# Efficient and Safe Strategies for Intersection Management: A Review
## 3 Efficiency
### Green Light Optimized Speed Advisory (GLOSA)
#### 3.2.2 Three Algorithms of GLOSA  
With the assistance of VANET, the vehicle OBU is responsible for receiving information from the RSU and knowing the traffic light time plan of the intersection in advance, thereby determining the speed of the vehicle passing through the intersection under the condition of avoiding the red light according to different algorithms. The green wave speed is generally calculated based on the signal time and the distance from the signal light, so we will introduce the basic computational formulas in theory and some different ways in actual implementation. As shown in Figure 6, \(d\) is the distance to the intersection, \(v\) is the initial speed, \(t\) is the time to reach the traffic light plus the remaining time for the next green phase (\(t_{red}\) or \(t_{yellow}\)+\(t_{red}\) respectively), and \(a\) is the acceleration, the basic rules of motion, given by (1)-(3) [40]:  
\[d=v*t+1/2*a*t^{2}. \tag{1}\]  
The time to reach the traffic light (\(t_{TL}\)) can be calculated as shown in (2):  
\[t_{TL}=\left\{ \begin{aligned} d/v,& a=0\\ -v/a+(v^{2}/a+2d/a),& a\
eq 0.\end{aligned} \right. \tag{2}\]  
The target speed (\(v_{t}\)) for the red light phase is calculated using (3):  
\[v_{t}=(2*d)/t-v. \tag{3}\]  
Based on these equations, predecessors propose many different algorithms. A number of conventional vehicles currently in the sale are equipped with Adaptive Cruise Control (ACC) auxiliary systems, such as the Weilai ES8, the Ford Co-Pilot360 TM, and the BMW 5 Series. The ACC equipment-connected vehicle travelling at the signalized intersection is often taken over by the driver without considering the signalized intersection constraints, and the vehicle speed changes obviously. In response to this problem, using the upcoming traffic signal information as the upper controller of the ACC system, [PERSON] et al. [41] propose an ecological driving intelligent prediction model (IDM) based on V2X communication. For the intersection signal constraint problem considered in IDM, it is used as a virtual front vehicle before the red light, and there is no obstacle to deal with when the green light is on. In order to reduce the idling time at the intersection, a crossroad model method is proposed to predict the arrival time and the downstream queue discharge time. The article also proposes an eco-driving model combined with a deceleration strategy to resolve the combined signal phase and time (SPaT) and vehicle state constraints. Numerical simulations show that the speed profile generated by the eco-driven model is a speed consultant, which can reduce idling time and fuel consumption level near the signalized intersection. In order to ensure that CV followers can track the leader's speed and interval time while achieving uniform performance, a conformance-based CV queue model [42] considering V2X network topology is proposed. Two crossing decision methods considering the duration of yellow light were proposed to determine the average expected speed of the leader and prevent the leader from running the red light. The safe and energy-saving oriented SAM based on triangle curve and logistics curve is used to guide the CV leader to approach and leave the signalized intersection with stable driving behavior when the green light is completely discharged from the queue for planning the speed trajectory of the CV leader. The main obstacles faced by electric vehicles are the relatively short-range, inadequate infrastructure, and the difficulty of long-distance travel. While the development of batteries and other technologies has enabled future vehicles to overcome these difficulties, it is necessary to reduce energy consumption by adopting more energy-efficient driving behavior. The emergence of advanced sensing technologies and V2X technology opens up new possibilities for security and energy-efficient applications.