# Assisting Drivers at Stop Signs in a Connected Vehicle Environment
## 2 Related Work  
Modern vehicles have been manufactured with several advanced equipment and sensors recently. Global positioning systems (GPS), digital maps, camcorders, and wireless transceivers are the most commonly added devices to vehicles [1; 7]. These devices allow tracking of the vehicles' movement and their main driving operations. Moreover, they enable the timely surveillance of the surrounding traffic for each vehicle. This introduces the chance to remotely and autonomously control the driving processes in each vehicle. Based on the surrounding traffic characteristics and the context in these driving control systems, the best driving operation is selected for each vehicle [1; 8]. In the target of enhancing the safety and efficiency conditions on road networks, several intelligent driving systems have been proposed. Several smart systems of controlling vehicles and directing drivers have been developed in the literature to assist drivers on the road networks [1]. These protocols have been developed and adapted to autonomous vehicles as well. These systems control the vehicles on the road networks according to the real-time traffic distribution and the context of the investigated road network [1]. Stop signs are commonly seen on road networks. They are usually installed at road intersections in downtowns, before the pedestrian crosswalk, or on an entrance point to the highway, as well as popping up on school buses [5]. Drivers must bring their vehicles to a full stop in front of these signs. This increases the surrounding area's safety conditions and gives drivers time to make the right decision that avoids possible accidents. However, stop signs decrease the traffic fluency at road intersections [9]. Vehicular networks and artificial intelligence systems have been used to develop a reliable and efficient controlling system for stop sign intersections. Some systems have been developed for small and low-traffic intersections. [PERSON] et al. [10] developed a traffic control system for two-way stop-controlled intersections with low traffic volumes. The technologies of neural networks and binary-logic models are in this system to predict the accepted and rejected gaps between vehicles on the main road. This helps to guarantee higher safety conditions at the secondary road of the investigated road intersection. [PERSON] et al. [8] also developed an unmanaged intersection control system for autonomous vehicles that replaces the stop sign. This system assumes an existing infrastructure at the intersection and mainly depends on peer-to-peer communications. Vehicles must call ahead an agent station at the intersection and reserve time and space for their traversal. This system achieves effective performance in small intersections and low-traffic scenarios. [PERSON] et al. [11] also designed intelligent intersections where traffic lights and stop signs are removed, and vehicles negotiate the intersection through an interaction of centralized and distributed decision-making technologies. Most of the proposed traffic control systems in this field aim to enhance the traffic fluency and safety conditions on all-ways stop-signed road intersections [12; 13]. Some smart advisory systems have been developed using the technology of RFID to provide drivers with earlier warning messages when they are approaching an unsignalized intersection [14]. [PERSON] and [PERSON] [12] developed a framework of smart traffic sign boards for smart cities. The latter framework depends on communication protocols among traveling vehicles and installed infrastructures on the road network. It keeps the drivers aware of their surrounding speed breakers, speed limits, schools, or U-turns ahead. Moreover, [PERSON] et al. [13] developed a driver assistance system with deceleration control and brake hold functions in stop sign intersections that alert the drivers of the approaching stop sign intersections to react by controlling their vehicles and forcing their complete stop there.