# Environmental-Based Speed Recommendation for Future Smart Cars +
## 2 Related Work  
Previous research works have introduced methodologies that try to combine multiple sources of information, taking advantage of the increased sensor and computing capabilities on modern vehicles, so that they can achieve a more accurate representation of the surrounding environment. The ultimate goal is to make the best decision according to the current circumstances, in order to improve safety and comfort of vehicular experiences for the passengers. Many research works have focused on the integration of multiple information sources in order to improve passenger safety. Authors in [17] envision the future smart car as an intelligent vehicle that can interact with various information sources (on-board sensors, road side infrastructure or Internet). Authors in [21] propose an optimization scheme that tries to optimize vehicle's trajectory and thus optimize the efficiency of the driver. They manage to minimize vehicle's fuel consumption by calculating the optimal velocity trajectory. Authors in [22] utilize information from multiple sources through increased vehicle communication capabilities (vehicle-to-vehicle and vehicle-to-infrastructure) in order to provide a more efficient driving style. Researchers in [23] aim at increasing vehicle'sperception by cooperatively utilizing information from other vehicles or infrastructure, through wireless connectivity. Based on that enhanced perception, the car can provide augmented situational awareness for a safer and more efficient vehicular experience for the passenger. Authors in [24] try to increase passenger safety by detecting events that indicate dangerous driving, combining on-vehicle sensor information with cloud information, while the authors in [25] present a methodology that aims to enhance vehicle's perception of the surrounding environment in order to detect dangerous parts of the road. Their approach combines on-vehicle measurements with wireless sensor inputs from Road Side Units (RSUs) and other vehicles to calculate the confidence of a possible road hazard. Additionally, the authors in [14] utilize weather information to provide a recommended vehicle speed. In [26], a multi-source framework is presented that performs real-time analysis in order to detect dangerous parts on the road surface. The research team in [27] proposes a novel methodology that targets the increasing of environmental perception and knowledge of the vehicle by combining on-vehicle, on-route and visual information. Authors in [28] propose a new system that incorporates cloud-based services, on-vehicle sensor data and information from roadside infrastructure, targeting the enhancement of vehicle's safety and identifying an efficient vehicle trajectory. Authors in [29] propose a deep learning-based architecture that merges data from multiple inputs, combining on-route information, visual information and vehicular measurements that capture driving environment from multiple points of view. Other research teams that target intelligent transportation systems manage to detect dangerous events on the road by mixing on-vehicle measurements and metrics that are gathered from Road Side Units (RSUs), using wireless sensors [30]. Moreover, many research works focus on utilizing multiple sources in order to make the passenger's experience more comfortable. For example, automated driving is one of the main characteristics of future smart cars. Authors in [31] combine different sources of information, such as localization information (via GPS) and road side units, in order to automate driving tasks and achieve enhanced passenger comfort. In [32], it is stated that future cars will incorporate enhanced connectivity capabilities with both the infrastructure (through Cloud) and other vehicles, so that future cars can offer advanced entertainment and cognitive services during travel time.