# A Systematic Literature Review of Autonomous and Connected Vehicles in Traffic Management
## 3 Research Analysis
### Traffic Management Methodologies Consisiting of Primary Goals
#### 3.2.3 Safety and Efficiency
Reference [108] suggested using decentralized coordination learning of autonomous intersection management (DCL-AIM) to optimize control strategy to reduce delays and prevent collisions at the intersection. Multi-agent Markov decision processes (MAMDPs) represent vehicles' sequential movement, and reinforcement learning, particularly multi-agent reinforcement learning, is used to address the problem. According to the simulation findings, the DCL-AIM performs better than the current control techniques. A distributed cooperative control has been developed to avoid collisions between connected and autonomous vehicles at an unsignalized crossroads [31]. A distributed synchronized signal-free intersection control logic is known as (DC-SICL). According to the simulation's findings, the suggested technique outperforms an optimum actuation signal control in terms of travel time, traffic flow, and safety. Another study [45] suggested a dispute-avoidance-based strategy for managing vehicles at the unsignalized crossroads by considering all-direction turn lanes (ADTL). The simulation results showed that, with assured collision avoidance, the suggested technique outperforms conventional traffic signals in terms of throughput and trip time. In addition, they offered a development approach to increase throughput at an unsignalized junction without increasing the danger of an accident. They created the individual and car sharing-based arrival model, which uses the optimal entry time scheduling (OETS) method and a heuristic algorithm. Compared to conventional traffic signals, the suggested strategy reduces traffic delays and increases efficiency [109]. A visible light communication (VLC)-based collision avoidance strategy is presented to coordinate autonomous vehicles in crossing roundabouts, which has excellent efficacy in vehicular contexts to prevent traffic jams or major accidents [69]. At the roundabout entrances, roadside units (RSUs) are set up to coordinate the vehicles in a vehicle-to-infrastructure (V2I) mode. Vehicles can pass the roundabout concurrently using the synchronization strategy if their trajectories are parallel. Otherwise, traffic is prioritized based on arrival time, and proper braking is used to avoid potential problems. Simulation findings show that our suggested technique fulfills the roundabout traffic demands in concurrency, safety, and time utilization, since only 22% of the situations investigated in the various scenarios strongly advise vehicles to slow down. Similarly, ref. [32] developed and tested a variety of spatiotemporal-based algorithms for autonomous, connected vehicles to enable safe and effective junction crossing. Vehicles communicate using dedicated short-range communications (DSRC) through vehicle-to-vehicle (V2V) communication. To improve safety and efficiency when interacting with other traffic participants, ref. [110] developed a multi-task safe reinforcement learning framework with social attention. In recent years, autonomous vehicles have been challenging to navigate in intersections and between heavy traffic. They set up trials in the simulators CARLA, which has very realistic vehicle models, and SUMO, which has a lot of traffic. Both examples demonstrated how the suggested approach for the multi-task junction navigation problem increases safety while maintaining steady traffic efficiency. Additionally, ref. [111] offered an intelligent overtaking choice for an autonomous vehicle based on the heuristic reinforcement learning approach. The proposed overtaking control is centered on the effectiveness and safety of operating an autonomous vehicle. As shown in Table 3, different strategies have been devised to increase intersection productivity while considering safety. Four optimization techniques, according to researchers, can be used in real-time or online applications ([62; 104; 107; 108]). Most of the suggested techniques and base cases were examined in a simulated setting.