Stochastic Parameter Identification Method for Driving Trajectory Simulation Processes Based on Mobile Edge Computing and Self‐Organizing Feature Mapping

Yang, Jingfeng; Luo, Zhiyong; Zhang, Nanfeng; Xiao, Jinchao; Wang, Honggang; Zhou, Shengpei; Xiao-song, Liu; Li, Ming

doi:10.1155/2021/8884390

Cited by 4 publications

(5 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The RBFNN has strong nonlinear approximation ability but simple network architecture. Also, it has a fast learning speed and the hidden layer input and output matrices have a linear relationship, which makes it an ideal algorithm for calculating the degree of influence (Yang et al, 2018;Wang et al, 2018;Yang et al, 2021;Zhao and Liu, 2021).…”

Section: Radial Basis Function Neural Networkmentioning

confidence: 99%

See 1 more Smart Citation

Collaborative processing and data optimization of environmental perception technologies for autonomous vehicles

Song

Zhou

Chang³

et al. 2021

Self Cite

View full text Add to dashboard Cite

Purpose Autonomous driving depends on the collection, processing and analysis of environmental information and vehicle information. Environmental perception and processing are important prerequisite for the safety of self-driving of vehicles; it involves road boundary detection, vehicle detection, pedestrian detection using sensors such as laser rangefinder, video camera, vehicle borne radar, etc. Design/methodology/approach Subjected to various environmental factors, the data clock information is often out of sync because of different data acquisition frequency, which leads to the difficulty in data fusion. In this study, according to practical requirements, a multi-sensor environmental perception collaborative method was first proposed; then, based on the principle of target priority, large-scale priority, moving target priority and difference priority, a multi-sensor data fusion optimization algorithm based on convolutional neural network was proposed. Findings The average unload scheduling delay of the algorithm for test data before and after optimization under different network transmission rates. It can be seen that with the improvement of network transmission rate and processing capacity, the unload scheduling delay decreased after optimization and the performance of the test results is the closest to the optimal solution indicating the excellent performance of the optimization algorithm and its adaptivity to different environments. Originality/value In this paper, the results showed that the proposed method significantly improved the redundancy and fault tolerance of the system thus ensuring fast and correct decision-making during driving.

show abstract

Section: Radial Basis Function Neural Networkmentioning

confidence: 99%

“…In supervised learning, the determined hidden layer parameters and the least square method are used to calculate the weights of the hidden layer and the output layer. The output of the i-th hidden layer of the RBF neural network is expressed as (Yang et al, 2018;Wang et al, 2018;Yang et al, 2021;Zhao and Liu, 2021):…”

Section: Radial Basis Function Neural Networkmentioning

confidence: 99%

Collaborative processing and data optimization of environmental perception technologies for autonomous vehicles

Song

Zhou

Chang³

et al. 2021

Self Cite

View full text Add to dashboard Cite

show abstract

“…However, most of the existing bus scheduling optimization methods have been developed based on certain assumptions, such as fixed inter-station running time [12], fixed demand of passengers in each group of origin-destinations (ODs) [12], and fixed frequency [13], which conflict with the actual management. In addition, continuous investment in customized buses [14][15][16], the promotion of multiple payment methods [17][18][19], and the application of autonomous driving [20][21][22][23], combined with various neural networks and route planning methods [24][25][26], have increased the complexity of statistical analysis of bus passenger flow and have put higher requirements for the optimization of intelligent bus scheduling methods.…”

Section: Introductionmentioning

confidence: 99%

Intelligent Bus Scheduling Control Based on On-Board Bus Controller and Simulated Annealing Genetic Algorithm

