Countries have invested considerable sums of human capital and material resources in the practical application of self-driving cars demonstrating the impressive market opportunity.In light of this trend,Taiwan does no...Countries have invested considerable sums of human capital and material resources in the practical application of self-driving cars demonstrating the impressive market opportunity.In light of this trend,Taiwan does not want to fall behind either.As on-road testing and technological development for self-driving cars continue to develop in different countries,the controversial issues of safety,ethics,liability,and the invasion of privacy continue to emerge.In order to resolve these issues,the government of Taiwan seeks to provide a good environment for AI(artificial intelligence)innovation and applications.This article summarizes and highlights relevant content and key points of Unmanned Vehicles Technology Innovative Experimentation Act,which was legislated in Taiwan in 2018.In addition,it points out the fundamental ethics regulation of AI,which has influenced Taiwan legal policy.展开更多
With the advent of deep learning,self-driving schemes based on deep learning are becoming more and more popular.Robust perception-action models should learn from data with different scenarios and real behaviors,while ...With the advent of deep learning,self-driving schemes based on deep learning are becoming more and more popular.Robust perception-action models should learn from data with different scenarios and real behaviors,while current end-to-end model learning is generally limited to training of massive data,innovation of deep network architecture,and learning in-situ model in a simulation environment.Therefore,we introduce a new image style transfer method into data augmentation,and improve the diversity of limited data by changing the texture,contrast ratio and color of the image,and then it is extended to the scenarios that the model has been unobserved before.Inspired by rapid style transfer and artistic style neural algorithms,we propose an arbitrary style generation network architecture,including style transfer network,style learning network,style loss network and multivariate Gaussian distribution function.The style embedding vector is randomly sampled from the multivariate Gaussian distribution and linearly interpolated with the embedded vector predicted by the input image on the style learning network,which provides a set of normalization constants for the style transfer network,and finally realizes the diversity of the image style.In order to verify the effectiveness of the method,image classification and simulation experiments were performed separately.Finally,we built a small-sized smart car experiment platform,and apply the data augmentation technology based on image style transfer drive to the experiment of automatic driving for the first time.The experimental results show that:(1)The proposed scheme can improve the prediction accuracy of the end-to-end model and reduce the model’s error accumulation;(2)the method based on image style transfer provides a new scheme for data augmentation technology,and also provides a solution for the high cost that many deep models rely heavily on a large number of label data.展开更多
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 ...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 posi- tions 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 pro- posed 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.展开更多
Based on the idea of infinitesimal analysis, we establish the basic model of relation between speed and flow. Since putting a certain amount of self-driving car will affect the average speed of mixed traffic flow, we ...Based on the idea of infinitesimal analysis, we establish the basic model of relation between speed and flow. Since putting a certain amount of self-driving car will affect the average speed of mixed traffic flow, we choose the proportion of self-driving car to be a variable, denoted by k. Based on the least square method, we find two critical values of k that are 38.63% and 68.26%. When k 38.63%, the self-driving cars have a negative influence to the traffic. When 38.63% < k < 68.26%, they have a positive influence to the traffic. When k > 68.26%, they have significant improvement to the traffic capacity of the road.展开更多
