A novel Big Data analytics and intelligent technique to predict driver's intent

Modern age offers a great potential for automatically predicting the driver's intent through the increasing miniaturization of computing technologies, rapid advancements in communication technologies and continuous connectivity of heterogeneous smart objects. Inside the cabin and engine of modern cars, dedicated computer systems need to possess the ability to exploit the wealth of information generated by heterogeneous data sources with different contextual and conceptual representations. Processing and utilizing this diverse and voluminous data, involves many challenges concerning the design of the computational technique used to perform this task. In this paper, we investigate the various data sources available in the car and the surrounding environment, which can be utilized as inputs in order to predict driver's intent and behavior. As part of investigating these potential data sources, we conducted experiments on e-calendars for a large number of employees, and have reviewed a number of available geo referencing systems. Through the results of a statistical analysis and by computing location recognition accuracy results, we explored in detail the potential utilization of calendar location data to detect the driver's intentions. In order to exploit the numerous diverse data inputs available in modern vehicles, we investigate the suitability of different Computational Intelligence (CI) techniques, and propose a novel fuzzy computational modelling methodology. Finally, we outline the impact of applying advanced CI and Big Data analytics techniques in modern vehicles on the driver and society in general, and discuss ethical and legal issues arising from the deployment of intelligent self-learning cars

