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The impact of sales effort on a dual-channel dynamical system under a price-sensitive stochastic demand

Journal of Retailing and Consumer Services 2024年 76卷

作者： Datta, Alotosh Sarkar, Biswajit Dey, Bikash Koli Sangal, Isha Yang, Liu Fan, Shu-Kai S. Sardar, Suman Kalyan Thangavelu, Lakshmi Department of Mathematics & Statistics Banasthali University Banasthali Rajasthan Jaipur 304022 India Department of Industrial Engineering Yonsei University 50 Yonsei-ro Sinchon-dong Seoul Seodaemun-gu 03722 South Korea Center for Global Health Research Saveetha Medical College Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai 600077 India Department of Industrial & Data Engineering Hongik University Wausan-ro 94 Seoul Mapo-Gu 04066 South Korea Department of Management Marketing & Information Systems College of Business Administration Sam Houston State University 1905 University Ave Huntsville 77340 TX United States Department of Industrial Engineering and Management National Taipei University of Technology Taipei City 106344 Taiwan Engineering Research Institute Gyeongsang National University 501 Jinju-daero Jinju 52828 South Korea Department of Pharmacology Saveetha Dental College Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai 600077 India

Sales efforts play a crucial role in dynamic retail management and can have a significant impact on the overall success of a retail business. The primary goal of sales efforts is to generate revenue. By effectively promoting and selling products or services, sales teams can drive sales and increase the company's revenue. Demand of products play a critical role for retailing sector and demand cannot be predicted perfectly in reality. Owing to this situation, a dynamical system with dynamic sales effort and price-sensitive stochastic demand is proposed in this study. A dual-channel (online & offline) is considered in this study to provide the best consumer experience. Contradictory to the existing literature, geometric Brownian motion under a fuzzy environment is utilized to handle the dynamic price situations, which is solved through the Black-Scholes equations. The effect of preseason cost on the optimal results is analyzed. To validate the study, a numerical analysis of the dual-channel retailing system is presented with optimal selling price, sales effort, and profit under stochastic demand. Sales efforts have a profound impact on dynamic retail management. They drive revenue generation, customer acquisition and retention, inform inventory management decisions, provide market insights, contribute to competitive advantage, align with marketing strategies, and enable performance measurement and improvement. From the findings, it is clear that sales effort can provide 0.02% higher profit per cycle in a dual-channel retailing. © 2023 Elsevier Ltd

关键词： Dynamic system O2O retailing Retail management Sales effort Stochastic model

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Stock Price Prediction and Portfolio Optimization Using Hidden Markov Model and Quadratic Programming

Stock Price Prediction and Portfolio Optimization Using Hidd...

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Integrated Intelligence and Communication systems (ICIICS), International Conference on

作者： August C. Thio-Ac Daniel D. Cabrales Drexanne E. Calibara Maries V. Madrazo Agustine Christian C. Matawaran Genesis E. Micaller Edmon O. Fernandez Timothy M. Amado Romeo L. Jorda Lean Karlo S. Tolentino Lejan Alfred C. Enriquez Department of Electronics Engineering Technological University of the Philippines Manila Philippines Integrated Research and Training Center Technological University of the Philippines Manila Philippines Intelligent Systems Applications and Circuits Laboratory (ISAAC) Integrated Research and Training Center Technological University of the Philippines Manila Philippines Data Analytics Visualization Information Driven (DAVID) Research Lab Integrated Research and Training Center Technological University of the Philippines Manila Philippines Center for Artificial Intelligence and Nanoelectronics (CAIN) Integrated Research and Training Center Technological University of the Philippines Manila Philippines

This study introduces a decision support system for optimizing stock portfolios. It employs a Hidden Markov Model for 95% accurate stock price forecasts over a five-day period. The system utilizes Quadratic Programming to refine allocation, focusing on up to 15 blue-chip companies. Key indicators like MACD, RSI, and Bollinger Bands offer clear buy, sell, or hold recommendations. It actively monitors the Philippine Stock Exchange, notifying users of significant deviations. Developed in Python and C#, both models were trained on a consistent 5-year dataset for reliable performance. Results show high prediction accuracy, averaging 98.923% (high price) and 98.741% (low price), benefiting investment strategies. The study compared SPOT-allocated portfolios to random ones, revealing a notable difference in profit/loss allocation. This underscores its value for informed and profitable decision-making in stock trading.