The self-driving cars are highly developed and about to meet the market, but the driving strategies and corresponding behaviors with others still need to be tested. In this paper, based on its characteristics and beha...The self-driving cars are highly developed and about to meet the market, but the driving strategies and corresponding behaviors with others still need to be tested. In this paper, based on its characteristics and behaviors of manual-driving vehicles, we propose the driving strategies of manual-driving cars as well as self-driving cars. And we use the cellular automaton to simulate the traffic reality under different conditions, and to evaluate the efficiency of a road when self-driving cars are put into use. This research can be a reference by traffic planning and vehicles performance test, and further research can be designed in a model which can calculate the efficiency of a road when the percentage of self-driving cars are different.展开更多
The autonomous vehicle(AV)technology has the potential to significantly improve safety and efficiency of the transportation and logistics industry.Full-scale AV testing is limited by time,space,and cost,while simulati...The autonomous vehicle(AV)technology has the potential to significantly improve safety and efficiency of the transportation and logistics industry.Full-scale AV testing is limited by time,space,and cost,while simulation-based testing often lacks the necessary accuracy of AV and environmental modeling.In recent years,several initiatives have emerged to test autonomous software and hardware on scaled vehicles.This systematic literature review provides an overview of the literature surrounding small-scale self-driving cars,summarizing the current autonomous platforms deployed and focusing on the software and hardware developments in this field.The studies published in English-language journals or conference papers that present small-scale testing of self-driving cars were included.Web of Science,Scopus,Springer Link,Wiley,ACM Digital Library,and TRID databases were used for the literature search.The systematic literature search found 38 eligible studies.Research gaps in the reviewed papers were identified to provide guidance for future research.Some key takeaway emerging from this manuscript are:(i)there is a need to improve the models and neural network architectures used in autonomous driving systems,as most papers present only preliminary results;(ii)increasing datasets and sharing databases can help in developing more reliable control policies and reducing bias and variance in the training process;(iii)small-scaled vehicles to ensure safety is a major benefit,and incorporating data about unsafe driving behaviors and infrastructure problems can improve the accuracy of predictive models.展开更多
Purpose–Decision-making is one of the key technologies for self-driving cars.The high dependency of previously existing methods on human driving data or rules makes it difficult to model policies for different driving...Purpose–Decision-making is one of the key technologies for self-driving cars.The high dependency of previously existing methods on human driving data or rules makes it difficult to model policies for different driving situations.Design/methodology/approach–In this research,a probabilistic decision-making method based on the Markov decision process(MDP)is proposed to deduce the optimal maneuver automatically in a two-lane highway scenario without using any human data.The decision-making issues in a traffic environment are formulated as the MDP by defining basic elements including states,actions and basic models.Transition and reward models are defined by using a complete prediction model of the surrounding cars.An optimal policy was deduced using a dynamic programing method and evaluated under a two-dimensional simulation environment.Findings–Results show that,at the given scenario,the self-driving car maintained safety and efficiency with the proposed policy.Originality/value–This paper presents a framework used to derive a driving policy for self-driving cars without relying on any human driving data or rules modeled by hand.展开更多
Aerodynamic research on road cars was reviewed in this work under the thread of reducing drag,with the awareness that this may succeed in effectively decreasing the carbon footprint of transportation.First,a selection...Aerodynamic research on road cars was reviewed in this work under the thread of reducing drag,with the awareness that this may succeed in effectively decreasing the carbon footprint of transportation.First,a selection of studies was presented to focus on the most important aerodynamic features of the flow around realistic car body shapes.Then,the discussion was organized around three pillars related to passive flow control,active flow control and active aerodynamics.Both experimental and numerical investigations were included to provide a comprehensive overview.A clear distinction was made between simplified and realistic car models,as well as production vehicles(within the limits of restricted access information).Moreover,a short essay was dedicated to electric vehicles,for which aerodynamics matters,especially at highway speeds.Last,the impact of aerodynamic principles on the design of current and future vehicle fleet was assessed,honestly admitting that recent market trends must be reversed to turn decarbonization goals into reality and damp the effects of global warming.展开更多
