Estimating QOL from Car View Scene using Deep Neural Network and Clustering Approach

Abstract

One of JST and JICA's technical cooperation projects, ”Smart Transportation Strategy to Realize Thailand 4.0,” aims to reduce traffic congestion to simultaneously achieve low carbon emissions and improve citizens’ quality of Life (QoL). To achieve this goal, it is necessary to develop an AI-based system that allows users to select a combination of transportation modes that meets the various needs of people of all ages and genders, and to realize this goal, evaluation of QoL from images is required. The project suggests how to improve QoL when people moves from some point to another point and some application software to recommend the time scheduling about how to optimize the QoL in a day. It recommends the highest QoL route when we have several path candidate of roads and further some application is necessary to show how QoL dynamically changes in the route. That is scenario of the QoL project. In the previous research, a method for QoL estimation from images was proposed based on semantic segmentation and MLP (Multi Layer Perceptron) by constructing a unique questionnaire form and collecting data in a car view scene. However, the previous method estimates QoL without considering differences in the attributes of the questionnaire data, so the constructed model does not satisfy the attributes of all people. In the proposed method, multiple subjects are asked to subjectively evaluate QoL in 5 levels for the same driving image data set, and the subjects are grouped by clustering based on the evaluation results. The effectiveness of the proposed method was demonstrated by comparing the MAE (mean absolute error) of the model constructed by the proposed method with that constructed by other subjects in a 5-step cross-validation.