This dissertation focuses on the challenges arising from real-time autonomous cyber-physical systems. Since many cyber-physical applications increasingly require high performance to run complex functionalities, e.g., self-driving software stacks, it is essential to use limited resources efficiently on resource-constrained embedded platforms. Besides, as real-time autonomy is expected to become more pervasive in various safety-critical application domains, e.g., aerospace and defense, timeliness end-to-end latency of critical computation chains is particularly crucial because the late response or the violation of timing constraints may cause catastrophic consequences. On top of that, any design and analysis methods to fulfill these requirements should be predictable in order to establish a reliable execution foundation.To address the aforementioned challenges, we develop analyzable yet practical scheduling techniques for practical real-time cyber-physical systems, with examples of autonomous vehicles. First, we propose a novel job-class-level scheduler (JCLS) equipped with a low-complexity analysis tool. The key observation behind this work is that many cyber-physical applications can often tolerate a certain degree of timing violations as long as the number of the violations is predictably bounded. By capturing this effect, JCLS exploits application-specific bounding constraints and efficiently manages limited resources, especially, enables overloaded workloads schedulable on embedded platforms. Secondly, we develop a chain-based scheduling method (CBS) to improve the data freshness of real-time tasks with data dependency. This technique provides better quality-of-service outputs by exploiting the effective job-level information flows in the read-execute-write model which is prevalent in automotive systems. Lastly, we propose a new scheduling architecture design Robot Operating System (ROS2), which is the most popular open-source robotic framework. Unlike the default fairness-based resource management methods in ROS2, our priority-driven chain-aware scheduling (PiCAS) enables prioritization of critical computation chains across system layers to minimize end-to-end latency, and its effectiveness has been verified under real-world scenarios. The contributions of this dissertation pave the road towards designing practical autonomous real-time systems with efficient and predictable scheduling and resource management schemes.