Evidential Data Fusion for Characterization of Pavement Surface Conditions during Winter Using a Multi-Sensor Approach

The role of a service that is dedicated to road weather analysis is to issue forecasts and warnings to users regarding roadway conditions, thereby making it possible to anticipate dangerous traffic conditions, especially during the winter period. It is important to define pavement conditions at all times. In this paper, a new data acquisition approach is proposed that is based upon the analysis and combination of two sensors in real time by nanocomputer. The first sensor is a camera that records images and videos of the road network. The second sensor is a microphone that records the tire–pavement interaction, to characterize each surface’s condition. The two low-cost sensors were fed to different deep learning architectures that are specialized in surface state analysis; the results were combined using an evidential theory-based data fusion approach. This study is a proof of concept, to test an evidential approach for improving classification with deep learning, applied to only two sensors; however, one could very well add more sensors and make the nanocomputers communicate together, to analyze a larger urban environment.

Reducing the Incidence of Skin Cancer through Landscape Architecture Design Education

There is a high and growing incidence of skin cancer associated with overexposure to the sun. Most of a person’s exposure occurs during their first eighteen years of life. While many children are taught to wear hats and sunscreen, studies indicate these are inadequate. There is a pressing need to improve the design of our landscapes to reduce exposure. Landscape architects can play a key role in driving this process, but only if they understand the factors determining sun protection behaviours among children in the landscape, and how to design for these. We introduced a systematic evidence-based teaching approach to landscape architecture students in New Zealand where the incidence of skin cancer is one of the highest in the world. In this paper, we describe the methods we used to integrate scientific information into a creative design process that included four design phases: (1) review, summary and translation of evidential theory into design guidelines; (2) inventory and analysis of existing schoolyard; (3) redesign of schoolyard; and (4) final design evaluation. We found this process was effective in developing student appreciation for the need to improve sun protection through design, for increasing their understanding of the evidential science, in addition to developing their ability to translate, often inaccessible, evidential data into its spatial form implications. Furthermore, the process led to a high degree of confidence and pride among many students as their resulting design solutions were not only supported by evidence but were often highly creative. Such evidence-based design courses are essential for preparing future landscape architects to design landscapes that significantly reduce the incidence and health effects of skin cancer.

Study on factors affecting preschool education service quality

Purpose This paper aims to explore the critical factors which affect the quality of preschool education service so that targeted and effective measures to improve service quality can be put forward. Design/methodology/approach Evidential theory is applied to aggregate experts’ knowledge, and a fuzzy cognitive map (FCM) model of preschool education service quality is established to further carry out a simulation for inference, thus figuring out the critical factors to improve service quality. Findings The simulation results show that the main body of supervision and environment of governments and policies are two critical factors affecting the quality of preschool education service. More emphasis should be put on these two aspects, and corresponding measures can be put forward so as to ensure the quality of preschool education service. Originality/value This paper proposes a new model based on FCM and evidential theory to study the factors affecting preschool education service quality.

Cloud Update of Tiled Evidential Occupancy Grid Maps for the Multi-Vehicle Mapping

Nowadays, many intelligent vehicles are equipped with various sensors to recognize their surrounding environment and to measure the motion or position of the vehicle. In addition, the number of intelligent vehicles equipped with a mobile Internet modem is increasing. Based on the sensors and Internet connection, the intelligent vehicles are able to share the sensor information with other vehicles via a cloud service. The sensor information sharing via the cloud service promises to improve the safe and efficient operation of the multiple intelligent vehicles. This paper presents a cloud update framework of occupancy grid maps for multiple intelligent vehicles in a large-scale environment. An evidential theory is applied to create the occupancy grid maps to address sensor disturbance such as measurement noise, occlusion and dynamic objects. Multiple vehicles equipped with LiDARs, motion sensors, and a low-cost GPS receiver create the evidential occupancy grid map (EOGM) for their passing trajectory based on GraphSLAM. A geodetic quad-tree tile system is applied to manage the EOGM, which provides a common tiling format to cover the large-scale environment. The created EOGM tiles are uploaded to EOGM cloud and merged with old EOGM tiles in the cloud using Dempster combination of evidential theory. Experiments were performed to evaluate the multiple EOGM mapping and the cloud update framework for large-scale road environment.