Roadside Unit Deployment of Cooperative Vehicle-Infrastructure System Based on Digital Measurable Image Method

Impact of early-stage cooperative vehicle infrastructure systems on traffic and energy consumption under various traffic conditions: From application to policy

Unknown remaining time of signal phase at a signalized intersection generally results in extra accelerations and decelerations that increase variations of operating conditions and thus emissions. A cooperative vehicle-infrastructure system can reduce unnecessary speed changes by establishing communications between vehicles and the signal infrastructure. However, the environmental benefits largely depend on drivers’ compliance behaviors. To quantify the effects of drivers’ compliance rates on emissions, this study applied VISSIM 5.20 (Planung Transport Verkehr AG, Karlsruhe, Germany) to develop a simulation model for a signalized intersection, in which light duty vehicles were equipped with a cooperative vehicle-infrastructure system. A vehicle-specific power (VSP)-based model was used to estimate emissions. Based on simulation data, the effects of different compliance rates on VSP distributions, emission factors, and total emissions were analyzed. The results show the higher compliance rate decreases the proportion of VSP bin = 0, which means that the frequencies of braking and idling were lower and light duty vehicles ran more smoothly at the intersection if more light duty vehicles complied with the cooperative vehicle-infrastructure system, and emission factors for light duty vehicles decreased significantly as the compliance rate increased. The case study shows higher total emission reductions were observed with higher compliance rate for all of CO2, NOx, HC, and CO emissions. CO2 was reduced most significantly, decreased by 16% and 22% with compliance rates of 0.3 and 0.7, respectively.

Video application is a research hotspot in cooperative vehicle-infrastructure systems (CVIS) which is greatly related to traffic safety and the quality of user experience. Dealing with large datasets of feedback from complex environments is a challenge when using traditional video application approaches. However, the in-depth structure of deep learning has the ability to deal with high-dimensional data sets, which shows better performance in video application problems. Therefore, the research value and significance of video applications over CVIS can be better reflected through deep learning. Firstly, the research status of traditional video application methods and deep learning methods over CVIS were introduced; the existing video application methods based on deep learning were classified according to generative and discriminative deep architecture. Then, we summarized the main methods of deep learning and deep reinforcement learning algorithms for video applications over CVIS, and made a comparative study of their performances. Finally, the challenges and development trends of deep learning in the field were explored and discussed.

The ability of edge computing determines the effectiveness of the cooperative vehicle-infrastructure system to improve traffic safety, efficiency and capacity, enabling traditional information release method have backstage processing and distribution. It is converted to vehicle, road and backstage after being processed by edge computing algorithm. Vehicles are timely and effectively controlled and induced. Based on the full analysis of the development status at home and abroad and the analysis of actual engineering needs, this paper proposes the composition of the cooperative vehicle-infrastructure system and the main factors affecting edge computing, studies the existing edge computing methods, and finally combines the current smart highway demonstration projects. The actual needs put forward the cooperative vehicle-infrastructure use case research based on edge computing. The use case analysis has significant guiding role for the design, R&D and engineering implementation of the smart highway cooperative vehicle-infrastructure system in China.

As one of the most important part of the modern comprehensive transportation system, highway plays a critical role in national traffic system, which bears the most of traffic load. Whereas, ramp areas may become bottleneck areas of highway if the control is inappropriate or ineffective. The cooperative vehicle infrastructure system (CVIS) brings great opportunities for ramp control to address many tricky problems, among which the merging control of on-ramp vehicles is one of the most prominent problem related to traffic efficiency and safety. In this paper, we apply the technology of cooperative vehicle infrastructure control in the highway ramp area to enhance the traffic efficiency and safety, and propose a merging control method for on-ramp vehicles. At last, a simulation is conducted to evaluate this control method.

Real-time regional path decision method in cooperative vehicle infrastructure system

Development and experiment of an intelligent connected cooperative vehicle infrastructure system based on multiple V2I modes and BWM-IGR method

Quantitation of thin-layer chromatography (TLC) using image analysis is attractive for its low cost and convenience. The image analysis is investigated by designing a digital imaging system with simple equipment, developing an image analysis software based on our algorithm, and validated the system in the TLC quantitative assay of cichoric acid present in Echinacea purpurea (L.) Moench. TLC used a polyamide thin-layer plate with chloroform-methanol formic acid-water (3:6:1:1) as the mobile phase and 3% (m/v) aqueous aluminum chloride solution as the visualization reagent. Images are acquired with a standard digital camera under a UV viewing lamp (365 nm) in a dark room. The three-dimensional gray scale digital image dataset (x, y, gray) is reduced to two-dimensional dataset (distance, accumulative gray) and then plotted as a curve. The area under the peak corresponding to the cichoric acid spot is integrated and used for quantitation. The whole method was validated by the assay tests of detection limit, calibration curve, repeatability, reproducibility, and recovery. The results showed that our digital imaging method and image analysis algorithm were applicable for the quantification of TLC. The whole method is convenient, efficient, and moderately accurate for the quantitative assay of cichoric acid present in Echinacea purpurea (L.) Moench.