A recently published study(Xin et al.,Prog Biochem Biophys,2026,53(2):431-441.DOI:10.3724/j.pibb.2025.0508)addresses the therapeutic challenges of pancreatic ductal adenocarcinoma(PDAC)by innovatively developing an or...A recently published study(Xin et al.,Prog Biochem Biophys,2026,53(2):431-441.DOI:10.3724/j.pibb.2025.0508)addresses the therapeutic challenges of pancreatic ductal adenocarcinoma(PDAC)by innovatively developing an orally administered nanogene delivery system.Designed to achieve in situ,efficient delivery of chimeric antigen receptor(CAR)genes to tumor sites,this approach offers a novel strategy for CAR-macrophage(CAR-M)based immunotherapy.Its key highlights are as follows.展开更多
The paradigm of cancer treatment has been reshaped by chimeric antigen receptor(CAR)αβT cell therapy,yet its full potential remains constrained by fundamental limitations.While conventional CARαβT cells have achie...The paradigm of cancer treatment has been reshaped by chimeric antigen receptor(CAR)αβT cell therapy,yet its full potential remains constrained by fundamental limitations.While conventional CARαβT cells have achieved notable success in hematological malignancies,their broader application is hindered by the high cost and delays of autologous manufacturing,as well as the critical risk of graft-vs-host disease(GvHD).In addition,their efficacy against solid tumors is often compromised by the immunosuppressive tumor microenvironment(TME).As a promising solution,γδT cells are being developed as an alternative CAR platform.Their intrinsic ability to recognize transformed cells in a major histocompatibility complex(MHC)-independent manner minimizes the risk of GvHD and supports the creation of safe,effective allogeneic therapies.Building on this unique biology,the therapeutic efficacy of CARγδT cells is being enhanced through advanced engineering strategies.Key innovations include“armoring”technologies,such as cytokine secretion,checkpoint blockade,and metabolic rewiring,to overcome local immunosuppression and improve persistence,as well as the use of induced pluripotent stem cells(iPSCs)to generate standardized products from a renewable and consistent source.This expanding technological toolbox is also enabling novel applications beyond oncology.For example,chimeric autoantibody receptor(CAAR)constructs built onγδT cells integrate both classical and emerging insights into CARγδT cell therapy,highlighting innovations that are driving the field toward safer,more versatile,and longer-lasting treatments for cancer and autoimmunity.In light of these advancements,this review provides an overview of the current understanding ofγδT cell biology and highlights emerging engineering strategies that enhance the efficacy and durability of CARγδT cells across oncologic and autoimmune contexts.展开更多
Self-driving and semi-self-driving cars play an important role in our daily lives.The effectiveness of these cars is based heavily on the use of their surrounding areas to collect sensitive and vital information.Howev...Self-driving and semi-self-driving cars play an important role in our daily lives.The effectiveness of these cars is based heavily on the use of their surrounding areas to collect sensitive and vital information.However,external infrastructures also play significant roles in the transmission and reception of control data,cooperative awareness messages,and caution notifications.In this case,roadside units are considered one of themost important communication peripherals.Random distribution of these infrastructures will overburden the spread of self-driving vehicles in terms of cost,bandwidth,connectivity,and radio coverage area.In this paper,a new distributed roadside unit is proposed to enhance the performance and connectivity of these cars.Therefore,this approach is based primarily on k-means to find the optimal location of each roadside unit.In addition,this approach supports dynamicmobility with a long period of connectivity for each car.Further,this system can adapt to various locations(e.g.,highways,rural areas,urban environments).The simulation results of the proposed system are reflected in its efficiency and effectively.Thus,the system can achieve a high connectivity rate with a low error rate while reducing costs.展开更多