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OLSR+: A new routing method based on fuzzy logic in flying ad-hoc networks (FANETs)

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Vehicular Communications 2022年 36卷

作者： Rahmani, Amir Masoud Ali, Saqib Yousefpoor, Efat Yousefpoor, Mohammad Sadegh Javaheri, Danial Lalbakhsh, Pooia Hassan Ahmed, Omed Hosseinzadeh, Mehdi Lee, Sang-Woong Future Technology Research Center National Yunlin University of Science and Technology Yunlin Taiwan Department of Information Systems College of Economics and Political Science Sultan Qaboos University Al Khoudh Muscat Oman Department of Computer Engineering Dezful Branch Islamic Azad University Dezful Iran Department of Computer Engineering Chosun University Gwangju 61452 South Korea Department of Data Science and Artificial Intelligence Faculty of Information Technology Monash University Clayton 3800 VIC Australia Department of Information Technology University of Human Development Sulaymaniyah Iraq Pattern Recognition and Machine Learning Lab Gachon University 1342 Seongnamdaero Sujeonggu Seongnam 13120 South Korea

Flying ad-hoc networks (FANETs) have many applications in military, industrial and agricultural areas. Due to specific features of FANETs, such as high-speed nodes, low density of nodes in the network, and rapid changes in the topology, most routing protocols designed for mobile ad hoc networks (MANETs) or vehicular ad hoc networks (VANETs) are not compatible with FANETs. In this paper, we propose a fuzzy logic-based routing approach called OLSR+ for FANETs. In this scheme, we seek to improve the optimized link state routing protocol (OLSR) so that it can efficiently be used in FANETs. OLSR+ includes four main phases: 1) Discovering neighboring nodes. In this phase, we propose a new and efficient technique for estimating the lifetime of the link between two unmanned ariel vehicles (UAVs) based on the link quality, distance, relative velocity, and movement direction. 2) Selecting multipoint relays (MPRs). In this phase, we present a fuzzy mechanism for selecting a set of MPR nodes. According to this mechanism, when a node has higher residual energy, higher link lifetime, and more neighborhood degree compared to others, it achieves more fitness to be selected as MPR. 3) Discovering the network topology. In this phase, we modify the format of the topology control (TC) message and add two fields, including route energy and route lifetime to this message. 4) Calculating the routing table. In OLSR+, we consider two parameters, including route energy and route lifetime, for establishing stable paths. Finally, we simulate OLSR+ using NS3 and compare its performance with two methods, namely greedy optimized link state routing (G-OLSR) and optimized link state routing (OLSR). The simulation results show that OLSR+ successfully reduces delay compared to G-OLSR and OLSR. In addition, it has higher packet delivery rate and throughput than others. Also, it improves energy consumption in the network. However, OLSR+ has more routing overhead than G-OLSR. © 2022 Elsevier Inc.

关键词： Artificial intelligence (AI) Flying ad hoc networks (FANETs) Fuzzy logic Routing Unmanned ariel vehicles (UAVs)

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Improving Automatic Speech Recognition Utilizing Audio-codecs for data Augmentation

Improving Automatic Speech Recognition Utilizing Audio-codec...

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IEEE Workshop on Multimedia Signal Processing

作者： Nirayo Hailu Ingo Siegert Andreas Nürnberger Faculty of Computer Science Data and Knowledge Engineering Group Otto-von-Guericke University Magdeburg Magdeburg Germany Faculty of Electrical Engineering and Information Technology Mobile Dialog Systems Otto-von-Guericke University Magdeburg Magdeburg Germany

ISBN: (数字)9781728193205

ISBN: (纸本)9781728193236

To train end-to-end automatic speech recognition models, it requires a large amount of labeled speech data. This goal is challenging for languages with fewer resources. In contrast to the commonly used feature level data augmentation, we propose to expand the training set by using different audio codecs at the data level. The augmentation method consists of using different audio codecs with changed bit rate, sampling rate, and bit depth. The change reassures variation in the input data without drastically affecting the audio quality. Besides, we can ensure that humans still perceive the audio, and any feature extraction is possible later. To demonstrate the general applicability of the proposed augmentation technique, we evaluated it in an end-to-end automatic speech recognition architecture in four languages. After applying the method, on the Amharic, Dutch, Slovenian, and Turkish datasets, we achieved a 1.57 average improvement in the character error rates (CER) without integrating language models. The result is comparable to the baseline result, showing CER improvement of 2.78, 1.25, 1.21, and 1.05 for each language. On the Amharic dataset, we reached a syllable error rate reduction of 6.12 compared to the baseline result.

