# Environmental-Based Speed Recommendation for Future Smart Cars +
## 3 Proposed Approach
### Overview  
Overall, the proposed methodology considers a vehicle as a system with \(N\) number of input sources and \(S=\{s_{1},s_{2},...,s_{N}\}\) is the set of the available sources. Each source \(s_{i}\) monitors and generates \(y_{i}=[v_{1},v_{2},\cdots v_{N}]\) values that form the initial set of data for the system: \(Y=[y_{1},y_{2},...,y_{N}]^{T}\). The next step is to rank and identify which inputs actually have an effect on the targeted metric. Data is filtered by a ranking function that selects the necessary portion of data for the specific application \(Y\xrightarrow{\mathcal{F}}\gamma^{\prime}\) and thus produces a new data set \(\gamma^{\prime}=[y^{{}^{\prime}}_{1},y^{{}^{\prime}}_{2},...,y^{{}^{\prime}}_{ N}]^{T}\). The purpose of this pruning procedure is to eliminate any redundant inputs in order to drop the dimensionality of the input matrix and achieve lower complexity of the design. After selecting the appropriate data, the next step is to extract the desired features. A function \(G(\cdot)\) gets as input the output \(y^{{}^{\prime}}_{i}\) of the previous step and produces a set of estimations:  
\[D=G(Y^{{}^{\prime}})=\{g_{1}(y^{{}^{\prime}}_{1}),g_{2}(y^{{}^{\prime}}_{2}),...,g_{N}(y^{{}^{\prime}}_{N})\}. \tag{1}\]  
Based on the targeted metric, the function \(G(\cdot)\) can (i) be a well defined formula; or (ii) an approximation depending on the complexity of the solution. In particular, the former case includes a mathematical representation of the system which incorporates a physical understanding of the functionality and the monitoring scheme. For example, on-vehicle sensors and monitoring ECUs can provide real-time information regarding the engine speed, the vehicle speed, the transmission gear position, etc. All of these values can be used to calculate various vehicle dynamics metrics. In the latter case, the system representation might not be available or may be too complex. However, it is possible to monitor the system and gather corresponding data values. In this case, the desired metric is calculated based on estimations derived from the monitored data. However, the critical part in this step is the quality of the data set. Generally, the confidence of the estimation increases as the data set includes more elements and the number of outliers is reduced. It should be noted that the initial model of the Equation (1) describes the general formulation of the problem, which is applicable to any number of information sources. In particular, the problem of combining \(N\) different information sources is tackled from the software perspective, as the presented methodology is developed on top of Hydra, a multi-agent distributed framework that targets smart cars [20]. Figure 1 depicts the overview of the proposed approach. In this paper, we utilized proprietary framework provided by Ford, regarding off-vehicle connectivity--cloud and Road Side Units (RSUs)--to retrieve weather data for a specific test-route and estimate the status of the road. Based on this estimation, a calculation of Road Surface Index (RSI) is performed and a recommended speed is provided. The estimation of road status based on weather information requires an approximate solution as it is too complicated to solve by a single mathematical formula. Thus, the goal is to create a data set with multiple observations throughout the year and correlate the different weather attributes with on-road surface status. Additionally, the proposed framework employs route-based information sources in order to capture specific intermediate way-points that dictate changes in the vehicle's speed. These points are referred to as _Points of Interest_ as they can provide useful and important information that can affect the nominal speed of the vehicle. Points of Interest are also based on vehicle's physical characteristics (weight, size, etc.) and road structure (bridges, turns, etc.) as well.