多发性骨髓瘤(multiple myeloma,MM)是一种不可治愈的血液系统恶性肿瘤,尽管新型蛋白酶体抑制剂、免疫调节剂及CD38单抗等药物的应用显著延长了患者的生存时间,但复发耐药仍难以避免。细胞免疫治疗,特别是嵌合抗原受体(chimeric antigen...多发性骨髓瘤(multiple myeloma,MM)是一种不可治愈的血液系统恶性肿瘤,尽管新型蛋白酶体抑制剂、免疫调节剂及CD38单抗等药物的应用显著延长了患者的生存时间,但复发耐药仍难以避免。细胞免疫治疗,特别是嵌合抗原受体(chimeric antigen receptor,CAR)T细胞疗法的快速发展,极大程度的改变了复发/难治性(relapsed/refractory,R/R)MM患者的治疗现状。FDA目前已批准了2款靶向B细胞成熟抗原(B cell maturation antigen,BCMA)的CAR-T细胞产品,使其用于既往接受过4线及以上治疗的R/R MM患者。随着临床研究的不断深入,靶向GPRC5D(G protein-coupled receptor C class Group 5 member D,G蛋白偶联受体C类第5组成员D)的CAR-T细胞治疗也显示出其独特的优势。除了应用于难治复发的患者,多项临床试验支持CAR-T在MM中治疗线数的前移。本文就CAR-T细胞治疗在MM中开展的关键性临床研究展开综述,旨在为临床应用提供参考。展开更多
文摘Countries have invested considerable sums of human capital and material resources in the practical application of self-driving cars demonstrating the impressive market opportunity.In light of this trend,Taiwan does not want to fall behind either.As on-road testing and technological development for self-driving cars continue to develop in different countries,the controversial issues of safety,ethics,liability,and the invasion of privacy continue to emerge.In order to resolve these issues,the government of Taiwan seeks to provide a good environment for AI(artificial intelligence)innovation and applications.This article summarizes and highlights relevant content and key points of Unmanned Vehicles Technology Innovative Experimentation Act,which was legislated in Taiwan in 2018.In addition,it points out the fundamental ethics regulation of AI,which has influenced Taiwan legal policy.
基金the National Natural Science Foundation of China(51965008)Science and Technology projects of Guizhou[2018]2168Excellent Young Researcher Project of Guizhou[2017]5630.
文摘With the advent of deep learning,self-driving schemes based on deep learning are becoming more and more popular.Robust perception-action models should learn from data with different scenarios and real behaviors,while current end-to-end model learning is generally limited to training of massive data,innovation of deep network architecture,and learning in-situ model in a simulation environment.Therefore,we introduce a new image style transfer method into data augmentation,and improve the diversity of limited data by changing the texture,contrast ratio and color of the image,and then it is extended to the scenarios that the model has been unobserved before.Inspired by rapid style transfer and artistic style neural algorithms,we propose an arbitrary style generation network architecture,including style transfer network,style learning network,style loss network and multivariate Gaussian distribution function.The style embedding vector is randomly sampled from the multivariate Gaussian distribution and linearly interpolated with the embedded vector predicted by the input image on the style learning network,which provides a set of normalization constants for the style transfer network,and finally realizes the diversity of the image style.In order to verify the effectiveness of the method,image classification and simulation experiments were performed separately.Finally,we built a small-sized smart car experiment platform,and apply the data augmentation technology based on image style transfer drive to the experiment of automatic driving for the first time.The experimental results show that:(1)The proposed scheme can improve the prediction accuracy of the end-to-end model and reduce the model’s error accumulation;(2)the method based on image style transfer provides a new scheme for data augmentation technology,and also provides a solution for the high cost that many deep models rely heavily on a large number of label data.
基金This research was partially supported by the National Natural Science Foundation of China (61773312), the National Key Research and Development Plan (2017YFC0803905), and the Program of Introducing Talents of Discipline to University (B13043).
文摘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 posi- tions 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 pro- posed 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.
文摘Based on the idea of infinitesimal analysis, we establish the basic model of relation between speed and flow. Since putting a certain amount of self-driving car will affect the average speed of mixed traffic flow, we choose the proportion of self-driving car to be a variable, denoted by k. Based on the least square method, we find two critical values of k that are 38.63% and 68.26%. When k 38.63%, the self-driving cars have a negative influence to the traffic. When 38.63% < k < 68.26%, they have a positive influence to the traffic. When k > 68.26%, they have significant improvement to the traffic capacity of the road.
