An integrated system has been provided with a-Si/H solar cells as energy conversion device,NiCo_(2)O_(4)battery-supercapacitor hybrid(BSH)as energy storage device,and light emitting diodes(LEDs)as energy utilization d...An integrated system has been provided with a-Si/H solar cells as energy conversion device,NiCo_(2)O_(4)battery-supercapacitor hybrid(BSH)as energy storage device,and light emitting diodes(LEDs)as energy utilization device.By designing three-dimensional hierarchical NiCo_(2)O_(4)arrays as faradic electrode,with capacitive electrode of active carbon(AC),BSHs were assembled with energy density of 16.6 Wh kg^(-1),power density of 7285 W kg^(-1),long-term stability with 100%retention after 15,000 cycles,and rather low self-discharge.The NiCo_(2)O_(4)//AC BSH was charged to 1.6 V in 1 s by solar cells and acted as reliable sources for powering LEDs.The integrated system is rational for operation,having an overall efficiency of 8.1%with storage efficiency of 74.24%.The integrated system demonstrates a stable solar power conversion,outstanding energy storage behavior,and reliable light emitting.Our study offers a precious strategy to design a self-driven integrated system for highly efficient energy utilization.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
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
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
In this article, we used the self-excitation and self-inductance characteristics of polyvinylidene fluoride(PVDF) piezoelectric materials, combined with the powerful signal processing and calculation analysis capabili...In this article, we used the self-excitation and self-inductance characteristics of polyvinylidene fluoride(PVDF) piezoelectric materials, combined with the powerful signal processing and calculation analysis capabilities of integrated circuits, for the first time to explore a set of microcantilever sensor "readout system" without additional driver(self-driving) and can realize self-sensing external signal(self-sensing).It was successfully applied to the unlabeled detection of avian influenza virus(AIV) H9N_(2). The specific force of the antigen-antibody complexes on the surface of the microcantilever leads to the change of the stress of the cantilever, which drives the constructed detection device, and does not require an additional excitation source to drive it, that is, the self-driving part. At the same time, due to the movement of piezoelectric charges in the film caused by the positive piezoelectric effect of the PVDF film, self-inductive charges are generated on the surface of the sensor dielectric. The charge signal is converted into a voltage signal, and the sensing part is completed, that is, self-sensing. The immunosensor has a linear range of100-1000 ng/m L with a detection limit of 2.9 ng/m L. The method will also open up a new avenue for the detection of other analytes based on antigen-antibody responses.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
Self-diiviiig tour is one of the most important wajrs for people to travel, and network travel notes actually reflect the traveling information of self-driving tourists. In this paper, witii the network travel notes o...Self-diiviiig tour is one of the most important wajrs for people to travel, and network travel notes actually reflect the traveling information of self-driving tourists. In this paper, witii the network travel notes of self-driving tourists as the tesearch object^ methods such as text analysis and visualization were adopted to study behavior patterns of self-driving tourists, tourism experience, time-space migration, and distribution of tourism resources in Inner Mongolia, fi：om the multiple dimensions of mobile drivers, perceived, dimensions, and spatial migration. The results showed tiiat ①self-cidviiig tourists had a variety of motivations for traveling, in which love for nature dominated; ②self-driving tour destinations were mainly Hulunbuir, Ordos, and Alxa League; ③spatial migration was characterized by obvious seasonal fluctuations. The fesearch on the behavior of self-driving tourists in Inner Mongolia is an important part of the study of the connection between tourism resources and market connection in Inner Mongolia, and is of significance for guiding the theory, practice and poliqr foimuktion of self-doving tours in Inner Mongolia.展开更多
Security systems are a necessity for the deployment of smart vehicles in our society. Security in vehicular ad hoe networks is crucial to the reliable exchange of information and control data. In this paper, we propos...Security systems are a necessity for the deployment of smart vehicles in our society. Security in vehicular ad hoe networks is crucial to the reliable exchange of information and control data. In this paper, we propose an intelligent Intrusion Detection System （IDS） to protect the external communication of self-driving and semi self-driving vehicles. This technology has the ability to detect Denial of Service （DOS） and black hole attacks on vehicular ad hoe networks （VANETs）. The advantage of the proposed IDS over existing security systems is that it detects attacks before they causes significant damage. The intrusion prediction technique is based on Linear Discriminant Analysis （LDA） and Quadratic Diseriminant Analysis （QDA） which are used to predict attacks based on observed vehicle behavior. We perform simulations using Network Simulator 2 to demonstrate that the IDS achieves a low rate of false alarms and high accuracy in detection.展开更多
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
Late this March.China＇s Internet giant Baidu became the first self-driving car developer to obtain temporary license plates to carry out self driving tests on public roads in Beijing.
