Network Evolution Analysis of Vehicle Road-Driving Behavior Strategies and Design of Information Guidance Algorithm

By analyzing the influence of time and safety factors on the behavior strategies of vehicles on the road,a network game evolution model between drivers that considers the behavior strategies of the driving vehicle itself and its neighbors is constructed,and the competition relationship between different types of cars is studied.The influence of the proportion of driving vehicle types on the potential risk of the road is also discussed.This paper presents a guidance algorithm for vehicle dynamic behavior preference information.The correctness of the algorithm is verified by an example.Research shows:The choice of behavior strategies,such as speeding and lane changing,is related to the expected benefits of time,safety,and neighboring vehicle strategies,and the critical value of payable benefits is obtained.The higher the proportion of aggressive vehicles on the road,the greater the potential risk on the road.Whether there is a vehicle in the adjacent lane of the driving vehicle will affect the type of driving vehicle.Information guidance helps to stabilize the state of vehicles on the road,and the policy transition probability also helps stabilize the form of vehicles cars on the road.Still,information guidance has a more significant impact on the transition of vehicle types.Finally,the guidance strategy of managers is given when the road is smooth and congested.

An Adaptive Vision Navigation Algorithm in Agricultural IoT System for Smart Agricultural Robots

As the agricultural internet of things(IoT)technology has evolved,smart agricultural robots needs to have both flexibility and adaptability when moving in complex field environments.In this paper,we propose the concept of a vision-based navigation system for the agricultural IoT and a binocular vision navigation algorithm for smart agricultural robots,which can fuse the edge contour and the height information of rows of crop in images to extract the navigation parameters.First,the speeded-up robust feature(SURF)extracting and matching algorithm is used to obtain featuring point pairs from the green crop row images observed by the binocular parallel vision system.Then the confidence density image is constructed by integrating the enhanced elevation image and the corresponding binarized crop row image,where the edge contour and the height information of crop row are fused to extract the navigation parameters(θ,d)based on the model of a smart agricultural robot.Finally,the five navigation network instruction sets are designed based on the navigation angleθand the lateral distance d,which represent the basic movements for a certain type of smart agricultural robot working in a field.Simulated experimental results in the laboratory show that the algorithm proposed in this study is effective with small turning errors and low standard deviations,and can provide a valuable reference for the further practical application of binocular vision navigation systems in smart agricultural robots in the agricultural IoT system.