# Combining a Universal OBD-II Module with Deep Learning to Develop an Eco-Driving Analysis System
## 2 Related Research  
The development of vehicle technology drives economic development in various countries but also creates severe mobile pollution sources [16]. For example, CO2 emissions cause the greenhouse effect; SO2 and NOx cause acid rain and pulmonary diseases [17]. The European Union has established regulations to reduce greenhouse gas emissions by 32% by 2030 [18]. Vehicle CO2 emissions, which originate from the burning of fossil fuels, are directly related to fuel consumption [19]. Accordingly, CO2 reduction is considered a global goal. Eco-driving, a novel vehicle driving technology, adopts intelligent and safe driving to cut fuel consumption and reduce greenhouse gas emissions, traffic accidents, and noise [20]. Studies have indicated that eco-driving reduces fuel consumption and emissions by 10-25% [21; 22; 23; 24; 25] and also reduces greenhouse gas emissions by 30% [26].  
Contemporary methods for promoting eco-driving consist of two types. First, holding education courses to improve driving behavior and methods [11]. In [12], an eco-driving course was held for drivers and modified according to drivers' feedback; fuel consumption was reduced by 4.8%. Second, employing driving assistance systems to promptly indicate or correct unsafe driving behavior. These systems are further divided into factory-installed products and aftermarket-installed products. Factory-installed systems, in which the engine parameters are monitored remotely, include General Motors' Onstar, BMW's Connected Drive, and Lexus's Link. In terms of the aftermarket-installed products, an OBD-II module is employed to acquire, analyze, and make suggestions based on the ECU parameters to reduce fuel consumption [27; 28; 29; 30]. [PERSON] et al. collected and evaluated vehicle speed, acceleration, engine speed, mass flow sensor (MAF), manifold absolute pressure (MAP), and intake air temperature (AIT) with an OBD-II module to determine driving styles and route types; with a focus on the relationship between driving behavior and fuel consumption, they reduced fuel consumption and greenhouse gas emissions [26]. [PERSON] et al. employed fuzzy logic to integrate OBD-II, a GPS, and climate data to provide driving advice and achieved fuel savings of 11.04% [25]. Therefore, this type of smart assistance (neural network) system integration is a crucial direction in eco-driving research.  
Studies have discussed implementing neural network assistance in eco-driving systems. Multiple frameworks of neural networks are available for selection based on the required data type and system demands. [PERSON] developed a regression model based on vehicle speed and acceleration; through using different regression coefficients under various driving conditions, the optimal model sum of squared errors (SSE) was achieved [31]. [PERSON] et al. used object detection to convert the external environment into processable data, which were then used as the input for a clustering model. Through connecting dynamic environmental data with data on vehicle operation signals for analysis, their model achieved a prediction accuracy of over 80% [32]. [PERSON] et al. used a back propagation neural network on smartphones and OBD-II devices to acquire data on driver behavior and fuel consumption and then explored their correlation [30]. [PERSON] et al. adopted Elman neural network-based energy management strategies (EMSs) to determine the fuel efficiency of optimized hybrid electric vehicles [33]. The aforementioned research indicates that neural networks have achieved outstanding performance as eco-driving analysis tools. Therefore, the present study referred to fuel consumption prediction models proposed by previous research and adjusted the parameters to obtain a model framework that could accurately predict fuel consumption.