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.

中文翻译：

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用于评估增强现实游戏的驾驶员油耗的模糊逻辑模块

增强现实的游戏是一种新兴的严肃游戏类型，可以在其真实的指令目标环境内将游戏情境化。此类游戏的关键模块是评估器，它可以感知用户的表现并向游戏提供相应的输入。在这个项目中，我们探索了在汽车领域的应用，基于三个直接由驾驶员控制的关键车辆信号，根据燃油消耗来估算驾驶员的性能：节气门位置传感器（TPS），发动机转速（RPM） ）和汽车速度。由于模糊逻辑具有体现专家知识和处理不完全信息可用性的能力，因此我们专注于模糊逻辑。我们可以根据文献专业知识和数据分析反复定义的模糊模型，可以轻松地插入到增强现实的游戏架构中。我们用所选变量的所有可能组合（TPS和RPM； RPM和速度； TPS和速度； TPS，速度和RPM）研究了四个模型。输入数据来自enviroCar数据库，我们的油耗预测与其估计值进行了比较。结果表明，具有三个输入的模型优于其他模型，具有更高的确定系数（R2）和更低的误差。我们的研究还表明，RPM是最重要的燃料消耗量预测指标，其次是TPS和速度。结果表明，具有三个输入的模型优于其他模型，具有更高的确定系数（R2）和更低的误差。我们的研究还表明，RPM是最重要的燃料消耗量预测指标，其次是TPS和速度。结果表明，具有三个输入的模型优于其他模型，具有更高的确定系数（R2）和更低的误差。我们的研究还表明，RPM是最重要的燃料消耗量预测指标，其次是TPS和速度。