An Innovative Multi-Sensor Fusion Algorithm to Enhance Positioning Accuracy of an Instrumented Bicycle

Cycling is an increasingly popular mode of travel in cities, but its poor safety record currently acts as a hurdle to its wider adoption as a real alternative to the private car. A particular source of hazard appears to originate from the interaction of cyclists with motorized traffic at low speeds in urban areas. But while technological advances in recent years have resulted in numerous attempts at systems for preventing cyclist-vehicle collisions, these have generally encountered the challenge of accurate cyclist localization. This paper addresses this challenge by introducing an innovative bicycle localization algorithm, which is derived from the geometrical relationships and kinematics of bicycles. The algorithm relies on the measurement of a set of kinematic variables (such as yaw, roll, and steering angles) through low-cost on-board sensors. It then employs a set of Kalman filters to predict-correct the direction and position of the bicycle and fuse the measurements in order to improve positioning accuracy. The capabilities of the algorithm are then demonstrated through a real-world field experiment using an instrumented bicycle, called ``iBike'', in an urban environment. The results show that the proposed fusion achieves considerably lower positioning errors than that would be achieved based on dead-reckoning alone, which makes the algorithm a credible basis for the development of future collision warning and avoidance systems

Inertial Navigation and Position Uncertainty during a Blind Safe Stop of an Autonomous Vehicle

This work considers the problem of position and position-uncertainty estimation for atonomous vehicles during power black-out, where it cannot be assumed that any position data is accessible. To tackle this problem, the position estimation will instead be performed using power separated and independent measurement devices, including one inertial 6 Degrees of Freedom (DOF) measurement unit, four angular wheel speed sensors and one pinion angle sensor. The measurement unit\u27s sensors are initially characterized in order to understand conceptual limitations of the inertial navigation and also to be used in a filtering process. Measurement models are then fused together with vehicle dynamics process models using the architecture of an Extended Kalman Filter (EKF). Two different EKF filter concepts are developed to estimate the vehicle position during a safe stop; one simpler filter for smooth manoeuvres and a complex filter for aggressive manoeuvres. Both filter designs are tested and evaluated with data gathered from an experimental vehicle for selected manoeuvres of developed safe-stop scenarios. The experimental results from a set of use-case manoeuvres show a trend where the size of the position estimation errors significantly grows above an initial vehicle speed of 70 km/h. This paper contributes to develop vehicle dynamics models for the purpose of a blind safe stop

A low-cost intelligent localisation system to improve cyclist safety

Cycling is an increasingly popular mode of travel in cities owing to the great advantages that it offers in terms of space consumption, health and environmental sustainability, and it is therefore favoured and promoted by many city authorities worldwide. A large number of recently introduced cycling-related schemes in many cities demonstrates this trend. However, the relatively low safety of pedal cycles as perceived by the users currently presents itself as a hurdle, and therefore cycling has yet to be adopted to a wider extent by users as a true alternative to the private car. Rising accident numbers, unfortunately, confirm this perception as reality, with a particular source of hazard appearing to originate from the interaction of cyclists with motorised traffic at low speeds in urban areas. Technological advances in recent years have resulted in a number of attempts to develop systems to prevent cyclist-vehicle collisions, but they have generally stumbled upon the challenge of accurate cyclist localisation and tracking, which can enable predicting a collision within a short-term time-horizon (5-10 seconds). Indeed, cyclist positioning accuracy is essential for any collision avoidance system, not only to ensure the effective operation of the system but also to minimise the occurrence of false alerts. Thus, motivated by the poor safety record, the research reported here involves the development and testing of an innovative technological solution for accurately localising and tracking cyclists, where the ultimate aim is to utilise the techniques in a concept called Cyclist 360° Alert to avoid collisions. The overarching innovation of this PhD is the development of the instrumented bicycle system, called iBike, which can be employed to track cyclists’ positions more precisely. The system relies on bicycles being instrumented with low-cost Micro-electromechanical systems (MEMS) sensors, and utilises multiple Kalman filters, which were developed from the geometrical and kinematics modelling of the bicycles, to conduct a multi-sensor fusion on the iBike acquisition data with the measurements from the Global Positioning System, Wi-Fi hotspots and mobile communication systems. Apart from the above, the thesis also reports on the results obtained from a number of field trials where an enhanced off-the-shelf positioning system was employed to validate the developed system. The overall results from the field experiments demonstrate that, on average with an 80% probability, the iBike system can be used to estimate a position with less than 0.5 m error compared to a 16.2 m error from the enhanced positioning system under the same circumstances. Thus, the results from the field trials using the iBike have shown successful outcomes for the developed methodologies. This means that the iBike can be used to predict a collision more precisely. These results are presented in detail together with the hardware and software of the iBike system in this thesis

