## 1 Introduction  
Vehicular Ad Hoc Networks (VANETs) are mobile network-based autonomous systems consisting of wirelessly connected mobile vehicles in the transportation sector. However, VANETs have a set of challenges for information dissemination due to frequent topology changes due to high mobility and the presence of isolated nodes. By incorporating the Software-Defined Networking (SDN) concept into VANETs, a new paradigm called Software-Defined Vehicular Network (SDVN) has been developed to overcome the previous challenges. It consists of a logically centralized control plane with a network view that collects data from the vehicular network and makes network decisions based on that view, thereby making vehicular networks more programmable and flexible. Routing is the process of selecting a path for traffic on a given network. It has been challenging for VANETs, due to their dynamic network topologies resulting from the high mobility of vehicular nodes. There exist numerous routing techniques for routing in VANETs, such as greedy stateless routing using immediate neighbors, distance vector routing on demand, using link states generated by multipoint relays, distance vector routing using periodic broadcasts, etc. In SDVN, routing occurs by modifying flow tables by the centralized controllers based on the metadata collected by the controllers. Heuristic shortest path algorithms, greedy routing algorithms minimizing cost, routing with minimum communication hops, shortest stable path routing, routing with minimum service delay, etc. have been used in SDVN for routing. However, the problem is that, to the best of our knowledge, a routing algorithm for SDVNs that is based on both link delay and link lifetime has not yet been proposed. In the existing literature, there is only a theoretical approach to estimating an average value for the total delay at each hop, while there is no practical approach for finding the exact total one-hop delay for each communication channel. Furthermore, most existing routing algorithms do not exploit the heterogeneous links existing in vehicular networks, which are wired and wireless. Furthermore, parameter computation at the controller has been conducted entirely by many routing algorithms using heuristic approaches. However, we note that parameter computations, such as link lifetime and link delay calculations using rule-based methods, are computationally complex and can grow with the network size, resulting in the controller not computing routes in a timely manner. This research focuses on a novel routing algorithm for SDVNs in order to overcome the problems stated above. The proposed routing technique has a hybrid mode with two modes: the highest stable shortest path mode and the highest stable least delay mode, in which the mode is selected based on the network contention. When network contention is low, the highest stable least delay mode is selected, as one-hop channel delay prediction is more accurate under low network contention, and vice versa. For computing network contention, we first distinctly identify the vehicular network as a heterogeneous network consisting of wired (I2I--Infrastructure to Infrastructure) and wireless links (V2V--Vehicle to Vehicle, V2I--Vehicle to Infrastructure, I2V--Infrastructure to Vehicle) and formulate heterogeneous link state entropy and the pending transmission packet distribution factor to predict collision probability. We use a trained Deep Neural Network (DNN) to predict delay at each hop for wired and wireless communication channels when network contention is lower than a specified threshold. According to the best of our knowledge, we were the first to predict the exact one-hop delay at each hop for the two communication channels in SDVNs with the help of machine learning and compute the link delays in the network based on the predictions.