# Environment-Aware Adaptive Reinforcement Learning-Based Routing for Vehicular Ad Hoc Networks
## 2 Related Work
### Traffic-Aware Routing  
The traffic-aware routing protocol can adapt to different network and traffic states. In VANETs, vehicles serve as the entities responsible for forwarding data packets from source to destination. As a result, traffic-aware routing protocols make routing decisions based on the availability of road segments and the reliability of intervehicle links. This ensures the efficiency and quality of data-packet transmission. Traffic awareness improves the intelligence of route selection, thereby catering to different road conditions and traffic states and making communication within VANETs more reliable and efficient. In [29; 30], the authors enhanced the improved greedy-forwarding mechanism by selecting neighbors with high received signal-strength indicator (RSSI) values. However, in areas near intersections, vehicles often change direction rapidly, which can lead to the selection of the wrong next hop. Furthermore, neighbor location predictions may be inaccurate, as the instantaneous movement information received by the beacons may not accurately depict the motion state of the vehicles over a period of time. This can lead to unreliable next-hop predictions. GyTAR [31] is a geographic routing protocol based on intersections, which is designed to find optimal routes in urban environments. GyTAR dynamically and sequentially selects intersections through which data packets are forwarded towards the destination. When packets were transmitted to an intersection, the routing decisions are made based on parameters such as changes in traffic flow in the surrounding road segments and the remaining distance to the destination from the next intersection. In GyTAR, within segments between intersections, data forwarding is achieved using an enhanced greedy-forwarding mechanism. In [32], the proposed LITAR is a lightweight traffic-aware routing protocol that relies on intersections to determine the transmission direction for packets. To reduce the additional network overhead generated during segment connectivity measurements and maintain the accuracy of measurement time, LITAR introduces two new algorithms: enhanced validity period calculation (EVPC) and restricted collector packet reply (RCPR). Additionally, LITAR makes routing decisions based on factors such as vehicle direction density, road-network connectivity (RNC), and distance to the destination. In [33], a reliable traffic-aware routing protocol was introduced, which leverages the real-time traffic and network-state measurement (RTNSM) process introduced in [32] to achieve accurate and lightweight road-state assessment. This protocol selects the next hop based on the road structure, predicted positions of neighbors, received signal strength, and recency of mobility information received from neighbors. TLRP [34] is a traffic-aware and link-quality-sensitive city vehicular network routing protocol. The protocol introduces a novel routing metric, namely link transmission quality (LTQ). Considering the high mobility of vehicles, nonuniform distribution, and the quantity and quality of the communication links along the routing path, the protocol prevents packet forwarding in situations of severe congestion in ultra-low density road sections or vehicle networks. Subsequently, a road weight evaluation scheme was proposed that utilized LTQ and link information to assess each road segment. IQRRL [35] is a quality-of-service (QoS) routing protocol based on intersections. IQRRL routing involves the selection of intersections within the chosen road and the choice of the next hop. IQRRL takes into account fundamental metrics such as latency and connection probability. It utilizes the Dijkstra algorithm to consider the communication quality from adjacent roads to the destination node, thereby avoiding the problem where local optimization leads to intersections getting farther away from the endpoint.