Autonomous vehicles (AVs) promise to transform transportation by improving road-safety, increasingmobility access, and enabling new mobility services. This paper presents a comprehensive systems-level review andpractical draft design for contemporary autonomous cars, covering levels of driving automation, sensor suites,perception pipelines, localization and mapping strategies, motion-planning and control architectures, simulationand evaluation approaches, and safety/ethical considerations. We summarize current industry and standardsperspectives on automation levels, then present a modular architecture that integrates multi-sensor perception(camera, LiDAR, radar), real-time sensor fusion and object tracking, simultaneous localization and mapping (SLAM),behavior planning (route and tactical), and trajectory generation and low-level control. Important algorithmicchoices—classical (A*, RRT, MPC) and learning-based (deep perception networks, reinforcement learning fordecision-making)—are compared with their strengths and failure modes. The draft includes recommendedsoftware/hardware stacks, data pipelines, validation approaches (closed-track testing and large-scale simulation),and metrics for safety and performance evaluation. It also examines practical failure modes (sensor occlusion,adverse weather, distributional shift), regulatory and ethical constraints, and socio-economic impacts. Wherepossible the design favors explainable, verifiable methods that enable safety cases supported by reproducibletesting. Finally, the paper outlines a staged roadmap for development from Level 2/3 driver-assistance prototypesto Level 4 operational design domains (ODDs). The review and draft aim to be a pragmatic blueprint both forresearch teams and startups seeking to build safe, testable autonomous driving systems while acknowledging openresearch challenges and policy needs.