Recent work on QoS routing in VANETs emphasises designing route-selection and adaptation strategies that can meet stringent service requirements such as low latency, high reliability, and consistent throughput in highly mobile vehicular environments. Researchers are using optimization techniques (e.g., Taguchi-based tuning, combinatorial algorithms) to enhance protocols like AODV, GPSR and OLSR for QoS performance under varying traffic densities and mobility patterns. Hybrid approaches combine machine learning, metaheuristics (such as ant-colony, snake optimization) and software-defined networking (SDN) control planes to route packets according to delay/jitter constraints and link?stability metrics. For instance, one recent protocol optimised OLSR parameters via meta-heuristics and achieved better QoS than standard settings. Others extend geographic routing to include QoE/QoS factors like link expiry time, packet loss, and jitter for video streaming services in VANETs. Together, these advances are steering VANET routing toward service-aware, performance-guaranteed frameworks capable of supporting both safety-critical and infotainment applications.