tree. We also propose the scenario metric Graph-DTW based on Graph Computation Tree and Dynamic Time Warping to conduct refined scenario comparison and labeling. The extreme interaction scenarios and corner cases can be efficiently filtered and extracted. Moreover, with naturalistic scenario datasets, testing examples on trajectory prediction model demonstrate the effectiveness and advantages of our framework. VistaScenario can provide solid support for the usage and indexing of scenario data, further promote the development of intelligent vehicles and transport automation., Comment: Accepted by IEEE Transactions on Intelligent Vehicles

Published
2024
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2. Integrated Decision and Control for High-Level Automated Vehicles by Mixed Policy Gradient and Its Experiment Verification

Author

Guan, Yang, Tang, Liye, Li, Chuanxiao, Li, Shengbo Eben, Ren, Yangang, Wei, Junqing, Zhang, Bo, and Li, Keqiang

Subjects
Computer Science - Robotics, Computer Science - Artificial Intelligence
Abstract

Self-evolution is indispensable to realize full autonomous driving. This paper presents a self-evolving decision-making system based on the Integrated Decision and Control (IDC), an advanced framework built on reinforcement learning (RL). First, an RL algorithm called constrained mixed policy gradient (CMPG) is proposed to consistently upgrade the driving policy of the IDC. It adapts the MPG under the penalty method so that it can solve constrained optimization problems using both the data and model. Second, an attention-based encoding (ABE) method is designed to tackle the state representation issue. It introduces an embedding network for feature extraction and a weighting network for feature fusion, fulfilling order-insensitive encoding and importance distinguishing of road users. Finally, by fusing CMPG and ABE, we develop the first data-driven decision and control system under the IDC architecture, and deploy the system on a fully-functional self-driving vehicle running in daily operation. Experiment results show that boosting by data, the system can achieve better driving ability over model-based methods. It also demonstrates safe, efficient and smart driving behavior in various complex scenes at a signalized intersection with real mixed traffic flow.

Published
2022

34. Measurement of Liquid Film Coverage on Vertical Plates with Hydrophilic and Structured Surface Treatments

Author

Jinping Liu, Wei Junqing, Jia Zhang, and Xiongwen Xu

Subjects
Surface (mathematics), Materials science, General Chemical Engineering, 02 engineering and technology, General Chemistry, 021001 nanoscience & nanotechnology, Industrial and Manufacturing Engineering, Liquid film, 020401 chemical engineering, Mass transfer, 0204 chemical engineering, Composite material, 0210 nano-technology, Falling (sensation), High heat
Abstract

Falling film techniques are widely used in various chemical industrial devices because of their high heat and mass transfer coefficients. Since the coverage of a liquid film plays an important role...

Published
2021

43. MetaScenario: A Framework for Driving Scenario Data Description, Storage and Indexing

Author

Chang, Cheng, primary, Cao, Dongpu, additional, Chen, Long, additional, Su, Kui, additional, Su, Kuifeng, additional, Su, Yuelong, additional, Wang, Fei-Yue, additional, Wang, Jue, additional, Wang, Ping, additional, Wei, Junqing, additional, Wu, Gansha, additional, Wu, Xiangbin, additional, Xu, Huile, additional, Zheng, Nanning, additional, and Li, Li, additional

Published
2022
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50. MetaScenario: A Framework for Driving Scenario Data Description, Storage and Indexing

Author

Chang, Cheng, Cao, Dongpu, Chen, Long, Su, Kui, Su, Kuifeng, Su, Yuelong, Wang, Fei-Yue, Wang, Jue, Wang, Ping, Wei, Junqing, Wu, Gansha, Wu, Xiangbin, Xu, Huile, Zheng, Nanning, and Li, Li

Abstract

Autonomous driving related researches require the analysis and usage of massive amounts of driving scenario data. Compared to raw data collected by sensors, scenario data provide a preliminary abstraction of driving tasks and processes, explicitly integrate information about the road environment and the dynamic and static attributes of traffic participants, making it easier to conduct task understanding and decision making. However, many existing driving scenario datasets have the following two problems. First, it is not clear which data fields need to be recorded for driving scenarios. The data storage formats and organization standards are inconsistent. Second, the datasets cannot establish driving scenario indexing effectively. Existing datasets are sparsely annotated and difficult to index, which is detrimental to data sampling and extraction for machine learning process, thus hindering efficient fusion and reuse. In this paper, we propose MetaScenario, a framework for driving scenario data. We describe driving scenarios and design the centralized and unified data framework for the storage, processing, and indexing of scenario data based on relational database. The concept of atom scenario is proposed and characterized using semantic graphs. We also annotate and classify behaviors and interactions of traffic participants in atom scenarios by extracting the spatiotemporal evolution of semantic information. The annotation facilitates the indexing and extraction of data. The scenario datasets are further evaluated via the data distribution and annotation statistics. MetaScenario can provide researchers with convenient tools for scenario data extraction and important analytical references.

Published
2023
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