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
Autonomous vehicles in industrial parks can provide intelligent,efficient,and environmentally friendly transportation services,making them crucial tools for solving internal transportation issues.Considering the chara...Autonomous vehicles in industrial parks can provide intelligent,efficient,and environmentally friendly transportation services,making them crucial tools for solving internal transportation issues.Considering the characteristics of industrial park scenarios and limited resources,designing and implementing autonomous driving solutions for autonomous vehicles in these areas has become a research hotspot.This paper proposes an efficient autonomous driving solution based on path planning,target recognition,and driving decision-making as its core components.Detailed designs for path planning,lane positioning,driving decision-making,and anti-collision algorithms are presented.Performance analysis and experimental validation of the proposed solution demonstrate its effectiveness in meeting the autonomous driving needs within resource-constrained environments in industrial parks.This solution provides important references for enhancing the performance of autonomous vehicles in these areas.展开更多
Self-driving and recreational vehicle(RV)camps are a new form of industry module with the integration of transportation and tourism in China,thus the scientific and reasonable site selection plays an important role in...Self-driving and recreational vehicle(RV)camps are a new form of industry module with the integration of transportation and tourism in China,thus the scientific and reasonable site selection plays an important role in the success of camps’construction and operation.In terms of relying resources and development factors,camps can be divided into five categories:scenic-spot-based,transportation-based,environment-based,project-based and leisure and vacation-based.According to whether it is of excludability and competitiveness,the camps in China mainly embody the attribute of private products.Based on the combination of subjective evaluation and objective calculation,the evaluation model of spatial site selection is constructed and the weight of each index is calculated by using analytic hierarchy process(AHP)method and entropy coefficient method.The results show that traffic condition factor is the priority to the selection of campsite,and whether it is on the popular main self-driving route and the grade of trunk roads are the dominant indices.The second factor taken into consideration is the social factors,in which government policy supports and land cost play a key role.The third factor is the market,in which the urban economic level,partnership with the government and tourist resource conditionsare of great importance.The fourth factor of the campsite selection includes natural elements,in which the quality of ecological environment and water source conditions are mostly considered.In the future,it is suggested that a camp pattern of"public goods"plus"private goods"should be built and the construction of camps in underdeveloped areas should be highly developed so as to form camp spatial network from individual points to a series of campsite and finally the campsite group in China will be set up.展开更多
End-to-end self-driving is a method that directly maps raw visual images to vehicle control signals using deep convolutional neural network(CNN).Although prediction of steering angle has achieved good result in single...End-to-end self-driving is a method that directly maps raw visual images to vehicle control signals using deep convolutional neural network(CNN).Although prediction of steering angle has achieved good result in single task,the current approach does not effectively simultaneously predict the steering angle and the speed.In this paper,various end-to-end multi-task deep learning networks using deep convolutional neural network combined with long short-term memory recurrent neural network(CNN-LSTM)are designed and compared,which could obtain not only the visual spatial information but also the dynamic temporal information in the driving scenarios,and improve steering angle and speed predictions.Furthermore,two auxiliary tasks based on semantic segmentation and object detection are proposed to improve the understanding of driving scenarios.Experiments are conducted on the public Udacity dataset and a newly collected Guangzhou Automotive Cooperate dataset.The results show that the proposed network architecture could predict steering angles and vehicle speed accurately.In addi-tion,the impact of multi-auxiliary tasks on the network performance is analyzed by visualization method,which shows the salient map of network.Finally,the proposed network architecture has been well verified on the autonomous driving simu-lation platform Grand Theft Auto V(GTAV)and experimental road with an average takeover rate of two times per 10 km.展开更多
