# Environmental-Based Speed Recommendation for Future Smart Cars +
## 1 Introduction
In the context of smart cars era, the available computation and connection capabilities can support the development of multi-source framework that combines heterogeneous information sources. The main objective of a multi-source framework that targets automotive domain is to increase driver's awareness about any possible danger in the observed environment. In other words, improve the coverage of the observation area and provide a better knowledge and perception of the environment of the vehicle, in order to make the driving experience safer, more comfortable and more efficient. In order to facilitate development of more sophisticated applications for road safety, infotainment, smart and green transportation, it is necessary to combine multiple heterogeneous sources of information [17]. This new automotive era is realized with increasing communication capabilities of future cars (wireless connectivity) and transforms the current static perception of vehicles to an intelligent entity that dynamically interacts with its rapidly changing environment [18; 19]. Thus, future intelligent transportation systems will combine different sources of information and create an enhanced environmental awareness of the vehicle, offering an advanced resourceful travel experience for the passengers/motorists. In this paper, we present a systematic methodology to cohesively align information provided by off- and on-vehicle sources. Specifically, the proposed approach utilizes enhanced connectivity of modern vehicles to gather data about weather, on-route and vehicular information and it manages to create a more accurate perception of the surrounding environment of future smart cars. As a use case, the presented methodology combines information from multiple heterogeneous sources and alerts the driver about the recommended speed that the car should follow, so that it can avoid dangerous driving. Compared to our original work presented in [14], the differentiators of the presented methodology are the following:* We further explain the details of our original implementation in [14], providing more details about the data analysis (Section 3.2). We also evaluate and compare the presented classification model against existing techniques and we present the results in Section 4.4. * We introduce a new feature that utilizes route-based information and extracts specific intermediate way-points that dictate change in vehicle's speed. * We utilize vehicle data as a new information source in order to calculate the mismatch between the theoretical engine speed and the actual engine speed of the vehicle. Such mismatch may cause the trigger of advanced driver-assistance systems (e.g., ABS, ESC). * We validate weather- and route-based information with real-time vehicle data and inform the driver about a recommended (dynamic) speed that the car should adapt to, based on the current road status. * We developed the overall service on top of Hydra [20], a distributed multi-agent computing framework that targets smart cars and it provides self-management functions such as in-car workload balancing and failure recovery. Section 4.1 further explains the implementation presented in [14] and enhances this work with more technical details regarding the development of the proposed methodology on real computing boards by utilizing the computation infrastructure in [20]. It is worth mentioning that, since such sophisticated services require flexible computing architectures, the proposed methodology was developed as a service layer on top of distributed computing framework for smart cars (Hydra [20]). However, Hydra was designed in order to provide self-management functionality and to support services as a box for modern vehicles improving scalability. The main differences between the proposed work and Hydra [20] are summarized as follows:  
1. Hydra is a generic framework that supports the deployment of a variety of applications, and several automotive benchmarks were tested in [20].