文摘The self-driving cars are highly developed and about to meet the market, but the driving strategies and corresponding behaviors with others still need to be tested. In this paper, based on its characteristics and behaviors of manual-driving vehicles, we propose the driving strategies of manual-driving cars as well as self-driving cars. And we use the cellular automaton to simulate the traffic reality under different conditions, and to evaluate the efficiency of a road when self-driving cars are put into use. This research can be a reference by traffic planning and vehicles performance test, and further research can be designed in a model which can calculate the efficiency of a road when the percentage of self-driving cars are different.
基金funded by the Brazilian National Council for Scientific and Technological Development(CNPq),under research grant number 408186/2021-6.
文摘The autonomous vehicle(AV)technology has the potential to significantly improve safety and efficiency of the transportation and logistics industry.Full-scale AV testing is limited by time,space,and cost,while simulation-based testing often lacks the necessary accuracy of AV and environmental modeling.In recent years,several initiatives have emerged to test autonomous software and hardware on scaled vehicles.This systematic literature review provides an overview of the literature surrounding small-scale self-driving cars,summarizing the current autonomous platforms deployed and focusing on the software and hardware developments in this field.The studies published in English-language journals or conference papers that present small-scale testing of self-driving cars were included.Web of Science,Scopus,Springer Link,Wiley,ACM Digital Library,and TRID databases were used for the literature search.The systematic literature search found 38 eligible studies.Research gaps in the reviewed papers were identified to provide guidance for future research.Some key takeaway emerging from this manuscript are:(i)there is a need to improve the models and neural network architectures used in autonomous driving systems,as most papers present only preliminary results;(ii)increasing datasets and sharing databases can help in developing more reliable control policies and reducing bias and variance in the training process;(iii)small-scaled vehicles to ensure safety is a major benefit,and incorporating data about unsafe driving behaviors and infrastructure problems can improve the accuracy of predictive models.
文摘Purpose–Decision-making is one of the key technologies for self-driving cars.The high dependency of previously existing methods on human driving data or rules makes it difficult to model policies for different driving situations.Design/methodology/approach–In this research,a probabilistic decision-making method based on the Markov decision process(MDP)is proposed to deduce the optimal maneuver automatically in a two-lane highway scenario without using any human data.The decision-making issues in a traffic environment are formulated as the MDP by defining basic elements including states,actions and basic models.Transition and reward models are defined by using a complete prediction model of the surrounding cars.An optimal policy was deduced using a dynamic programing method and evaluated under a two-dimensional simulation environment.Findings–Results show that,at the given scenario,the self-driving car maintained safety and efficiency with the proposed policy.Originality/value–This paper presents a framework used to derive a driving policy for self-driving cars without relying on any human driving data or rules modeled by hand.
文摘Aerodynamic research on road cars was reviewed in this work under the thread of reducing drag,with the awareness that this may succeed in effectively decreasing the carbon footprint of transportation.First,a selection of studies was presented to focus on the most important aerodynamic features of the flow around realistic car body shapes.Then,the discussion was organized around three pillars related to passive flow control,active flow control and active aerodynamics.Both experimental and numerical investigations were included to provide a comprehensive overview.A clear distinction was made between simplified and realistic car models,as well as production vehicles(within the limits of restricted access information).Moreover,a short essay was dedicated to electric vehicles,for which aerodynamics matters,especially at highway speeds.Last,the impact of aerodynamic principles on the design of current and future vehicle fleet was assessed,honestly admitting that recent market trends must be reversed to turn decarbonization goals into reality and damp the effects of global warming.
文摘A recently published study(Xin et al.,Prog Biochem Biophys,2026,53(2):431-441.DOI:10.3724/j.pibb.2025.0508)addresses the therapeutic challenges of pancreatic ductal adenocarcinoma(PDAC)by innovatively developing an orally administered nanogene delivery system.Designed to achieve in situ,efficient delivery of chimeric antigen receptor(CAR)genes to tumor sites,this approach offers a novel strategy for CAR-macrophage(CAR-M)based immunotherapy.Its key highlights are as follows.