The photothermal self-driving process of Janus microparticles has wide application prospects in the fields of biomedicine.Since silica and gold have good biocompatibility and high photothermal conversion efficiency,th...The photothermal self-driving process of Janus microparticles has wide application prospects in the fields of biomedicine.Since silica and gold have good biocompatibility and high photothermal conversion efficiency,the SiO_(2)@Au Janus microparticles are widely used as drug carriers.Based on the multiphysics coupling method,the photothermal self-driving process of SiO_(2)@Au Janus microparticles was investi-gated,wherein the substrate was SiO_(2)particles and one side of the particles was coated with gold film.Under a continuous wave laser with irradiation of 20 W/cm^(2),the distance covered by the Janus particles was increased by increasing the thickness of the gold film and reducing the size of the SiO_(2)particles;the self-driving characteristics of the Janus particles were controlled substantially by increasing the intensity of the incident laser.Based on the simulation results,the thermophoretic motion and Brownian motion of particles can be measured by comparing the absolute values of the thermophoretic force impulse,Brownian force impulse,and drag force impulse.The Brownian force acting on Janus microparticles under low laser power cannot be ignored.Furthermore,the minimum laser power required for Janus particles to overcome Brownian motion was calculated.The results can effectively guide the design of Janus particles in biomedicine and systematically analyze the mechanism of particle thermophoretic motion during drug delivery.展开更多
Purpose–This study aims to develop an automatic lane-change mechanism on highways for self-driving articulated trucks to improve traffic safety.Design/methodology/approach–The authors proposed a novel safety lane-cha...Purpose–This study aims to develop an automatic lane-change mechanism on highways for self-driving articulated trucks to improve traffic safety.Design/methodology/approach–The authors proposed a novel safety lane-change path planning and tracking control method for articulated vehicles.A double-Gaussian distribution was introduced to deduce the lane-change trajectories of tractor and trailer coupling characteristics of intelligent vehicles and roads.With different steering and braking maneuvers,minimum safe distances were modeled and calculated.Considering safety and ergonomics,the authors invested multilevel self-driving modes that serve as the basis of decision-making for vehicle lane-change.Furthermore,a combined controller was designed by feedback linearization and single-point preview optimization to ensure the path tracking and robust stability.Specialized hardware in the loop simulation platform was built to verify the effectiveness of the designed method.Findings–The numerical simulation results demonstrated the path-planning model feasibility and controller-combined decision mechanism effectiveness to self-driving trucks.The proposed trajectory model could provide safety lane-change path planning,and the designed controller could ensure good tracking and robust stability for the closed-loop nonlinear system.Originality/value–This is a fundamental research of intelligent local path planning and automatic control for articulated vehicles.There are two main contributions:thefirst is a more quantifiable trajectory model for self-driving articulated vehicles,which provides the opportunity to adapt vehicle and scene changes.The second involves designing a feedback linearization controller,combined with a multi-objective decision-making mode,to improve the comprehensive performance of intelligent vehicles.This study provides a valuable reference to develop advanced driving assistant system and intelligent control systems for self-driving articulated vehicles.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
Beijing is expanding its lead in the development of China’s self-driving technology with a plan to build a 200,000-square-meter testing ground For self-driving vehicles, Beijing Daily reported on December 3.The enclo...Beijing is expanding its lead in the development of China’s self-driving technology with a plan to build a 200,000-square-meter testing ground For self-driving vehicles, Beijing Daily reported on December 3.The enclosed testing ground is expected to open by 2020 in Shunyi District, about 30 km northeast of downtown Beijing. It will require an investment of 480 miLLion yuan ($69.7 million), according to the newspaper.展开更多
In recent years,advancements in autonomous vehicle technology have accelerated,promising safer and more efficient transportation systems.However,achieving fully autonomous driving in challenging weather conditions,par...In recent years,advancements in autonomous vehicle technology have accelerated,promising safer and more efficient transportation systems.However,achieving fully autonomous driving in challenging weather conditions,particularly in snowy environments,remains a challenge.Snow-covered roads introduce unpredictable surface conditions,occlusions,and reduced visibility,that require robust and adaptive path detection algorithms.This paper presents an enhanced road detection framework for snowy environments,leveraging Simple Framework forContrastive Learning of Visual Representations(SimCLR)for Self-Supervised pretraining,hyperparameter optimization,and uncertainty-aware object detection to improve the performance of YouOnly Look Once version 8(YOLOv8).Themodel is trained and evaluated on a custom-built dataset collected from snowy roads in Tromsø,Norway,which covers a range of snow textures,illumination conditions,and road geometries.The proposed framework achieves scores in terms of mAP@50 equal to 99%and mAP@50–95 equal to 97%,demonstrating the effectiveness of YOLOv8 for real-time road detection in extreme winter conditions.The findings contribute to the safe and reliable deployment of autonomous vehicles in Arctic environments,enabling robust decision-making in hazardous weather conditions.This research lays the groundwork for more resilient perceptionmodels in self-driving systems,paving the way for the future development of intelligent and adaptive transportation networks.展开更多
