Improving low cost sensor based vehicle positioning with Machine Learning

Learning Wheel Odometry and IMU Errors for Localization

“…[27] studies visual odometry from the perspective of end-to-end deep learning. [28] combines a machine learning technique with an EKF for fusion of wheel speed sensors and GPS. Finally, [29] considers the problem of system identification of helicopter dynamics, and poses the dynamics modeling problem as a high-dimensional regression problem which is solved with the help of stochastic variational inference.…”

Data Mining and Machine Learning to Promote Smart Cities: A Systematic Review from 2000 to 2018

“…Yuan et al [22] T-Drive: Enhancing Driving Directions with Taxi Drivers' Intelligence Rudin et al [23] Machine Learning for the New York City Power Grid Jurado et al [24] Hybrid methodologies for electricity load forecasting: Entropy-based feature selection with machine learning and soft computing techniques Pérez-Chacón et al [25] Big data analytics for discovering electricity consumption patterns in smart cities Peña et al [26] Rule-based system to detect energy efficiency anomalies in smart buildings, a data mining approach Liu et al [27] A machine learning-based method for the large-scale evaluation of the qualities of the urban environment Muhammed et al [28] UbeHealth: A Personalized Ubiquitous Cloud and Edge-Enabled Networked Healthcare System for Smart Cities Massana et al [29] Identifying services for short-term load forecasting using data driven models in a Smart city platform Wang et al [30] Identification of key energy efficiency drivers through global city benchmarking: a data driven approach Abbasi and El Hanandeh [31] Forecasting municipal solid waste generation using artificial intelligence modelling approaches Badii et al [32] Predicting Available Parking Slots on Critical and Regular Services by Exploiting a Range of Open Data Madu et al [33] Urban sustainability management: A deep learning perspective Gomede et al [34] Application of Computational Intelligence to Improve Education in Smart Cities. Cramer et al [35] An extensive evaluation of seven machine learning methods for rainfall prediction in weather derivatives You and Yang [36] Urban expansion in 30 megacities of China: categorizing the driving force profiles to inform the urbanization policy Nagy and Simon [37] Survey on traffic prediction in smart cities Belhajem et al [38] Improving Vehicle Localization in a Smart City with Low Cost Sensor Networks and Support Vector Machines Fernández-Ares et al [39] Studying real traffic and mobility scenarios for a Smart City using a new monitoring and tracking system Belhajem et al [40] Improving low cost sensor based vehicle positioning with Machine Learning Gopalakrishnan [41] Deep Learning in Data-Driven Pavement Image Analysis and Automated Distress Detection: A Review Khan et al [42] Smart City and Smart Tourism: A Case of Dubai Idowu et al [43] Applied machine learning: Forecasting heat load in district heating system Bellini et al [44] Wi-Fi based...…”

“…The authors Belhajem et al [40] used SVM and extended Kalman filter to provide more accurate information about the positioning of vehicles for smart cities.…”

“…The studies also exhibit that the proposed studies are motivated to find solutions for specific domains of the city (e.g., traffic, mobility, environment, and so on). Clustering analyses were the most used approaches in the studies [22,25,30,39,44,46,50,52,54,57] and later, supported vector machine [23,31,38,40,43,49,57].…”

“…The designated studies focused primarily on the areas of smart mobility [22,32,33,[36][37][38][39][40][41][42][48][49][50][51][52]54,[56][57][58][59][60] and smart environment (RQ2) [23][24][25][26][29][30][31]33,35,36,42,43,[45][46][47]52,53,55,59].…”