关键词： Training Codecs Speech coding Error analysis Bit rate Feature extraction Automatic speech recognition

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Modeling and performances assessments of PV/T bifluid hybrid collector: Three cooling modes operation case 4

Modeling and performances assessments of PV/T bifluid hybrid...

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4th International Conference on Electrical and information Technologies, ICEIT 2020

作者： El Manssouri, Oussama El Fouas, Chaimae Hajji, Bekkay Rabhi, Abdelhamid Tina, Giuseppe Marco Gagliano, Antonio Mohamed First University Renewable Energy Embedded System and Data Processing Laboratory National School of Applied Sciences Oujda Morocco Picardie Jules Verne University Modelization Information and Systems Laboratory Amiens France University of Catania DIEEI: Department of Electric Electronic and Computer Engineering Catania Italy

ISBN: (数字)9781728143415

ISBN: (纸本)9781728143415

The objective of the present work is the performance assessments of a bi-fluid photovoltaic/thermal solar collector (PV/T). Based on energy-balance equations, a mathematical model of PV/T hybrid collector has been developed and simulated using Matlab Software. This model takes into account the effect of the three modes of fluid operation under the same PV/T system, namely: the air mode, the water mode, and the simultaneous mode of water and air. The results show that the lowest PV module temperature is observed in the simultaneous mode (air water) compared to others modes. It's maximum value achieved is around 62°C in the bi-fluid mode against 80°C and 73°C respectively in the air mode and the water mode. The maximum efficiencies of 66.97 % (thermal efficiency) and 10.1% (electrical efficiency) are also observed, when both fluids are operated simultaneously compared to others modes. Finally, the working in simultaneous mode is beneficial to transmit more heat, produce more thermal energy and consequently improve the electrical and thermal performances of the PV/T hybrid collector. Moreover, the theoretical model developed is validated, evidencing a good fit between simulation results and the experimental data available in literature. The PV/T collector designed in this study has a variety of applications, as solar drying in agriculture, air conditioning, space heating and hot water supply for domestic applications. © 2020 IEEE.

关键词： Air

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DieT Transformer model with PCA-ADE integration for advanced multi-class brain tumor classification

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Intelligence-Based Medicine 2025年 11卷 100192-100192页

作者： Amin, Mohammad Nahar, Khalid M.O. Gharaibeh, Hasan Nasayreh, Ahmad Alsalmanc, Neda'a Alomar, Alaa Malkawi, Majd Alqasem, Noor Smerat, Aseel Zitar, Raed Abu Nusier, Shawd Ezugwu, Absalom E. Abualigah, Laith Department of Information Technology and Computer Sciences Yarmouk University Irbid 211633 Jordan Faculty of Computer Studies Arab Open University P.O.Box 84901 Riyadh 11681 Saudi Arabia Department of Computer Science Faculty of Information Technology Jordan University of Science and Technology Irbid 22110 Jordan Department of Medicine Faculty of Medicine Jordan University of Science and Technology Irbid 22110 Jordan Department of Biomedical Systems and Informatics Engineering Hijjawi Faculty for Engineering Technology Yarmouk University Irbid 21163 Jordan Centre for Research Impact & Outcome Chitkara University Institute of Engineering and Technology Chitkara University Punjab Rajpura 140401 India Department of Biosciences Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai 602105 India Artificial Intelligence and Sensing Technologies (AIST) Research Center University of Tabuk Tabuk 71491 Saudi Arabia Faculty of Engineering and Computing Liwa College Abu Dhabi United Arab Emirates Department of Health and Clinical Outcomes Research Saint Louis University St. Louis MO United States Unit for Data Science and Computing North-West University 11 Hofman Street Potchefstroom 2520 South Africa Computer Science Department Al al-Bayt University Mafraq 25113 Jordan Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman 19328 Jordan