基金support of National Natural Science Foundation of China(Nos.51702284 and 21878270)Zhejiang Provincial Natural Science Foundation of China(LR19B060002)+5 种基金the Startup Foundation for Hundred-Talent Program of Zhejiang University(112100-193820101/001/022)the support of Shenzhen Science and Technology Project of China(JCYJ20170412105400428)the support of Zhejiang Provincial Natural Science Foundation of China(LR16F040001)Open Project of Laboratory for Biomedical Engineering of Ministry of Education,Zhejiang Universitythe support of Innovation Platform of Energy Storage Engineering and New Material in Zhejiang University(K19-534202-002)Provincial Innovation Team on Hydrogen Electric Hybrid Power Systems in Zhejiang Province
文摘An integrated system has been provided with a-Si/H solar cells as energy conversion device,NiCo_(2)O_(4)battery-supercapacitor hybrid(BSH)as energy storage device,and light emitting diodes(LEDs)as energy utilization device.By designing three-dimensional hierarchical NiCo_(2)O_(4)arrays as faradic electrode,with capacitive electrode of active carbon(AC),BSHs were assembled with energy density of 16.6 Wh kg^(-1),power density of 7285 W kg^(-1),long-term stability with 100%retention after 15,000 cycles,and rather low self-discharge.The NiCo_(2)O_(4)//AC BSH was charged to 1.6 V in 1 s by solar cells and acted as reliable sources for powering LEDs.The integrated system is rational for operation,having an overall efficiency of 8.1%with storage efficiency of 74.24%.The integrated system demonstrates a stable solar power conversion,outstanding energy storage behavior,and reliable light emitting.Our study offers a precious strategy to design a self-driven integrated system for highly efficient energy utilization.
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.
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.
基金the financial support from National Natural Science Foundation of China (No. 22102141)the Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD)+2 种基金Nature Science Foundation of Jiangsu Province No.BK20190905Project for Science and Technology of Yangzhou(No. YZ2020067)the open funds of the Ministry of Education Key Lab for Avian Preventive Medicine (No. YF202020)。
文摘In this article, we used the self-excitation and self-inductance characteristics of polyvinylidene fluoride(PVDF) piezoelectric materials, combined with the powerful signal processing and calculation analysis capabilities of integrated circuits, for the first time to explore a set of microcantilever sensor "readout system" without additional driver(self-driving) and can realize self-sensing external signal(self-sensing).It was successfully applied to the unlabeled detection of avian influenza virus(AIV) H9N_(2). The specific force of the antigen-antibody complexes on the surface of the microcantilever leads to the change of the stress of the cantilever, which drives the constructed detection device, and does not require an additional excitation source to drive it, that is, the self-driving part. At the same time, due to the movement of piezoelectric charges in the film caused by the positive piezoelectric effect of the PVDF film, self-inductive charges are generated on the surface of the sensor dielectric. The charge signal is converted into a voltage signal, and the sensing part is completed, that is, self-sensing. The immunosensor has a linear range of100-1000 ng/m L with a detection limit of 2.9 ng/m L. The method will also open up a new avenue for the detection of other analytes based on antigen-antibody responses.
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Sponsored by Scientific Research Projects of Colleges and Universities in the Inner Mongolia Autonomous Region(NJSY018)
文摘Self-diiviiig tour is one of the most important wajrs for people to travel, and network travel notes actually reflect the traveling information of self-driving tourists. In this paper, witii the network travel notes of self-driving tourists as the tesearch object^ methods such as text analysis and visualization were adopted to study behavior patterns of self-driving tourists, tourism experience, time-space migration, and distribution of tourism resources in Inner Mongolia, fi：om the multiple dimensions of mobile drivers, perceived, dimensions, and spatial migration. The results showed tiiat ①self-cidviiig tourists had a variety of motivations for traveling, in which love for nature dominated; ②self-driving tour destinations were mainly Hulunbuir, Ordos, and Alxa League; ③spatial migration was characterized by obvious seasonal fluctuations. The fesearch on the behavior of self-driving tourists in Inner Mongolia is an important part of the study of the connection between tourism resources and market connection in Inner Mongolia, and is of significance for guiding the theory, practice and poliqr foimuktion of self-doving tours in Inner Mongolia.
