Exploring Fuzzy Logic and Random Forest for Car Drivers’ Fuel Consumption Estimation in IoT-Enabled Serious Games

Internet of Things (IoT) technologies have a promising potential for instructional serious games related to field operations. We explore IoT’s potential for serious games in the automotive application domain to improve driving, choosing fuel consumption (FC) as an indicator of the driver performance as it is strongly influenced by driving styles and can be quantified and validated. We propose a FC prediction model, exploiting three vehicular signals that are controllable by the driver (player), that are able to provide direct coaching feedback to the driver and are easily available through the widely available On-Board Diagnostic-II (OBD-II) vehicular interface: throttle position, engine rotation speed (RPM) and car speed. We processed the data with two techniques, random forest (RF) and fuzzy logic (FL). Implementation, training and testing of both models, were made using the enviroCar database which freely provides a significant amount of naturalistic drive data. Results show that RF achieves quite a higher estimation accuracy, which complements FL’s ability to provide driver with easily understandable feedback. We thus argue that the combination of the two models can supply valuable information usable by game designers in the automotive environment

Application of Real Field Connected Vehicle Data for Aggressive Driving Identification on Horizontal Curves

The emerging technology of connected vehicles generates a vast amount of data that could be used to enhance roadway safety. In this paper, we focused on safety applications of a real field connected vehicle data on a horizontal curve. The database contains connected vehicle data that were collected on public roads in Ann Arbor, Michigan with instrumented vehicles. Horizontal curve negotiations are associated with a great number of accidents, which are mainly attributed to driving errors. Aggressive/risky driving is a contributing factor to the high rate of crashes on horizontal curves. Using basic safety message data in connected vehicle data set, this paper modeled aggressive/risky driving while negotiating a horizontal curve. The model was developed using the machine learning method of random forest to classify the value of time to lane crossing (TLC), a proxy for aggressive/risky driving, based on a set of motion-related metrics as features. Three scenarios were investigated considering different TLCs value for tagging aggressive driving moments. The model contributed to high detection accuracy in all three scenarios. This suggests that the motion-related variables used in the random forest model can accurately reflect drivers\u27 instantaneous decisions and identify their aggressive driving behavior. The results of this paper inform the design of warning/feedback systems and control assistance from unsafe events which are transmittable through vehicles-to-vehicles and vehicles-to-infrastructure applications

A Fuzzy Logic Module to Estimate a Driver’s Fuel Consumption for Reality-Enhanced Serious Games

Reality-enhanced gaming is an emerging serious game genre, that could contextualize a game within its real instruction-target environment. A key module for such games is the evaluator, that senses a user performance and provides consequent input to the game. In this project, we have explored an application in the automotive field, estimating driver performance in terms of fuel consumption, based on three key vehicular signals, that are directly controllable by the driver: throttle position sensor (TPS), engine rotation speed (RPM) and car speed. We focused on Fuzzy Logic, given its ability to embody expert knowledge and deal with incomplete information availability. The fuzzy models – that we iteratively defined based on literature expertise and data analysis – can be easily plugged into a reality-enhanced gaming architecture. We studied four models with all the possible combinations of the chosen variables (TPS and RPM; RPM and speed; TPS and speed; TPS, speed and RPM). Input data were taken from the enviroCar database, and our fuel consumption predictions compared with their estimated values. Results indicate that the model with the three inputs outperforms the other models giving a higher coefficient of determination (R2), and lower error. Our study also shows that RPM is the most important fuel consumption predictor, followed by TPS and speed

