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

Eco-Driving Systems for Connected Automated Vehicles: Multi-Objective Trajectory Optimization

This study aims to leverage advances in connected automated vehicle (CAV) technology to design an eco-driving and platooning system that can improve the fuel and operational efficiency of vehicles during freeway driving. Following a two-stage control logic, the proposed algorithm optimizes CAVs’ trajectories with three objectives: travel time minimization, fuel consumption minimization, and traffic safety improvement. The first stage, designed for CAV trajectory planning, is carried out with two optimization models. The second stage, for real-time control purposes, is developed to ensure the operational safety of CAVs. Based on extensive numerical simulations, the results have confirmed the effectiveness of the proposed framework both in mitigating freeway congestion and in reducing vehicles’ fuel consumption

A Mini Review of Peer-to-Peer (P2P) for Vehicular Communication

In recent times, peer-to-peer (P2P) has evolved, where it leverages the capability to scale compared to server-based networks. Consequently, P2P has appeared to be the future distributed systems in emerging several applications. P2P is actually a disruptive technology for setting up applications that scale to numerous concurrent individuals. Thus, in a P2P distributed system, individuals become themselves as peers through contributing, sharing, and managing the resources in a network. In this paper, P2P for vehicular communication is explored. A comprehensive of the functioning concept of both P2P along with vehicular communication is examined. In addition, the advantages are furthermore conversed for a far better understanding on the implementation

Review on Construction Procedures of Driving Cycles

The goal of this paper is to give an overview of the literature of construction techniques of driving cycles. Our motivation for the overview is the future goal of constructing our own driving cycles for various types of vehicles and routes. This activity is part of a larger project focusing on determination of fuel and energy consumption by dynamic simulation of vehicles. Accordingly, the papers dealing with sample route determination, data collection and processing, driving cycle construction procedures, statistical evaluation of data are in our focus