Autonomous Vehicles (AVs) promise to disrupt the traditional systems of transportation. An autonomous driving environment requires an uninterrupted, continuous stream of data and information based on complex traffic data sets and predictive measurements to make critical and real-time decisions in uncertain situations. Such an environment fosters a self-organizing system where vehicles must be seamlessly connected and various other services and intelligent decisions to manage traffic flow must be executed in an emergent manner. To proceed towards this vision, in this paper, we develop a traffic flow management model which is based on a novel two-phase approach for AVs to optimize traffic during congestion periods. In the first phase of our approach, we build an adaptive traffic signal control, using Deep Reinforcement Learning (DRL) to optimize traffic flow on road intersections during the periods when traffic is congested. In the second phase, we implement a Smart Re-routing (SR) technique for the traffic approaching intersections. Re-routing is used to carry out load-balancing of traffic to alternate paths to avoid congested road intersections. The experimental evaluation of the proposed approach is validated using simulations that demonstrate up to 31% improved performance efficiency compared to the traditional settings using pre-timed signals and without re-routing. The two-phase approach improves the overall traffic flow while reducing delays and minimizing long traffic queues’ lengths. This approach is useful for making infrastructure intelligent enough to handle traffic congestion and balance traffic flow efficiently.

Intelligent traffic management systems have become one of the main applications of Intelligent Transportation Systems (ITS). There is a growing interest in Reinforcement Learning (RL) based control methods in ITS applications such as autonomous driving and traffic management solutions. Deep learning helps in approximating substantially complex nonlinear functions from complicated data sets and tackling complex control issues. In this paper, we propose an approach based on Multi-Agent Reinforcement Learning (MARL) and smart routing to improve the flow of autonomous vehicles on road networks. We evaluate Multi-Agent Advantage Actor-Critic (MA2C) and Independent Advantage Actor-Critical (IA2C), recently suggested Multi-Agent Reinforcement Learning techniques with smart routing for traffic signal optimization to determine its potential. We investigate the framework offered by non-Markov decision processes, enabling a more in-depth understanding of the algorithms. We conduct a critical analysis to observe the robustness and effectiveness of the method. The method’s efficacy and reliability are demonstrated by simulations using SUMO, a software modeling tool for traffic simulations. We used a road network that contains seven intersections. Our findings show that MA2C, when trained on pseudo-random vehicle flows, is a viable methodology that outperforms competing techniques.

Connected Autonomous Vehicles (AVs) promise innovative solutions for traffic flow management, especially for congestion mitigation. Vehicle-to-Vehicle (V2V) communication depends on wireless technology where vehicles can communicate with each other about obstacles and make cooperative strategies to avoid these obstacles. Vehicle-to-Infrastructure (V2I) also helps vehicles to make use of infrastructural components to navigate through different paths. This paper proposes an approach based on swarm intelligence for the formation and evolution of platoons to maintain traffic flow during congestion and collision avoidance practices using V2V and V2I communications. In this paper, we present a two level approach to improve traffic flow of AVs. At the first level, we reduce the congestion by forming platoons and study how platooning helps vehicles deal with congestion or obstacles in uncertain situations. We performed experiments based on different challenging scenarios during the platoon’s formation and evolution. At the second level, we incorporate a collision avoidance mechanism using V2V and V2I infrastructures. We used SUMO, Omnet++ with veins for simulations. The results show significant improvement in performance in maintaining traffic flow.