SummaryAfter‐cooking darkening (ACD) phenomenon occurs when potato tubers develop greyish dark colour after boiling, frying, or steaming. The pigment responsible for ACD is a complex of chlorogenic acid and iron, which is formed during cooking and oxidised during cooling. The evaluation of ACD is thus based either on the amount of chlorogenic acid in raw tubers or the greyish colour intensity on cooked tuber surfaces. This study introduced a new method, the digital imaging method, for evaluation of potato ACD. The comparison of this new method with visual evaluation and the measurement of chlorogenic acid using HPLC were also presented. The digital imaging method is based on the direct measurement of the degree of dark colour on cut tuber surfaces, expressed by pixel levels. The new method is faster, more reliable and simpler when compared with any methods presently used. It is particularly applicable to handling large sample numbers.

Collaboration between infrastructures and connected and autonomous vehicles (CAVs) at unsignalised intersections has emerged as an effective strategy for improving transportation efficiency. To enhance the safety and efficiency of CAVs passing through unsignalised intersections, this paper proposes a cooperative vehicle-infrastructure system (CVIS) based multi-vehicle trajectory planning architecture. The architecture comprises a roadside planner and vehicle planners. The roadside planner formulates the problem of generating multi-vehicle trajectories as an optimisation task and addresses it using an improved particle swarm optimisation (PSO) approach along with a space-time voxels strategy to determine the times for vehicles to reach the intersection and the trajectories passing through it. Then, the vehicle planner plans according to the results of the roadside planner. The results show that our approach enables potentially conflicting CAVs to pass through unsignalised intersections without stopping and waiting, ensuring safety while improving traffic efficiency.

This work presents the solution based on the augmenting sequence of linear programming problems (LPP) as a tool for intellectualizing home environment. The proposed solution empowers the intelligent decision making procedure which can be applied to various intelligent control applications. The augmenting self-training procedure based on LPP approach is presented as well, which allows making reasonable decisions having only limited data about the controlled environment. The method permits retraining the decision making system when new data is available. As a proof of concept, this solution is applied to intelligent light control application. The obtained simulation results show the method’s capability in making reasonable decisions according to users preferences. DOI: http://dx.doi.org/10.5755/j01.itc.44.2.10235

A progressive reduction in the area of foot ulcer on serial measurement is traditionally done by tracing the margin of the ulcer on a transparent film and counting the number of squares on a graph paper underneath. We set out to use and validate the measurement of foot ulcers using a digital imaging technique and compare this with the traditional method. Thirty diabetic foot ulcers (18 patients) were studied over 10 weeks. Each ulcer was traced by three independent observers with a pen over a flexigrid Opsite film and digital photographs were taken. Each observer calculated the area of an ulcer first using a 1-mm2 graph paper and then with the computer software. For each ulcer we calculated the mean area using measurements from all the observers. We then calculated the deviation from this mean for each observer. There was significantly less interobserver variation using the digital image than the traditional method with mean coefficient of variation (CV) 16% vs. 27%; P = 0.05. The digital imaging method was faster and easier to use and the patients preferred it, as it was a noncontact method. In addition it also provides a photographic record for comparison.

Measuring volume and density of a barley grain using ellipsoid approximation from a 2-D digital image

The study of grain size distribution of gravels is an important and challenging issue in stratigraphy and morphology, especially in the field of automated measurement. It largely reduces many manual processes and time consumption. Using digital image processing method to extract effective information from digital images is a more advanced method. In this study, a digital image method using automated multiple superpixel segmentation and merging is proposed for extraction of the grain size data. It adds grain size estimation and improved merging rules and repeated execution rules to the superpixel segmentation and merging. It has made great improvements in many respects, especially in the accuracy of edge segmentation and measurement. Compared with manual measurement and other image processing ways, the method proposed in this paper is an efficient approach for precisely measuring the grain size distribution of riverbed material.

Wine authentication: Current progress and state of the art