The future autonomous vehicle is not only processing the copious amount of indispensable data generated by its onboard sensors but also utilizing the data from other vehicles, roadside unit (RSU) etc. Managing the mixed-criticality data requires intelligent time-sensitive scheduling and routing within the in-vehicle network (IVN) infrastructure. Use-cases related to self-adaptivity (including vehicular communication), partial networking and embedded virtualization require to change the configuration of the IVN at runtime. State-of-the-art IEEE Time-Sensitive Networking (TSN) standards possess a grave challenge in handling runtime reconfigurations. Above mentioned use-cases foster the development of scalable and efficient dynamic scheduling and routing algorithms for TSN based IVN. In this paper, four meticulously designed heuristics are analyzed for dynamic scheduling and routing on-the-fly in TSN based IVN. One of the algorithms, Bottleneck heuristic outperforms others in term of schedulability and response time. It schedules around 16 − 22% more flows as compared to other developed heuristics depending on the network load.