A novel framework to promote eco-driving through smartphone-vehicle integration

Tesis por compendioIt was not that long ago, just in the first half on the 1990s, when mobile phones were first introduced, being big and expensive. All you could do with them was to make phone calls. Since then mobile devices have experienced a great technological advance: we carry smartphones in our pockets that provide Internet access, having accelerometers that can measure acceleration, a gyroscope that can provide orientation information, different wireless interfaces such as Bluetooth connections, and above all, great computing power. On the other hand, the automobile industry has evolved significantly during the last 10 years. One of the most exciting advances in vehicle development is vehicle-to-vehicle V2V communication, which allows cars to communicate with each other over a dedicated Wi-Fi band, and share information about vehicle speed, route direction, traffic flow, and road and weather conditions. An example of such a system is GM's (General Motors) OnStar, introduced in 1996, and that provides automatic response in case of an accident, stolen-vehicle recovery, remote door unlock, and vehicle diagnostics. Also, the standard On Board Diagnosis (OBD-II), available for several years, allows us to connect to the Electronic Control Unit (ECU) via a Bluetooth OBD-II connector. This connection interface allows connectivity between the smartphone and the vehicle, and can be purchased for just over 15 euros. The spectrum of possibilities that arise when combining the car and the smartphone is unlimited, such as performing the diagnosis of the car by assuming the tasks performed by the car's On Board Unit (OBU), or sending the collected data to a platform where the diagnosis or maintenance of the system can be realized in order to detect possible faults, help you to save gas and reduce environment pollution, and notify you of your car's problems, among other features. The general objective pursued with this doctoral thesis is to help drivers to correct bad habits in their driving. To achieve this we promote the combination between smartphones and vehicular networks to design and develop a platform able to offer useful tips to achieve safer driving and greater fuel economy. It is well-known that intelligent driving can lead to lower fuel consumption, with the consequent positive impact on the environment. The proposal that has been carried out in this doctoral thesis begins with the data capture from the vehicles' OBD-II port and data analysis through the use of graphs, maps, and statistics, both, on the server itself and in the smartphone's application developed. We applied data mining techniques and neural networks to analyze, study and generate a classiffication on driving styles based on the analysis of the characteristics of each specific route used for testing. In a second phase, we demostrate the relationship between fuel consumption and driving style. To achieve that goal, the first thing that we had to realize was how to apply different algorithms for the instantaneous consumption calculation (this parameter cannot be obtained directly from the vehicle ECU). Later, we studied and analyzed all data that was collected from the drivers who shared their monitored data with the server. Although drivers do not recognize themselves as being in a state of anxiety while driving, they are more stressed than in any other daily activity, for example, when trying to stay in the right lane, keeping the car at a certain speed, and starting and stopping the vehicle. In general, drivers are more concentrated than they think, which causes an increase in the heart rate. Many factors influence heart rate while at rest, e.g. stress, medications, medical conditions, even genes play a role. In our study we also investigate how stress and the driving behavior influence the heart rate. So, in the last phase, we demostrate the correlation between heart rate and driving style, showing how the driving style can make the heart rate vary by 3 %.No hace mucho tiempo, tan sólo en la primera mitad en la década de los 90, cuando los teléfonos móviles aparecieron, eran grandes y caros, todo lo que se podía hacer con ellos era realizar llamadas telefónicas. Desde entonces los dispositivos móviles han experimentado un gran avance tecnológico, llevamos teléfonos inteligentes en el bolsillo con acceso a Internet, acelerómetros que calculan la aceleración instantánea, giroscopios que proporcionan información de orientación, diferentes conexiones inalámbricas como Bluetooth, y sobre todo, gran capacidad de computación. Por otro lado, la industria del automóvil ha evolucionado mucho durante los últimos 10 años. Uno de los avances más interesantes en el desarrollo de vehículos ha sido la conectividad, V2V, o comunicación vehículo a vehículo, permite a los automóviles comunicarse mediante Wi-Fi y compartir información sobre la velocidad del vehículo, la dirección de la ruta actual, el tráfico, así como las condiciones de la carretera y las condiciones ambientales. Por otra parte, el estándar On Board Diagnosis (OBD-II), disponible desde hace varios años, permite conectarnos de forma sencilla a la ECU (Electronic Control Unit) mediante un conector Bluetooth OBD-II. Este interfaz de conexión permite la conectividad entre el dispositivo móvil y el vehículo, se puede adquirir por poco más de 15 euros. El espectro de posibilidades que surgen al combinar el automóvil y el Smartphone es amplísimo, como por ejemplo realizar el diagnóstico del coche a través del móvil asumiendo las tareas que hace la unidad On Board Unit (OBU) del coche, o bien enviar los datos recogidos a una plataforma donde se pueda realizar el diagnóstico o mantenimiento del sistema, detectando posibles fallos puede ayudar a ahorrar en el consumo de combustible, notificar los problemas del coche en tiempo real, entre otras características. El objetivo general que se persigue con esta tesis doctoral es ayudar al conductor a corregir malos hábitos en su forma de conducción. Conseguimos esto mediante la combinación entre smartphones y las redes vehiculares, diseñamos y desarrollamos una plataforma capaz de ofrecer consejos útiles para conseguir una conducción más segura y un mayor ahorro de combustible. Es conocido que una conducción inteligente puede llevarnos a un menor consumo de combustible, con el consiguiente impacto positivo que ello conlleva sobre el medio ambiente. La propuesta que se ha llevado a cabo en esta tesis doctoral comienza con la obtención de los datos desde el OBD-II del coche y su presentación y análisis mediante el uso de gráficas, mapas, estadísticas, tanto en el propio servidor como en la aplicación móvil desarrollada para la obtención de datos recibidos desde la ECU. Se aplicaron técnicas de minería de datos y redes neuronales para analizar, estudiar y generar una clasificación sobre los estilos de conducción en base al análisis de las características de la vía sobre la que ha realizado la ruta. En una segunda fase se demostró la relación entre el consumo de combustible con el estilo de conducción, para ello lo primero que tuvimos que realizar fue aplicar diversos algoritmos para el cálculo del consumo instantáneo, este parámetro no es posible obtenerlo directamente de la ECU del vehículo. Posteriormente se realizó el estudio y el análisis de todos los datos que se recogieron de los conductores que se prestaron a la realización del estudio enviando los datos al servidor. Muchos factores influyen en la frecuencia cardíaca en reposo, por ejemplo, el estrés, los medicamentos, las condiciones médicas, incluso los genes tienen su influencia, el envejecimiento tiende a acelerarlo, y el ejercicio regular tiende a ralentizarlo. En nuestro estudio también investigamos cómo el estrés y el comportamiento en la conducción influyen en la frecuencia cardíaca. En la última fase vemos la correlación existente entre el riNo fa molt de temps, tan sols en la primera mitat en la dècada dels 90, quan els telèfons mòbils van aparéixer, eren grans i cars, tot el que es podia fer amb ells era realitzar telefonades. Des de llavors els dispositius mòbils han experimentat un gran avanç tecnològic, portem telèfons intel_ligents en la butxaca amb accés a Internet, acceleròmetres que calculen l'acceleració instantània, giroscopis que proporcionen informació d'orientació, diferents connexions sense _ls com Bluetooth, i sobretot gran capacitat de computació. D'altra banda, la indústria de l'automòbil ha evolucionat molt durant els últims 10 anys. Un dels avanços més interessants en el desenrotllament de vehicles ha sigut la connectivitat, V2V, o comunicació vehicle a vehicle, permet als automòbils comunicar-se per mitjà de la banda de Wi-Fi i compartir información sobre la velocitat del vehicle, la direcció de la ruta actual, les condicions del trà_c, així com l'estat de la carretera i les condicions ambientals. D'altra banda l'estàndard On Board Diagnosi (OBD-II), disponible des de fa diversos anys, permet connectar-nos de forma senzilla a l'ECU (Electronic Control Unit) per mitjà d'un connector Bluetooth OBD-II. Esta interfície de connexió permet la connectivitat entre el dispositiu mòbil i el vehicle, es pot adquirir per poc més de 15 euros. L'espectre de possibilitats que sorgixen al combinar l'automòbil i el Smartphone és il_limitat, com per exemple realitzar el diagnòstic del cotxe a través del móvil assumint les tasques que fa la unitat On Board Unit (OBU) del cotxe, o bé enviar les dades arreplegades a una plataforma on es puga realitzar el diagnòstic o manteniment del sistema, detectant possibles fallades, ajuda a estalviar en el consum de combustible, noti_car els problemes del cotxe en temps real, entre altres característiques. L'objectiu general que es perseguix amb esta tesi doctoral és ajudar al conductor a corregir mals hàbits en la seua forma de conducció. Aconseguim açò mitjançant de la combinació entre smartphones i les xarxes vehiculares, dissenyem i desenrotllem una plataforma capaç d'oferir consells útils per a aconseguir una conducció més segura i un major estalvi de combustible. És conegut que una conducció intel_ligent pot emportar-nos a un menor consum de combustible, amb el consegüent impacte positiu que això comporta sobre el medi ambient. La proposta que s'ha dut a terme en esta tesi doctoral comença amb l'obtenció de les dades des de l'OBD-II del cotxe i la seua presentació i anàlisi per mitjà de l'ús de grà_ques, mapes, estadístiques, tant en el propi servidor, com en l'aplicació mòbil desenrotllada per a l'obtenció de dades rebudes des de l'ECU. S'apliquen tècniques de mineria de dades i xarxes neuronals per a analitzar, estudiar i generar una classi_cació sobre els estils de conducció basant-se en l'anàlisi de les característiques de la via sobre la qual ha realitzat la ruta. En una segona fase es va a demostrar la relació entre el consum de combustible amb l'estil de conducció, per a això la primera cosa que vam haver de realizar va ser aplicar diversos algorismes per al càlcul del consum instantani, este paràmetre no és possible obtindre-ho directament de l'ECU del vehicle. Posteriorment es va realitzar l'estudi i l'anàlisi de totes les dades que es van arreplegar dels conductors que es van prestar a la realització de l'estudi enviant les dades al servidor. Molts factors in_ueixen en la freqüència cardíaca en repòs, per exemple, l'estrès, els medicaments, les condicions mèdiques, _ns i tot els gens tenen la seua in_uència, l'envelliment tendeix a accelerar-ho, i l'exercici regular tendeix a ralentir-ho. En el nostre estudi només estem interessats en com l'estrès i el comportament en la conducció in_ueixen en la freqüència cardíaca. En l'última fase vam veure la correlació existent entre el ritme cardíac i l'estil de conducciMeseguer Anastasio, JE. (2017). A novel framework to promote eco-driving through smartphone-vehicle integration [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/84287TESISCompendi