Early and accurate diagnosis of brain tumors is crucial to improving patient outcomes and optimizing treatment strategies. Long-term brain injury results from aberrant proliferation of either malignant or nonmalignant tissues in the brain. MRIs, or magnetic resonance imaging, are one of the most used approaches for detecting brain tumors. Professionals physically evaluate people after they have had MRI filtering, the process of enhancing MRI scans for radiologist interpretation, to establish if they have a brain tumor. Because different specialists use different frames to make judgments on the same MRI image, their analyses may yield contradictory results. Furthermore, simply detecting a tumor is insufficient. Inconsistent diagnoses can lead to delays in treatment, impacting survival rates and quality of care. It is also crucial to diagnose the patient's tumor so that treatment can begin as soon as possible. In this research, we investigate the multi-class classification of brain tumors utilizing a cutting-edge methodology that includes feature extraction from pictures using the DieT Transformer model, dimensionality reduction with PCA, and feature selection using the ADE algorithm. The proposed model, known in the publication as ADE_DieT, obtained an accuracy of 96.09 %. In addition, this article analyzes the performance of various pre-trained models, including MobileNetV3, NasNet, ResNet50, VGG16, VGG19, and DeiT. The proposed approach shortens the time required for manual diagnosis by clinicians by assisting in the rapid and accurate identification of brain tumors using MRI data. In oncology, this is important since it allows for early treatment. Integrating ADE_DieT into clinical workflows can support radiologists by reducing diagnosis time and enhancing diagnostic consistency. © 2024 The Authors

关键词： Brain tumor Medical image analysis MRI Vision Transformer

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Quantifying knowledge synchronisation in the 21st century

arXiv

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arXiv 2022年

作者： Yoon, Jisung Park, Jinseo Yun, Jinhyuk Jung, Woo-Sung Department Of Industrial And Management Engineering Pohang University Of Science And Technology Pohang37673 Korea Republic of Center For Complex Networks And Systems Research Luddy School Of Informatics Computing And Engineering Indiana University BloomingtonIN47408 United States Center For Global R&D Data Analysis Korea Institute Of Science And Technology Information Seoul02456 Korea Republic of School Of AI Convergence Soongsil University Seoul06978 Korea Republic of Department Of Physics Pohang University Of Science And Technology Pohang37673 Korea Republic of

Humans acquire and accumulate knowledge through language usage and eagerly exchange their knowledge for advancement. Although geographical barriers had previously limited communication, the emergence of information technology has opened new avenues for knowledge exchange. However, it is unclear which communication pathway is dominant in the 21st century. Here, we explore the dominant path of knowledge diffusion in the 21st century using Wikipedia, the largest communal dataset. We evaluate the similarity of shared knowledge between population groups, distinguished based on their language usage. When population groups are more engaged with each other, their knowledge structure is more similar, where engagement is indicated by socioeconomic connections, such as cultural, linguistic, and historical features. Moreover, geographical proximity is no longer a critical requirement for knowledge dissemination. Furthermore, we integrate our data into a mechanistic model to better understand the underlying mechanism and suggest that the knowledge "Silk Road" of the 21st century is based online. © 2022, CC BY.

关键词： knowledge management

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The bulk motion of gas in the core of the Centaurus galaxy cluster

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Nature 2025年第8050期638卷 365-369页

