# Environmental-Based Speed Recommendation for Future Smart Cars +
## 1 Introduction  
Modern high-end vehicles are equipped with up to 100 embedded Electronic Control Units (ECUs), each executing standard services with pre-defined inputs [1; 2]. Advanced driver-assistant systems (ADAS) and multimedia applications are some examples of services that are realized by running complex algorithms on embedded ECUs [3]. However, those pre-specified functions cannot be enhanced throughout the traditional vehicle architecture, since it is very expensive to develop new services on the current static vehicle architecture [4]. For that reason, the current design has become an obstacle towards the evolution of the functionality in the car, and thus modern vehicles are shifting from the traditional distributed hardware modules with fixed non-scalable functionality to a more robust distributed multi-agent based architecture. Moreover, modern vehicles integrate and exploit a large number of sensors, in order to acquire an accurate representation of the environment and automate several driving tasks, that were traditionally handled by humans [1]. For instance, radar, lidar, GPS, are some of the types of sensors which are deployed in modern vehicles and create input streams that are processed by the ECUs when running complex algorithms for sophisticated services, such as cruise control or forward-collision warning [5; 6]. Vehicles are also equipped with visual sensors which generate a huge amount of visual data with rich and resourceful content [7]. This data is streamed to a variety of heterogeneous computing elements in order to create a precise representation of the surrounding environment of the car [8]. This increased amount of information requires innovative and efficient off-loading techniques for information extraction [9]. All these advantages in the functionality of modern vehicles has established the term _smart cars_. Apart from the increased computing and sensing capabilities that allow smart cars to process input data from multiple sources, they present growing connectivity capabilities as well [10]. Modern vehicles are connected over the Internet and cooperate with other vehicles or road-side units. A variety of technologies and different communication protocols are employed to serve different application contexts, such as vehicle safety, comfort, and entertainment. Specifically, connected vehicles are linked to the cloud, to other vehicles or even to road-side infrastructure utilizing wireless technologies (WiFi, 4G, LTE, etc.) [11]. The impact of vehicle connectivity is considered crucial in terms of vehicle safety and, for that reason, the National Highway Traffic Safety Administration (NHTSA) of the USA expects that every new car will be equipped with connectivity capabilities after year 2020 [12]. Thus, smart cars are becoming the most sophisticated entity in the Internet of Things (IoT) era, hence they prevail as a replacement to modern cars introducing the era of _Internet of Vehicles_[13; 14; 15]. Increased vehicle connectivity brings major challenges to the automotive research engineering groups that try to utilize all the available information sources efficiently. Existing approaches focus on processing data from single information sources, while their outcome is strongly affected by the information quality. A stand-alone information source or sensor cannot overcome certain physical limitations, i.e., the limited sensor range or the field of view. For instance, camera systems have difficulty in detecting bright objects when the camera sensor is blinded by the sun, whereas a radar system is able to detect other vehicles or obstacles in the same situation. As a result, data are susceptible to imprecision and inconsistency [14; 16] and making single information source approaches inefficient and error prone, as they can lead to imprecise results. Therefore, it becomes prominent to abandon traditional single source approaches and move on multi source strategies.