How machine learning informs ride-hailing services: A survey

In recent years, online ride-hailing services have emerged as an important component of urban transportation system, which not only provide significant ease for residents’ travel activities, but also shape new travel behavior and diversify urban mobility patterns. This study provides a thorough review of machine-learning-based methodologies for on-demand ride-hailing services. The importance of on-demand ride-hailing services in the spatio-temporal dynamics of urban traffic is first highlighted, with machine-learning-based macro-level ride-hailing research demonstrating its value in guiding the design, planning, operation, and control of urban intelligent transportation systems. Then, the research on travel behavior from the perspective of individual mobility patterns, including carpooling behavior and modal choice behavior, is summarized. In addition, existing studies on order matching and vehicle dispatching strategies, which are among the most important components of on-line ride-hailing systems, are collected and summarized. Finally, some of the critical challenges and opportunities in ride-hailing services are discussed

Context-Aware Driver Distraction Severity Classification using LSTM Network

Advanced Driving Assistance Systems (ADAS) has been a critical component in vehicles and vital to the safety of vehicle drivers and public road transportation systems. In this paper, we present a deep learning technique that classifies drivers’ distraction behaviour using three contextual awareness parameters: speed, manoeuver and event type. Using a video coding taxonomy, we study drivers’ distractions based on events information from Regions of Interest (RoI) such as hand gestures, facial orientation and eye gaze estimation. Furthermore, a novel probabilistic (Bayesian) model based on the Long shortterm memory (LSTM) network is developed for classifying driver’s distraction severity. This paper also proposes the use of frame-based contextual data from the multi-view TeleFOT naturalistic driving study (NDS) data monitoring to classify the severity of driver distractions. Our proposed methodology entails recurrent deep neural network layers trained to predict driver distraction severity from time series data