Performance assessment for mountain bike based on WSN and Cloud Technologies

The mountain bike is one of the most used equipment’s in outdoor sports activities. The thesis describes the design and all development and implementation of Performance Assessment for Mountain Bike based on Wireless Sensor Network (WSN) and Cloud Technologies. The work presents a distributed sensing system for cycling assessment-providing data for objective evaluation of the athlete performance during training. Thus a wireless sensor network attached to the sport equipment provides to the athlete and the coach with performance values during practice. The sensors placed in biker equipment’s behave as nodes of a WSN. This is possible with the developing of IoT-based systems in sports, the tracking and monitoring of athletes in their activities has an important role on his formation as bikers and helps to increase performance, through the analyze of each session. The implemented system performs acquisition, processing and transmission, of data using a ZigBee wireless networks that provide also machine-to-machine communication and data storage in a server located in the cloud. As in many cycling applications use the phone as a module to get the values, this work will be a little different making use of phone/tablet to consult information. The information stored on the cloud server is accessed through a mobile application that analyses and correlates all metrics calculated using the training data obtained during practice. Additional information regarding the health status may be also considered. Therefore, the system permits that athletes perform an unlimited number of trainings that can be accessed at any time through the mobile application by the bikers and coach. Based on capability of the system to save a history of the evolution of each athlete during training the system permits to perform appropriate comparisons between different training sessions and different athlete’s performances.A bicicleta de montanha é um dos equipamentos para desportos no exterior mais usada. A tese descreve todo o desenho, desenvolvimento e implementação de Performance Assessment for Mountain Bike based on WSN and Cloud Technologies. Este apresenta um sistema de deteção distribuída para o aumento do desempenho, melhorar a metodologia da prática do ciclismo e para formação de atletas. Para tal foi desenvolvida e anexada uma rede de sensores que está embutida no equipamento do ciclista, através desta rede de sensores sem fios são obtidos os valores respetivos à interação do utilizador e a sua bicicleta, sendo estes apresentados ao treinador e ao próprio ciclista. Os sensores colocados comportam-se como nós de uma rede de sensores sem fios. Isso é possível com o desenvolvimento de sistemas baseados na Internet das coisas no desporto, a observação da movimentação e monitoramento de atletas nas suas atividades tem um papel importante na sua formação como ciclistas e ajuda a aumentar o desempenho. O sistema é baseado numa rede ZigBee sem fios, que permite a comunicação máquina-para-máquina e o armazenamento de dados num servidor localizado na nuvem. Toda a informação na nuvem pode ser acedida através de uma aplicação mobile que analisa e correlaciona todos os valores calculados usando os dados recolhidos durante o treino efetuado por cada ciclista. Como em muitas aplicações de ciclismo estas usam o telefone como um módulo para obter os valores, neste trabalho o caso é diferente fazendo o uso do telefone/tablet para apenas consultar as informações. Alguma informação sobre o ciclista é fornecida para poder efetuar alguns cálculos, relativos à saúde do ciclista, neste caso toda a energia gasta na prática de um determinado treino. Toda esta informação pode ser acedida através de uma aplicação Android e por consequência num dispositivo Android. Com a aplicação desenvolvida é possível observar e processar toda a informação recolhida através dos sensores implementados, a observação dos dados recolhidos pode ser efetuada pelo treinador responsável, como pelo próprio atleta. Portanto, o sistema permite a realização de um ilimitado número de sessões de treino, estes podem ser consultados a qualquer momento através da aplicação móvel. Fazendo com que seja possível manter um histórico da evolução de cada atleta, podendo assim observar e comparar cada sessão de treino, realizada por cada atleta