Eco-friendly Naturalistic Vehicular Sensing and Driving Behaviour Profiling

PhD ThesisInternet of Things (IoT) technologies are spurring of serious games that support training directly in the field. This PhD implements field user performance evaluators usable in reality-enhanced serious games (RESGs) for promoting fuel-efficient driving. This work proposes two modules – that have been implemented by processing information related to fuel-efficient driving – to be employed as real-time virtual sensors in RESGS. The first module estimates and assesses instantly fuel consumption, where I compared the performance of three configured machine learning algorithms, support vector regression, random forest and artificial neural networks. The experiments show that the algorithms have similar performance and random forest slightly outperforms the others. The second module provides instant recommendations using fuzzy logic when inefficient driving patterns are detected. For the game design, I resorted to the on-board diagnostics II standard interface to diagnostic circulating information on vehicular buses for a wide diffusion of a game, avoiding sticking to manufacturer proprietary solutions. The approach has been implemented and tested with data from the enviroCar server site. The data is not calibrated for a specific car model and is recorded in different driving environments, which made the work challenging and robust for real-world conditions. The proposed approach to virtual sensor design is general and thus applicable to various application domains other than fuel-efficient driving. An important word of caution concerns users’ privacy, as the modules rely on sensitive data, and provide information that by no means should be misused

A multi-factor model for range estimation in electric vehicles

Electric vehicles (EVs) are well-known for their challenges related to trip planning and energy consumption estimation. Range anxiety is currently a barrier to the adoption of EVs. One of the issues influencing range anxiety is the inaccuracy of the remaining driving range (RDR) estimate in on-board displays. RDR displays are important as they can help drivers with trip planning. The RDR is a parameter that changes under environmental and behavioural conditions. Several factors (for example, weather, and traffic) can influence the energy consumption of an EV that are not considered during the RDR estimation in traditional on-board computers or third-party applications, such as navigation or mapping applications. The need for accurate RDR estimation is growing, since this can reduce the range anxiety of drivers. One way of overcoming range anxiety is to provide trip planning applications that provide accurate estimations of the RDR, based on various factors, and which adapt to the users’ driving behaviour. Existing models used for estimating the RDR are often simplified, and do not consider all the factors that can influence it. Collecting data for each factor also presents several challenges. Powerful computing resources are required to collect, transform, and analyse the disparate datasets that are required for each factor. The aim of this research was to design a Multi-factor Model for range estimation in EVs. Five main factors that influence the energy consumption of EVs were identified from literature, namely, Route and Terrain, Driving Behaviour, Weather and Environment, Vehicle Modelling, and Battery Modelling. These factors were used throughout this research to guide the data collection and analysis processes. A Multi-factor Model was proposed based on four main components that collect, process, analyse, and visualise data from available data sources to produce estimates relating to trip planning. A proof-of-concept RDR system was developed and evaluated in field experiments, to demonstrate that the Multi-factor Model addresses the main aim of this research. The experiments were performed to collect data for each of the five factors, and to analyse their impact on energy consumption. Several machine learning techniques were used, and evaluated, for accuracy in estimating the energy consumption, from which the RDR can be derived, for a specified trip. A case study was conducted with an electric mobility programme (uYilo) in Port Elizabeth, South Africa (SA). The case study was used to investigate whether the available resources at uYilo were sufficient to provide data for each of the five factors. Several challenges were noted during the data collection. These were shortages of software applications, a lack of quality data, technical interoperability and data access between the data collection instruments and systems. Data access was a problem in some cases, since proprietary systems restrict access to external developers. The theoretical contribution of this research is a list of factors that influence RDR and a classification of machine learning techniques that can be used to estimate the RDR. The practical contributions of this research include a database of EV trips, proof-of-concept RDR estimation system, and a deployed machine learning model that can be accessed by researchers and EV practitioners. Four research papers were published and presented at local and international conferences. In addition, one conference paper was published in an accredited journal: NextComp 2017 (Appendix C), Conference Paper, Pointe aux Piments (Mauritius); SATNAC 2017 (Appendix F), Conference Paper, Barcelona (Spain); GITMA 2018 (Appendix B), Conference Paper, Mexico City (Mexico); SATNAC 2018 (Appendix G), Conference Paper, George (South Africa), and IFIP World Computer Congress 2018 (Appendix E), Journal Article