Xiao

Yang

2022

Electronics

View full text Add to dashboard Cite

The stable and fast service of a bus network is one of the important indicators of the service quality and management level of urban public transport. With the continuous expansion of cities, the bus network complexity has been increasing accordingly. The application of new technologies such as self-driving buses has made the bus network more complex and its vulnerability more obvious. Therefore, how to collect information on passenger flow, traffic flow, and transport distribution using intelligent means, and how to establish an effective intelligent bus scheduling control method have been important questions surrounding the improvement of the level of urban bus operation. To address this challenge, this paper proposes the design method of a bus controller based on data collection and the edge computing requirements of autonomous driving buses; and installs them widely on buses. In addition, an intelligent bus control scheduling method based on the simulated annealing genetic algorithm was developed according to the current scheduling requirements. The proposed method combines the strong local search ability of the simulated annealing algorithm, which prevents the search process from falling into a local optimum, and the strong search ability of the genetic algorithm in the overall search process, leading an intelligent bus control scheduling method based on the simulated annealing genetic algorithm. The proposed method was verified by experiments on the optimal scheduling of multi-destination public transport as an example, we verified the research method, and finally, simulated it using historical data. There is good model prediction of the experimental results. Therefore, the intelligent traffic control can be realized through efficient bus scheduling, thus improving the robustness of the bus network operation.

show abstract

“…In addition, neural networks [10][11][12], fuzzy control [13,14], and deep learning [15] have been widely used in bus scheduling. Through the recent introduction of customized buses [16][17][18], the adoption of different payment methods [19][20][21], and the promotion of self-driving vehicles [22][23][24][25], the complexity of bus-passenger flow has significantly increased, which has led to new challenges for bus-scheduling services.…”

Section: Introductionmentioning

confidence: 99%

A Bus-Scheduling Method Based on Multi-Sensor Data Fusion and Payment Authenticity Verification

Xiao

Yang

2022

Electronics

View full text Add to dashboard Cite

It is of great significance to ensure public transportation management capabilities by improving urban public transport services. One method is to solve the problems related to the quality of data submitted for public funding as well as the accuracy and transparency of the supervision and review processes; moreover, improving public-transportation-service systems is a viable method to solve such problems. With technological advancements and the application of new technologies such as automatic driving and multiple payment, it has gradually become difficult for user-data verification systems, based on the original single bus payment method, to cater to these new technologies. Diversified payment and complex management methods have highlighted the need for new verification methods. Firstly, in this paper, we constructed the Origin–Destination (OD) model of bus-passenger flows by using real-time transmission of passenger-multiple-payment data, on-board-video passenger flow detection data and vehicle real-time positioning data. On this basis, the bus waybill data of other intelligent bus systems and the wait data of bus stations were integrated, so as to establish the travel chain theory by matching passenger flow and the temporal and spatial distribution model. Then, an OD analysis of public-transport passenger flows could be carried out, with a detailed analysis of vehicle, station and line-passenger flow, and the travel characteristics of public transport passenger flow could be excavated. Then, according to the means-end chain theory, the spatiotemporal distribution of the passenger flow data was obtained to carry out an OD analysis of the passenger flow, so as to perform a refinement analysis of the vehicle, station, and passenger flow. Thereby, the characteristics of the passenger flow were explored. Subsequently, payment-authenticity-verification models were established for the data-validity assessment, video-data analysis, passenger-flow estimation, and early warnings in order to determine the authenticity of the payment data. Lastly, based on the multi-sensor passenger flow data fusion and the data authenticity verification models, combined with the application of new technologies such as the use of autonomous buses, the test was promoted. That is, by taking intelligent bus scheduling as the scenario, the research method was tested and verified with real-time passenger flow data according to historical data. The results showed that the method accurately predicted the passenger flow, and had a positive role in improving the efficiency of payment-data-authenticity verification. The application of the method can enhance the management and service quality of public transportation.

show abstract

Stochastic Parameter Identification Method for Driving Trajectory Simulation Processes Based on Mobile Edge Computing and Self‐Organizing Feature Mapping

Cited by 4 publications

References 18 publications

Collaborative processing and data optimization of environmental perception technologies for autonomous vehicles

Collaborative processing and data optimization of environmental perception technologies for autonomous vehicles

Intelligent Bus Scheduling Control Based on On-Board Bus Controller and Simulated Annealing Genetic Algorithm

A Bus-Scheduling Method Based on Multi-Sensor Data Fusion and Payment Authenticity Verification

Contact Info

Product

Resources

About