自动驾驶前沿方案：从端到端到VLA工作一览

Core Viewpoint - The article discusses the advancements in end-to-end (E2E) and VLA (Vision-Language Architecture) algorithms in the autonomous driving industry, highlighting their potential to enhance driving capabilities through unified perception and control modeling, despite their higher technical complexity [1][5]. Summary by Sections End-to-End Algorithms - End-to-end approaches are categorized into single-stage and two-stage methods, with the latter focusing more on joint prediction, where perception serves as input for trajectory planning and prediction [3]. - Single-stage end-to-end models include various methods such as UniAD, DiffusionDrive, and Drive-OccWorld, each emphasizing different aspects and likely to be optimized by combining their strengths in production [3][37]. VLA Algorithms - VLA extends the capabilities of large models to enhance scene understanding in production models, with internal discussions on language models as interpreters and various algorithm summaries for modular and unified end-to-end VLA [5][45]. - The community has compiled over 40 technical routes, facilitating quick access to industry applications, benchmarks, and learning pathways [7]. Community and Resources - The community provides a platform for knowledge exchange among members from renowned universities and leading companies in the autonomous driving sector, offering resources such as open-source projects, datasets, and learning routes [19][35]. - A comprehensive technical stack and roadmap for beginners and advanced researchers are available, covering various aspects of autonomous driving technology [12][15]. Job Opportunities and Networking - The community has established job referral mechanisms with multiple autonomous driving companies, encouraging members to connect and share job opportunities [10][17]. - Regular discussions on industry trends, research directions, and practical applications are held, fostering a collaborative environment for learning and professional growth [20][83].