文摘Security systems are a necessity for the deployment of smart vehicles in our society. Security in vehicular ad hoe networks is crucial to the reliable exchange of information and control data. In this paper, we propose an intelligent Intrusion Detection System （IDS） to protect the external communication of self-driving and semi self-driving vehicles. This technology has the ability to detect Denial of Service （DOS） and black hole attacks on vehicular ad hoe networks （VANETs）. The advantage of the proposed IDS over existing security systems is that it detects attacks before they causes significant damage. The intrusion prediction technique is based on Linear Discriminant Analysis （LDA） and Quadratic Diseriminant Analysis （QDA） which are used to predict attacks based on observed vehicle behavior. We perform simulations using Network Simulator 2 to demonstrate that the IDS achieves a low rate of false alarms and high accuracy in detection.
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.
文摘Late this March.China＇s Internet giant Baidu became the first self-driving car developer to obtain temporary license plates to carry out self driving tests on public roads in Beijing.
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.
基金supported by the Natural Science Foundation of Jiangsu Province(BK20211357)the Qing Lan Project of Jiangsu Province(2022)+1 种基金the Natural Science Foundation of the Jiangsu Higher Education Institutions of China(22KJB520036 and 23KJB510033)the Innovation Project of Engineering Research Center of Integration and Application of Digital Learning Technology of MOE(1221046)。
文摘Autonomous vehicles in industrial parks can provide intelligent,efficient,and environmentally friendly transportation services,making them crucial tools for solving internal transportation issues.Considering the characteristics of industrial park scenarios and limited resources,designing and implementing autonomous driving solutions for autonomous vehicles in these areas has become a research hotspot.This paper proposes an efficient autonomous driving solution based on path planning,target recognition,and driving decision-making as its core components.Detailed designs for path planning,lane positioning,driving decision-making,and anti-collision algorithms are presented.Performance analysis and experimental validation of the proposed solution demonstrate its effectiveness in meeting the autonomous driving needs within resource-constrained environments in industrial parks.This solution provides important references for enhancing the performance of autonomous vehicles in these areas.
基金sponsored by the National Social Science Fund of China(grant number 20&ZD099)with the project name“research on the spatial effects of China’s cross regional major infrastructure”。
文摘Self-driving and recreational vehicle(RV)camps are a new form of industry module with the integration of transportation and tourism in China,thus the scientific and reasonable site selection plays an important role in the success of camps’construction and operation.In terms of relying resources and development factors,camps can be divided into five categories:scenic-spot-based,transportation-based,environment-based,project-based and leisure and vacation-based.According to whether it is of excludability and competitiveness,the camps in China mainly embody the attribute of private products.Based on the combination of subjective evaluation and objective calculation,the evaluation model of spatial site selection is constructed and the weight of each index is calculated by using analytic hierarchy process(AHP)method and entropy coefficient method.The results show that traffic condition factor is the priority to the selection of campsite,and whether it is on the popular main self-driving route and the grade of trunk roads are the dominant indices.The second factor taken into consideration is the social factors,in which government policy supports and land cost play a key role.The third factor is the market,in which the urban economic level,partnership with the government and tourist resource conditionsare of great importance.The fourth factor of the campsite selection includes natural elements,in which the quality of ecological environment and water source conditions are mostly considered.In the future,it is suggested that a camp pattern of"public goods"plus"private goods"should be built and the construction of camps in underdeveloped areas should be highly developed so as to form camp spatial network from individual points to a series of campsite and finally the campsite group in China will be set up.
基金This work is funded by the Youth Talent Lifting Project of China Society of Automotive Engineers.
文摘End-to-end self-driving is a method that directly maps raw visual images to vehicle control signals using deep convolutional neural network(CNN).Although prediction of steering angle has achieved good result in single task,the current approach does not effectively simultaneously predict the steering angle and the speed.In this paper,various end-to-end multi-task deep learning networks using deep convolutional neural network combined with long short-term memory recurrent neural network(CNN-LSTM)are designed and compared,which could obtain not only the visual spatial information but also the dynamic temporal information in the driving scenarios,and improve steering angle and speed predictions.Furthermore,two auxiliary tasks based on semantic segmentation and object detection are proposed to improve the understanding of driving scenarios.Experiments are conducted on the public Udacity dataset and a newly collected Guangzhou Automotive Cooperate dataset.The results show that the proposed network architecture could predict steering angles and vehicle speed accurately.In addi-tion,the impact of multi-auxiliary tasks on the network performance is analyzed by visualization method,which shows the salient map of network.Finally,the proposed network architecture has been well verified on the autonomous driving simu-lation platform Grand Theft Auto V(GTAV)and experimental road with an average takeover rate of two times per 10 km.
