清华&小米团队发布VLA模型综述

Core Viewpoint - The article discusses the evolution of autonomous driving technology, highlighting the transition from basic perception-control systems to advanced cognitive intelligence models, specifically focusing on the latest Visual-Language-Action (VLA) paradigm [1]. Group 1: Evolution of Autonomous Driving Technology - Autonomous driving technology is evolving from simple perception-control to advanced cognitive intelligence, categorized into three main paradigms: End-to-End AD, Visual Language Models for AD, and Visual-Language-Action Models for AD [3][4]. - The VLA model integrates visual perception, language understanding, and action execution into a unified framework, enabling vehicles to follow natural language commands directly [3][4]. Group 2: VLA Model Architecture - A typical VLA model consists of three parts: input, processing, and output, aiming to seamlessly integrate environmental perception, advanced instruction understanding, and vehicle control [4]. - Multi-modal inputs include visual and sensor data, with advancements from single front-facing cameras to multi-camera systems and various sensors like LiDAR, RADAR, IMU, and GPS for enhanced spatial awareness [5][6]. - Language inputs have evolved to include direct commands, environmental queries, task-level instructions, and conversational reasoning, supporting multi-turn dialogues and complex reasoning [6][9][10]. Group 3: Core Processing Modules - The core processing modules include a visual encoder for transforming raw images into understandable representations, a language processor using pre-trained models for natural language commands, and an action decoder for generating control outputs [11][12]. - The action decoder can implement various methods such as autoregressive tokenization, diffusion models, and hierarchical controllers for generating control signals [12][13][14]. Group 4: Development Stages of VLA Models - The development of VLA models is divided into four stages: 1. Language as an Explainer: Initially used for enhancing system interpretability without direct control involvement [19]. 2. Modular VLA: Language evolves into an active planning component, directly informing planning decisions [20][21]. 3. End-to-End VLA: Unified networks map sensor inputs directly to driving actions, improving responsiveness but facing challenges in long-term planning [22]. 4. Reasoning-Augmented VLA: Incorporates reasoning and memory, allowing for long-term predictions and dynamic human-machine interaction [23]. Group 5: Datasets and Benchmarks - High-quality, diverse datasets are crucial for advancing VLA research, covering large-scale real-world data, critical safety testing scenarios, and fine-grained reasoning data [25]. Group 6: Challenges and Future Outlook - Key challenges include robustness and reliability, real-time performance, data bottlenecks, multi-modal alignment, multi-agent social complexity, and generalization across different traffic environments [26][27][28][29][30][31][32]. - Future directions involve creating a foundational driving model, integrating neural-symbolic safety cores, fleet-level continuous learning, standardizing traffic language, and developing cross-modal social intelligence [33][34][35][36][37].