DeepWiTraffic: Low Cost WiFi-Based Traffic Monitoring System Using Deep Learning

A traffic monitoring system (TMS) is an integral part of Intelligent Transportation Systems (ITS). It is an essential tool for traffic analysis and planning. One of the biggest challenges is, however, the high cost especially in covering the huge rural road network. In this paper, we propose to address the problem by developing a novel TMS called DeepWiTraffic. DeepWiTraffic is a low-cost, portable, and non-intrusive solution that is built only with two WiFi transceivers. It exploits the unique WiFi Channel State Information (CSI) of passing vehicles to perform detection and classification of vehicles. Spatial and temporal correlations of CSI amplitude and phase data are identified and analyzed using a machine learning technique to classify vehicles into five different types: motorcycles, passenger vehicles, SUVs, pickup trucks, and large trucks. A large amount of CSI data and ground-truth video data are collected over a month period from a real-world two-lane rural roadway to validate the effectiveness of DeepWiTraffic. The results validate that DeepWiTraffic is an effective TMS with the average detection accuracy of 99.4% and the average classification accuracy of 91.1% in comparison with state-of-the-art non-intrusive TMSs.Comment: Accepted for publication in the 16th IEEE International Conference on Mobile Ad-Hoc and Smart Systems (MASS), 201

Design and Construction of an Automatic Home and Office Power Control System

In homes and offices, it is very common for occupants to forget to switch OFF the lighting and fans when leaving the premises. This can be attributed to human forgetfulness and the epileptic power supply which causes interruption that results in users forgetting the state of their appliances (whether they are ON or OFF). Consequently, these appliances would continue to work whenever power is restored when the occupants might have vacated the premise. This action is not a small contributor to energy wastage in a country like Nigeria where there is an inadequate energy supply to go round the populace. In this work, a simple but robust automatic home and office power control system is developed to auto-detect the presence of an occupant in the room through the passive infrared (PIR) sensor and control the electrical appliances (lighting and fan source) in the room. Certain conditions must be met for the operation of lighting and the fan source. The lighting comes up when the PIR sensor senses the presence of an occupant and the room is in darkness, while the fan would work when there is an occupant and the temperature in the room is above 35 °C. These conditions are programmed to suit the need of the occupant but cannot be changed by the user. The device automatically switches OFF within five minutes after the last occupant leaves the room

Enabling Low Cost WIFI-Based Traffic Monitoring System Using Deep Learning

A traffic monitoring system (TMS) is an integral part of Intelligent Transportation Systems (ITS) for traffic analysis and planning. However, covering huge miles of rural highways (119,247 miles in U.S.) with a large number of TMSs is a very challenging problem due to the cost issue. This paper aims to address the problem by developing a low-cost and portable TMS called DeepWiTraffic based on COTs WiFi devices. The proposed system enables accurate vehicle detection (counting) and classification by exploiting the unique WiFi Channel State Information (CSI) of passing vehicles. Spatial and temporal correlations of CSI amplitude and phase data are identified and analyzed using a deep learning technique to classify a vehicle into five different types: motorcycle, passenger vehicle, SUV, pickup truck, and large truck (a vehicle with more than three axles according to the FHWA classification). The principal component analysis (PCA) technique is exploited to reduce the dimension of the subcarriers and remove the device specific noise. The CSI phase data of a received signal are preprocessed by applying a linear transformation and the correlations of CSI phase information of multiple subcarriers are taken into account for effective vehicle classification. A convolutional neural network (CNN) is designed to extract optimal features of the preprocessed CSI amplitude and phase data. A huge amount of CSI data of passing vehicles as well as ground truth video data are collected for about 120 hours to validate the performance of the proposed proof-of-concept system. The results show that the average detection accuracy of 99.4%, and the average classification accuracy of 91.1% (Motorcycle: 97.2%, Passenger Car: 91.1%, SUV: 83.8%, Pickup Truck: 83.3%, and Large Truck: 99.7%) can be achieved with a very small cost of less than $1,000

Intelligent Traffic Monitoring Systems for Vehicle Classification: A Survey

A traffic monitoring system is an integral part of Intelligent Transportation Systems (ITS). It is one of the critical transportation infrastructures that transportation agencies invest a huge amount of money to collect and analyze the traffic data to better utilize the roadway systems, improve the safety of transportation, and establish future transportation plans. With recent advances in MEMS, machine learning, and wireless communication technologies, numerous innovative traffic monitoring systems have been developed. In this article, we present a review of state-of-the-art traffic monitoring systems focusing on the major functionality--vehicle classification. We organize various vehicle classification systems, examine research issues and technical challenges, and discuss hardware/software design, deployment experience, and system performance of vehicle classification systems. Finally, we discuss a number of critical open problems and future research directions in an aim to provide valuable resources to academia, industry, and government agencies for selecting appropriate technologies for their traffic monitoring applications.Comment: Published in IEEE Acces

Sensor ultrasònic múltiple per a conducció autònoma

El present treball busca solucionar una problemàtica de detecció d’obstacles amb sensors d’ultrasons. El treball s’emmarca en la recerca en control de velocitat adaptatiu (adaptive cruise control en anglès) dut a terme per l’IOC de la UPC. L’estudi gira entorn la implementació de una solució amb diversos sensors mitjançant un filtre de Kalman, així que fa incís en la formació sobre el funcionament d’aquest. En una primera instància, s’estudien les bases físiques i matemàtiques del filtre així com els algorismes de processament d’aquest i a continuació es dissenya un filtre per poder solucionar la problemàtica afrontada. Aquest disseny es simula mitjançant el programari Octave per ratificar-ne la fiabilitat. Finalment es realitza una proposta d’implementació sobre el maquinari i programari amb els quals el sistema treballa actualment emprant el programari Eclipse