基金supported by the National Research Foundation of Korea(NRF)through the Ministry of Education(2021R1I1A3059820)(to Jea-Hyun Baek).
文摘The paradigm of cancer treatment has been reshaped by chimeric antigen receptor(CAR)αβT cell therapy,yet its full potential remains constrained by fundamental limitations.While conventional CARαβT cells have achieved notable success in hematological malignancies,their broader application is hindered by the high cost and delays of autologous manufacturing,as well as the critical risk of graft-vs-host disease(GvHD).In addition,their efficacy against solid tumors is often compromised by the immunosuppressive tumor microenvironment(TME).As a promising solution,γδT cells are being developed as an alternative CAR platform.Their intrinsic ability to recognize transformed cells in a major histocompatibility complex(MHC)-independent manner minimizes the risk of GvHD and supports the creation of safe,effective allogeneic therapies.Building on this unique biology,the therapeutic efficacy of CARγδT cells is being enhanced through advanced engineering strategies.Key innovations include“armoring”technologies,such as cytokine secretion,checkpoint blockade,and metabolic rewiring,to overcome local immunosuppression and improve persistence,as well as the use of induced pluripotent stem cells(iPSCs)to generate standardized products from a renewable and consistent source.This expanding technological toolbox is also enabling novel applications beyond oncology.For example,chimeric autoantibody receptor(CAAR)constructs built onγδT cells integrate both classical and emerging insights into CARγδT cell therapy,highlighting innovations that are driving the field toward safer,more versatile,and longer-lasting treatments for cancer and autoimmunity.In light of these advancements,this review provides an overview of the current understanding ofγδT cell biology and highlights emerging engineering strategies that enhance the efficacy and durability of CARγδT cells across oncologic and autoimmune contexts.
文摘Self-driving and semi-self-driving cars play an important role in our daily lives.The effectiveness of these cars is based heavily on the use of their surrounding areas to collect sensitive and vital information.However,external infrastructures also play significant roles in the transmission and reception of control data,cooperative awareness messages,and caution notifications.In this case,roadside units are considered one of themost important communication peripherals.Random distribution of these infrastructures will overburden the spread of self-driving vehicles in terms of cost,bandwidth,connectivity,and radio coverage area.In this paper,a new distributed roadside unit is proposed to enhance the performance and connectivity of these cars.Therefore,this approach is based primarily on k-means to find the optimal location of each roadside unit.In addition,this approach supports dynamicmobility with a long period of connectivity for each car.Further,this system can adapt to various locations(e.g.,highways,rural areas,urban environments).The simulation results of the proposed system are reflected in its efficiency and effectively.Thus,the system can achieve a high connectivity rate with a low error rate while reducing costs.
文摘多发性骨髓瘤(multiple myeloma,MM)是一种不可治愈的血液系统恶性肿瘤,尽管新型蛋白酶体抑制剂、免疫调节剂及CD38单抗等药物的应用显著延长了患者的生存时间,但复发耐药仍难以避免。细胞免疫治疗,特别是嵌合抗原受体(chimeric antigen receptor,CAR)T细胞疗法的快速发展,极大程度的改变了复发/难治性(relapsed/refractory,R/R)MM患者的治疗现状。FDA目前已批准了2款靶向B细胞成熟抗原(B cell maturation antigen,BCMA)的CAR-T细胞产品,使其用于既往接受过4线及以上治疗的R/R MM患者。随着临床研究的不断深入,靶向GPRC5D(G protein-coupled receptor C class Group 5 member D,G蛋白偶联受体C类第5组成员D)的CAR-T细胞治疗也显示出其独特的优势。除了应用于难治复发的患者,多项临床试验支持CAR-T在MM中治疗线数的前移。本文就CAR-T细胞治疗在MM中开展的关键性临床研究展开综述,旨在为临床应用提供参考。
