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
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
目的分离纯化碧根果致敏原Car i 1,并对其结构进行表征鉴定。方法以新鲜碧根果果仁为原料,通过粉碎、脱脂、浸提、粗分级、凝胶过滤层析,对碧根果致敏原蛋白Car i 1进行分离纯化。结合十二烷基硫酸钠-聚丙烯酰胺凝胶电泳、液相色谱-串...目的分离纯化碧根果致敏原Car i 1,并对其结构进行表征鉴定。方法以新鲜碧根果果仁为原料,通过粉碎、脱脂、浸提、粗分级、凝胶过滤层析,对碧根果致敏原蛋白Car i 1进行分离纯化。结合十二烷基硫酸钠-聚丙烯酰胺凝胶电泳、液相色谱-串联质谱法和免疫印迹法3种方法对Cari1进行鉴定,并通过圆二色谱仪与紫外分光光度计表征其二、三级结构。结果本方法纯化获得碧根果致敏原Cari1,单轮制备量可达5 mg以上,且纯度大于95%,蛋白质高级结构未被破坏,能够被全部3名碧根果过敏患者的血清准确识别。结论该纯化方法技术路线简单、设备要求低且单次制备量高,总得率可达65%,操作便捷,为碧根果致敏原Car i 1的相关研究奠定了物质基础。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.
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.
文摘目的分离纯化碧根果致敏原Car i 1,并对其结构进行表征鉴定。方法以新鲜碧根果果仁为原料,通过粉碎、脱脂、浸提、粗分级、凝胶过滤层析,对碧根果致敏原蛋白Car i 1进行分离纯化。结合十二烷基硫酸钠-聚丙烯酰胺凝胶电泳、液相色谱-串联质谱法和免疫印迹法3种方法对Cari1进行鉴定,并通过圆二色谱仪与紫外分光光度计表征其二、三级结构。结果本方法纯化获得碧根果致敏原Cari1,单轮制备量可达5 mg以上,且纯度大于95%,蛋白质高级结构未被破坏,能够被全部3名碧根果过敏患者的血清准确识别。结论该纯化方法技术路线简单、设备要求低且单次制备量高,总得率可达65%,操作便捷,为碧根果致敏原Car i 1的相关研究奠定了物质基础。
文摘目的 探讨CAR-T细胞疗法治疗老年急性B淋巴细胞白血病(B-ALL)患者的安全性和有效性。方法 回顾性分析2020年5月—2022年12月苏州大学附属第一医院收治的接受CAR-T治疗的21例老年急性B淋巴细胞白血病患者的临床及随访资料,探讨CAR-T的有效性及安全性。结果 21例老年B-ALL患者CAR-T治疗后细胞因子释放综合征(cytokine release syndrome,CRS),中性粒细胞减少症和中性粒细胞缺乏症发生率分别为:38.1%(8/21),42.9%(9/21)和28.6%(6/21);与CAR-T回输前相比,CAR-T后一周白细胞绝对计数无显著差异,一个月后显著升高(P<0.001),中性粒细胞计数在CAR-T后一周和一个月均无显著差异(P>0.05),C反应蛋白在CAR-T后7天显著升高,30天后显著降低(-3 d vs 7 d,P=0.007;30 d vs 7 d,P=0.000 6);首次输注CAR-T后完全缓解率(complete remission,CR)为85.7%(18/21),中位随访时间为17个月;CAR-T后无进展生存率(progression-free survival,PFS)为81.0%,与性别、CAR-T细胞类型、费城染色体、高肿瘤负荷、桥接造血干细胞移植(HSCT)、治疗次数、LDH值以及血小板计数均无相关性(P>0.05),中位PFS为13个月;R/R B-ALL患者CAR-T治疗后CR率为75%(6/8),PFS率为67.5%,中位PFS时间为12个月;回输CAR-T后复发时间平均为10.2个月。结论 CAR-T细胞疗法用于治疗老年B-ALL患者具有较好的缓解率,为预后差的老年B-ALL患者提供有潜能的治疗手段。