IMM-Based lane-change prediction in highways with low-cost GPS/INS

The prediction of lane changes has been proven to be useful for collision avoidance support in road vehicles. This paper proposes an interactive multiple model (IMM)-based method for predicting lane changes in highways. The sensor unit consists of a set of low-cost Global Positioning System/inertial measurement unit (GPS/IMU) sensors and an odometry captor for collecting velocity measurements. Extended Kalman filters (EKFs) running in parallel and integrated by an IMM-based algorithm provide positioning and maneuver predictions to the user. The maneuver states Change Lane (CL) and Keep Lane (KL) are defined by two models that describe different dynamics. Different model sets have been studied to meet the needs of the IMM-based algorithm. Real trials in highway scenarios show the capability of the system to predict lane changes in straight and curved road stretches with very short latency times.Ministerio de Fomento: FOM/2454/200

The Impact of Telemetry Received Signal Strength of IMU/GNSS Data Transmission on Autonomous Vehicle Navigation

This paper presents the effect of received signal strength on IMU/GNSS sensor data transmission for autonomous vehicle navigation. A pixhawk 2.1 flight controller is used to build the navigation system. Straight lines with back-and-forth routes were tested using two types of SiK telemetry: Holybro and RFD. The results of the tests show that when the RSSI value falls close to the receiver's sensitivity value, the readings of the gyro sensor data, accelerometer, magnetometer, and GNSS compass data are disturbed. When the RSSI signal collides with noise, the radio telemetry link is lost, affecting the accuracy of speed data and the orientation of autonomous vehicles. According to Cisco's conversion table, the highest RSSI on Holybro telemetry is -48 dBm, and the lowest is -103 dBm, with a receiver sensitivity of -117 and data reading at a distance of about 427 meters. While the highest RSSI value on RFD telemetry is -17 dBm and the lowest is -113 dBm, even the lowest value is above the receiver's sensitivity limit of -121 dBm with data readings at a distance of approximately 749.4 meters. RFD outperforms Holybro in terms of RSSI and sensitivity at low data rates. When reading distance data to reference distance data using Google Earth and ArcGIS, RFD telemetry has a higher accuracy, with an average accuracy of 98.8%

Doctor of Philosophy

dissertationThe need for position and orientation information in a wide variety of applications has led to the development of equally varied methods for providing it. Amongst the alternatives, inertial navigation is a solution that o ffers self-contained operation and provides angular rate, orientation, acceleration, velocity, and position information. Until recently, the size, cost, and weight of inertial sensors has limited their use to vehicles with relatively large payload capacities and instrumentation budgets. However, the development of microelectromechanical system (MEMS) inertial sensors now o ers the possibility of using inertial measurement in smaller, even human-scale, applications. Though much progress has been made toward this goal, there are still many obstacles. While operating independently from any outside reference, inertial measurement su ers from unbounded errors that grow at rates up to cubic in time. Since the reduced size and cost of these new miniaturized sensors comes at the expense of accuracy and stability, the problem of error accumulation becomes more acute. Nevertheless, researchers have demonstrated that useful results can be obtained in real-world applications. The research presented herein provides several contributions to the development of human-scale inertial navigation. A calibration technique allowing complex sensor models to be identified using inexpensive hardware and linear solution techniques has been developed. This is shown to provide significant improvements in the accuracy of the calibrated outputs from MEMS inertial sensors. Error correction algorithms based on easily identifiable characteristics of the sensor outputs have also been developed. These are demonstrated in both one- and three-dimensional navigation. The results show significant improvements in the levels of accuracy that can be obtained using these inexpensive sensors. The algorithms also eliminate empirical, application-specific simplifications and heuristics, upon which many existing techniques have depended, and make inertial navigation a more viable solution for tracking the motion around us