基金supported by the Heilongjiang Province Natural Science Foundation(Grant No.LH2019E053)Fundamental Research Funds for Central Universities(Grant No.FRFCU5710051421).
文摘The photothermal self-driving process of Janus microparticles has wide application prospects in the fields of biomedicine.Since silica and gold have good biocompatibility and high photothermal conversion efficiency,the SiO_(2)@Au Janus microparticles are widely used as drug carriers.Based on the multiphysics coupling method,the photothermal self-driving process of SiO_(2)@Au Janus microparticles was investi-gated,wherein the substrate was SiO_(2)particles and one side of the particles was coated with gold film.Under a continuous wave laser with irradiation of 20 W/cm^(2),the distance covered by the Janus particles was increased by increasing the thickness of the gold film and reducing the size of the SiO_(2)particles;the self-driving characteristics of the Janus particles were controlled substantially by increasing the intensity of the incident laser.Based on the simulation results,the thermophoretic motion and Brownian motion of particles can be measured by comparing the absolute values of the thermophoretic force impulse,Brownian force impulse,and drag force impulse.The Brownian force acting on Janus microparticles under low laser power cannot be ignored.Furthermore,the minimum laser power required for Janus particles to overcome Brownian motion was calculated.The results can effectively guide the design of Janus particles in biomedicine and systematically analyze the mechanism of particle thermophoretic motion during drug delivery.
文摘Purpose–This study aims to develop an automatic lane-change mechanism on highways for self-driving articulated trucks to improve traffic safety.Design/methodology/approach–The authors proposed a novel safety lane-change path planning and tracking control method for articulated vehicles.A double-Gaussian distribution was introduced to deduce the lane-change trajectories of tractor and trailer coupling characteristics of intelligent vehicles and roads.With different steering and braking maneuvers,minimum safe distances were modeled and calculated.Considering safety and ergonomics,the authors invested multilevel self-driving modes that serve as the basis of decision-making for vehicle lane-change.Furthermore,a combined controller was designed by feedback linearization and single-point preview optimization to ensure the path tracking and robust stability.Specialized hardware in the loop simulation platform was built to verify the effectiveness of the designed method.Findings–The numerical simulation results demonstrated the path-planning model feasibility and controller-combined decision mechanism effectiveness to self-driving trucks.The proposed trajectory model could provide safety lane-change path planning,and the designed controller could ensure good tracking and robust stability for the closed-loop nonlinear system.Originality/value–This is a fundamental research of intelligent local path planning and automatic control for articulated vehicles.There are two main contributions:thefirst is a more quantifiable trajectory model for self-driving articulated vehicles,which provides the opportunity to adapt vehicle and scene changes.The second involves designing a feedback linearization controller,combined with a multi-objective decision-making mode,to improve the comprehensive performance of intelligent vehicles.This study provides a valuable reference to develop advanced driving assistant system and intelligent control systems for self-driving articulated vehicles.
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Beijing is expanding its lead in the development of China’s self-driving technology with a plan to build a 200,000-square-meter testing ground For self-driving vehicles, Beijing Daily reported on December 3.The enclosed testing ground is expected to open by 2020 in Shunyi District, about 30 km northeast of downtown Beijing. It will require an investment of 480 miLLion yuan ($69.7 million), according to the newspaper.
文摘In recent years,advancements in autonomous vehicle technology have accelerated,promising safer and more efficient transportation systems.However,achieving fully autonomous driving in challenging weather conditions,particularly in snowy environments,remains a challenge.Snow-covered roads introduce unpredictable surface conditions,occlusions,and reduced visibility,that require robust and adaptive path detection algorithms.This paper presents an enhanced road detection framework for snowy environments,leveraging Simple Framework forContrastive Learning of Visual Representations(SimCLR)for Self-Supervised pretraining,hyperparameter optimization,and uncertainty-aware object detection to improve the performance of YouOnly Look Once version 8(YOLOv8).Themodel is trained and evaluated on a custom-built dataset collected from snowy roads in Tromsø,Norway,which covers a range of snow textures,illumination conditions,and road geometries.The proposed framework achieves scores in terms of mAP@50 equal to 99%and mAP@50–95 equal to 97%,demonstrating the effectiveness of YOLOv8 for real-time road detection in extreme winter conditions.The findings contribute to the safe and reliable deployment of autonomous vehicles in Arctic environments,enabling robust decision-making in hazardous weather conditions.This research lays the groundwork for more resilient perceptionmodels in self-driving systems,paving the way for the future development of intelligent and adaptive transportation networks.
