Automobiles are getting more and more sophisticated with increased demand for more comfort and safety by customers. Due to this, the automotive Electronic Control Units (ECU) and the software applications running on these ECUs have become more complex and computationally more intensive. This has resulted in Original Equipment Manufacturers (OEMs) migrating to multicore platforms. Optimal usage of multicore platform necessitates the design of new scheduling algorithms. In the past decade, different approaches to implement hard real time scheduling in automotive domain have been proposed for single core as well as multicore architectures. We explore different scheduling techniques proposed so far which are relevant to automotive domain and also, provide a taxonomy of these scheduling algorithms, which will help the automotive design engineer to make an informed decision. Through this study it is realized that, automotive standards such as AUTOSAR use manual scheduling, which consume lot of time to develop a schedule table and are inflexible. To address this issue, a new mathematical scheduling approach has been discussed as a case study. This systematic approach will not only reduce the time taken to develop a schedule table, it will also predict schedulability of a given set of tasks. The schedulability will be in terms of task overlaps and deadline misses which can be analyzed at design phase and is also more flexible by providing the users with multiple options based on earliest start time, least utilization and least task overlap criteria, along with the possibility of producing optimal results.