作者： Majumder, Anwesh Hosogi, Kokoro Sun, Ming Plšek, Tomáš Werner, Norbert Kondo, Marie Zhuravleva, Irina Yukita, Mihoko Yoshida, Tessei Yoneyama, Tomokage Yaqoob, Tahir Yamauchi, Shigeo Yamauchi, Makoto Yamasaki, Noriko Y. Yamaoka, Kazutaka Yamaguchi, Hiroya Yamada, Shinya Yamada, Satoshi Williams, Brian J. Watanabe, Shin Vink, Jacco Uno, Shinichiro Ueda, Yoshihiro Uchiyama, Hideki Uchida, Yuusuke Uchida, Nagomi Uchida, Hiroyuki Tsuru, Takeshi G. Tsunemi, Hiroshi Tsujimoto, Masahiro Tsuboi, Yohko Trigo, María Díaz Terashima, Yuichi Terada, Yukikatsu Tashiro, Makoto Tanimoto, Atsushi Tanaka, Takaaki Tamura, Keisuke Tamagawa, Toru Takeo, Mai Takahashi, Hiromitsu Szymkowiak, Andrew Suzuki, Hiromasa Smith, Randall Simionescu, Aurora Shidatsu, Megumi Seta, Hiromi Sawada, Makoto Sato, Toshiki Sato, Kosuke Pottschmidt, Katja Porter, Frederick Scott Plucinsky, Paul Petre, Robert Paltani, Stephane Ota, Naomi Okajima, Takashi Ogorzalek, Anna Ogawa, Shoji Odaka, Hirokazu Noda, Hirofumi Nobukawa, Masayoshi Nobukawa, Kumiko Ness, Jan-Uwe Nakazawa, Kazuhiro Nakajima, Hiroshi Mushotzky, Richard Murakami, Hiroshi Mukai, Koji Mori, Koji Mizuno, Tsunefumi Mizumoto, Misaki Mitsuishi, Ikuyuki Miller, Jon M. Miller, Eric D. Mernier, François McNamara, Brian McCammon, Dan Matsushita, Kyoko Matsumoto, Hironori Markevitch, Maxim Maeda, Yoshitomo Loewenstein, Michael Leutenegger, Maurice Kubota, Aya Kohmura, Takayoshi Kobayashi, Shogo Kitamoto, Shunji Kilbourne, Caroline Kelley, Richard Kanemaru, Yoshiaki Katsuda, Satoru Kara, Erin Kallman, Timothy Kaastra, Jelle Ishisaki, Yoshitaka Ishikawa, Kumi Ishida, Manabu Ichinohe, Yuto Hornschemeier, Ann Hodges-Kluck, Edmund Hell, Natalie Hayashi, Takayuki Hayashi, Katsuhiro Hatsukade, Isamu Hamaguchi, Kenji Hagino, Kouichi Guainazzi, Matteo Gu, Liyi García, Javier A. Gallo, Luigi Furuzawa, Akihiro Fukushima, Kotaro Fukazawa, Yasushi Fujita, Yutaka Fujimoto, Ryuichi Foster, Adam Ezoe, Yuichiro Eguchi, Satoshi Enoto, Teruaki Eckert, Dominique Eckart, Megan E. Ebisawa, Ken Dotani, Tadayasu Done, Chris Department of Physics Ehime University Matsuyama Japan Department of Astronomy University of Maryland College Park MD United States NASA/Goddard Space Flight Center Greenbelt MD United States Center for Research and Exploration in Space Science and Technology NASA/GSFC (CRESST II) Greenbelt MD United States Department of Physics University of Tokyo Tokyo Japan Department of Physics Technion Haifa Israel Center for Space Science and Technology University of Maryland Baltimore County Baltimore MD United States Center for Astrophysics | Harvard-Smithsonian Cambridge MA United States Lawrence Livermore National Laboratory Livermore CA United States Department of Astronomy University of Michigan Ann Arbor MI United States SRON Netherlands Institute for Space Research Leiden Netherlands Centre for Extragalactic Astronomy Department of Physics University of Durham Durham United Kingdom Institute of Space and Astronautical Science (ISAS) Japan Aerospace Exploration Agency (JAXA) Kanagawa Japan Department of Physics Kyoto University Kyoto Japan Department of Economics Kumamoto Gakuen University Kumamoto Japan Department of Physics Tokyo Metropolitan University Tokyo Japan Department of Physics Hiroshima University Hiroshima Japan Department of Physics Fujita Health University Toyoake Japan Department of Astronomy and Physics Saint Mary’s University Halifax Nova Scotia Canada Cahill Center for Astronomy and Astrophysics California Institute of Technology Pasadena CA United States European Space Agency (ESA) European Space Research and Technology Centre (ESTEC) Noordwijk Netherlands Faculty of Engineering University of Miyazaki Miyazaki Japan RIKEN Nishina Center Saitama Japan Leiden Observatory University of Leiden Leiden Netherlands Kavli Institute for Astrophysics and Space Research Massachusetts Institute of Technology Cambridge MA United States Department of Physics Saitama University Saitama Japan Department of Physics Rikkyo Universi

