Quick Start is designed forBeginner Learners<\/strong>. It provides problem descriptions and starter code for four major areas\u2014perception, prediction, decision-making and planning, and control\u2014balancing theory with practice. Learners can quickly understand the autonomous-driving technology stack, the role of each area, and its inputs and outputs, while also running code and completing hands-on tasks.<\/p>\n<\/div> Task descriptions:<\/strong>To help beginners understand the autonomous-driving technology stack and the role and research direction of each module, Quick Start provides a task-description page for every area. Each page explains why the area matters, its related research tasks, and the inputs, outputs, key characteristics, and challenges of each task, helping learners build a thorough understanding of autonomous driving.<\/p>\n<\/div><\/li> Get started with code:<\/strong>Quick Start selects representative tasks for each area and provides example models. Using foundational models such as YOLO, LSTM, and MPC for different autonomous-driving tasks, learners can quickly gain practical experience and deepen their understanding through hands-on work. The examples cover the full workflow from environment setup to model execution, while keeping complexity manageable for beginners.<\/p>\n<\/div><\/li><\/ul><\/div><\/div><\/div><\/div>