Integrated Vehicular System with Black Box Capability and Intelligent Driving Diagnosis

Hoy en día, una de las causas de las altas tasas de mortalidad en el mundo son los accidentes de tránsito. Según la Organización Mundial de la Salud (OMS), los accidentes de tránsito alcanzan más de 1.3 millones de víctimas anuales en el mundo; y sólo en Colombia más de 5000 víctimas al año. Por esta razón, esta investigación presenta el desarrollo de un “Agente para el Diagnóstico Inteligente de Conducción”, implementado mediante un algoritmo de Lógica Difusa. Con la aproximación computacional del conocimiento experto en conducción vehicular, este trabajo permite realizar el diagnóstico de las maniobras del conductor de manera que se pueda determinar si son riesgosas o si no lo son. Los experimentos fueron realizados bajo condiciones reales de “conducción segura” en la ciudad de Barranquilla. Los resultados muestran que se puede lograr un diagnóstico inteligente de conducción gracias al “Agente para el Diagnóstico Inteligente de Conducción” propuesto

Ensuring Safe and Robust Human-Machine Interaction in Autonomous Electric Vehicles: State-of-the-Art Techniques

Autonomous electric vehicles (AEVs) are gaining popularity due to their potential to reduce accidents caused by human error and decrease carbon emissions. However, ensuring safe and robust human-machine interaction in AEVs remains a significant challenge. To address this challenge, we reviewed several state-of-the-art techniques currently being developed and implemented. Our findings show that AEVs rely on a range of sensors and perception systems, including cameras, lidars, radars, and GPS, to detect and respond to their environment. Advanced perception algorithms and machine learning techniques are used to process the data collected by these sensors and provide real-time information about the vehicle's surroundings. The human-machine interface (HMI) is the primary means of interaction between the vehicle and the passenger, and it should be designed to be intuitive, informative, and easy to use. Artificial intelligence and machine learning algorithms are used to make decisions and adapt to changing road conditions. Cybersecurity measures, such as encryption, authentication, and intrusion detection, are essential to prevent cyberattacks on AEVs. Redundancy and fail-safe systems, including redundant sensors, processors, communication systems, backup power sources, and emergency braking systems, ensure that AEVs can continue to operate safely in the event of a failure or malfunction. Finally, rigorous testing and validation are necessary to ensure that AEVs meet safety standards and perform as intended. Our review provides valuable insights into the state-of-the-art techniques for ensuring robust and safe human-machine interaction in AEVs, which can guide future research and development in this area

Human-Centric Detection and Mitigation Approach for Various Levels of Cell Phone-Based Driver Distractions

abstract: Driving a vehicle is a complex task that typically requires several physical interactions and mental tasks. Inattentive driving takes a driver’s attention away from the primary task of driving, which can endanger the safety of driver, passenger(s), as well as pedestrians. According to several traffic safety administration organizations, distracted and inattentive driving are the primary causes of vehicle crashes or near crashes. In this research, a novel approach to detect and mitigate various levels of driving distractions is proposed. This novel approach consists of two main phases: i.) Proposing a system to detect various levels of driver distractions (low, medium, and high) using a machine learning techniques. ii.) Mitigating the effects of driver distractions through the integration of the distracted driving detection algorithm and the existing vehicle safety systems. In phase- 1, vehicle data were collected from an advanced driving simulator and a visual based sensor (webcam) for face monitoring. In addition, data were processed using a machine learning algorithm and a head pose analysis package in MATLAB. Then the model was trained and validated to detect different human operator distraction levels. In phase 2, the detected level of distraction, time to collision (TTC), lane position (LP), and steering entropy (SE) were used as an input to feed the vehicle safety controller that provides an appropriate action to maintain and/or mitigate vehicle safety status. The integrated detection algorithm and vehicle safety controller were then prototyped using MATLAB/SIMULINK for validation. A complete vehicle power train model including the driver’s interaction was replicated, and the outcome from the detection algorithm was fed into the vehicle safety controller. The results show that the vehicle safety system controller reacted and mitigated the vehicle safety status-in closed loop real-time fashion. The simulation results show that the proposed approach is efficient, accurate, and adaptable to dynamic changes resulting from the driver, as well as the vehicle system. This novel approach was applied in order to mitigate the impact of visual and cognitive distractions on the driver performance.Dissertation/ThesisDoctoral Dissertation Applied Psychology 201