Galaxy clusters contain vast amounts of hot ionized gas known as the intracluster medium (ICM). In relaxed cluster cores, the radiative cooling time of the ICM is shorter than the age of the cluster. However, the absence of line emission associated with cooling suggests heating mechanisms that offset the cooling, with feedback from active galactic nuclei (AGNs) being the most likely source^1,2. Turbulence and bulk motions, such as the oscillating (‘sloshing’) motion of the core gas in the cluster potential well, have also been proposed as mechanisms for heat distribution from the outside of the core^3,4. Here we present X-ray spectroscopic observations of the Centaurus galaxy cluster with the X-Ray Imaging and Spectroscopy Mission satellite. We find that the hot gas flows along the line of sight relative to the central galaxy, with velocities from 130 km s⁻¹ to 310 km s⁻¹ within about 30 kpc of the centre. This indicates bulk flow consistent with core gas sloshing. Although the bulk flow may prevent excessive accumulation of cooled gas at the centre, it could distribute the heat injected by the AGN and bring in thermal energy from the surrounding ICM. The velocity dispersion of the gas is found to be only ≲120 km s⁻¹ in the core, even within about 10 kpc of the AGN. This suggests that the influence of the AGN on the surrounding ICM motion is limited in the cluster. © The Author(s), under exclusive licence to Springer Nature Limited 2025.

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Kernel-Based Fuzzy Intuitionistic Possibilistic Clustering: Analyzing High-Dimensional Gene Expression Cancer database

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data-Enabled Discovery and Applications 2020年第1期4卷 1-18页

作者： R, Kannan S Kashyap, Esha Last, Mark Department of Mathematics Pondicherry University Puducherry India Head of Ben-Gurion University Data Science Research Center Department of Software and Information Systems Engineering Ben-Gurion University of the Negev Beer-Sheva Israel

Identifying cohesion of genes for subtypes of diseases in a high-dimensional gene expression database is a highly challenging problem, since the subtypes are based on slight intensity differences between gene expressions. The existed clustering methods are biased with training dataset in identifying the subtypes, and the methods have received irrelevant subtypes. Therefore, this paper introduces unsupervised way of fuzzy clustering to identify the subtypes of genes in a breast cancer database. Here, we have used the dataset which contains 12,634 genes and 288 for finding three available subclasses. In order to cluster the similar intensity genes in the breast cancer dataset, this paper incorporates possibilistic approach, intuitionistic fuzzy sets, and kernel functions with proposed fuzzy clustering techniques. The experimental part of this paper shows that the proposed clustering method how notably identifies the similar gene patterns for common subtypes of breast cancer.

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Parallel Planning:A New Motion Planning Framework for Autonomous Driving

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IEEE/CAA Journal of Automatica Sinica 2019年第1期6卷 236-246页

作者： Long Chen Xuemin Hu Wei Tian Hong Wang Dongpu Cao Fei-Yue Wang IEEE School of Data and Computer Science Sun Yat-sen University the School of Computer Science and Information Engineering Hubei University Institute of Measurement and Control Systems Karlsruhe Institute of Technology Department of Mechanical and Mechatronics Engineering University of Waterloo the State Key Laboratory of Management and Control for Complex Systems.Institute of Automation Chinese Academy of Sciences the Research Center for Military Computational Experiments and Parallel Systems Technology National University of Defense Technology

Motion planning is one of the most significant technologies for autonomous driving. To make motion planning models able to learn from the environment and to deal with emergency situations, a new motion planning framework called as"parallel planning" is proposed in this paper. In order to generate sufficient and various training samples, artificial traffic scenes are firstly constructed based on the knowledge from the reality.A deep planning model which combines a convolutional neural network(CNN) with the Long Short-Term Memory module(LSTM) is developed to make planning decisions in an end-toend mode. This model can learn from both real and artificial traffic scenes and imitate the driving style of human ***, a parallel deep reinforcement learning approach is also presented to improve the robustness of planning model and reduce the error rate. To handle emergency situations, a hybrid generative model including a variational auto-encoder(VAE) and a generative adversarial network(GAN) is utilized to learn from virtual emergencies generated in artificial traffic scenes. While an autonomous vehicle is moving, the hybrid generative model generates multiple video clips in parallel, which correspond to different potential emergency scenarios. Simultaneously, the deep planning model makes planning decisions for both virtual and current real scenes. The final planning decision is determined by analysis of real observations. Leveraging the parallel planning approach, the planner is able to make rational decisions without heavy calculation burden when an emergency occurs.

关键词： Autonomous driving artificial traffic scene deep learning emergencies motion planning parallel planning

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