Driving style recognition for intelligent vehicle control and advanced driver assistance: a survey

Driver driving style plays an important role in vehicle energy management as well as driving safety. Furthermore, it is key for advance driver assistance systems development, toward increasing levels of vehicle automation. This fact has motivated numerous research and development efforts on driving style identification and classification. This paper provides a survey on driving style characterization and recognition revising a variety of algorithms, with particular emphasis on machine learning approaches based on current and future trends. Applications of driving style recognition to intelligent vehicle controls are also briefly discussed, including experts' predictions of the future development

Learning from the Offline Trace: A Case Study of the Taxi Industry

The growth of mobile and sensor technologies today leads to the digitization of individual\u27s offline behavior. Such large-scale and fine-grained information can help better understand individual decision making. We instantiate our research by analyzing the digitized taxi trails to study the impact of information on driver behavior and economic outcome. We propose homogeneous and heterogeneous Bayesian learning models and validate them using a unique data set containing complete information on 10.6M trip records from 11,196 taxis in a large Asian city in 2009. We find strong heterogeneity in individual learning behavior and driving decisions, which significantly associate with individual economic outcome. Interestingly, our policy simulations indicate information that is noisy at individual level can become most valuable after being aggregated across various spatial and temporal dimensions. Overall, our work demonstrates the potential of analyzing the digitized offline behavioral trace to infer demand as well as to improve individual decision efficiency

Cyclist-aware intelligent transportation system

Abstract. Rapidly developing cities make cycling popular way of traveling around and with enhanced smart traffic light infrastructure cycling can be safer and smoother. Smartphones with an internet connectivity and advanced positioning sensors can be used to build a cost-effective infrastructure to enable cyclist-aware traffic lights system. However, such systems depends on proper time of arrival estimation which can be affected by the GPS errors which works poorly in area with tall buildings and driver behaviour. In this paper we discuss how presence of feedback from smart traffic system influence the driver awareness of the cyclist and affects the negative impact of time of arrival estimation errors. This paper gives an analysis of the existing approaches to build smart cyclist-aware traffic systems and different sources of errors that affects their performance. With designed computer appliance we evaluated the effectiveness of cyclist-aware system with and without a presence of additional haptic and audio feedback. The results show that the presence of feedback positively affects the driver awareness of cyclist and allow them to react earlier. Experiment shows that just introduction of feedback can increase the accuracy of time of arrival estimation up to 34% without any other modification to the system.Pyöräilijät tiedostava älykäs liikennejärjestelmä. Tiivistelmä. Pyöräily on suosittu tapa liikkua nopeasti kasvavissa kaupungeissa. Parannetuilla älyliikennevaloilla pyöräilystä voisi tulla turvallisempaa ja sujuvampaa. Huokean infrastruktuurin rakentamisessa pyöräilijät tiedostavaan liikennevalojärjestelmään voidaan hyödyntää älypuhelinten verkkoyhteyttä sekä pitkälle kehitettynyttä paikannusmahdollisuutta. Paikannuksen haasteena kuitenkin ovat epätarkkuus korkeiden rakennusten katveessa sekä pyöräilijöiden ja autoilijoiden käyttäytyminen. Kyseisen kaltainen järjestelmä vaatii toimivan kulunaika-arvioinnin, mikä on haastavaa GPS-paikannuksen epätarkkuuden vuoksi. Tässä julkaisussa keskustelemme siitä, kuinka älykkäästä liikennejärjestelmästä saatu palaute vaikuttaa autoilijoiden tiedostavuuteen ja sitä kautta saapumisaika-arvioiden epätarkkuuteen. Analysoimme olemassa olevia älykkäitä pyöräiljät tiedostavia liikennejärjestelmiä ja niihin vaikuttavia epätarkkuus- sekä virhelähteitä. Käytämme kehittämäämme tietokone ohjelmaa arvioimaan pyöräilijät tiedostavan järjestelmän tehokkuutta käyttäen koemuuttujina haptista ja auditiivista palautetta. Tulokset paljastavat, että saatu palaute vaikuttaa positiivisesti parantaen autoilijoiden reaktioaikaa sekä sitä kuinka he tiedostavat pyöräiljät. Kokeet osoittavat, että pelkästään esittelyn ja palautteen olemassaolo lisäävät saapumisaika-arvioiden tarkkuutta jopa 34%