王建强：自动驾驶正从规则驱动与数据驱动向认知驱动演进

Core Viewpoint - Intelligent automotive technology is a key solution for traffic safety, which remains a perpetual theme in the development of smart vehicles [1] Group 1: Current State of Intelligent Vehicles - Low-level intelligent vehicles have achieved a high market penetration rate, but accidents still occur as the industry transitions to higher levels of autonomous driving [1] - There are significant challenges in safety technology that need to be addressed in the context of complex long-tail scenarios [1] Group 2: Technological Approaches - The early development of intelligent vehicles relied on rule-driven approaches, while current mainstream autonomous driving methods include data-driven techniques [4] - Rule-driven systems are observable and interpretable but are inflexible in complex environments, whereas data-driven systems utilize deep learning but suffer from a "black box" nature that obscures decision-making processes [4] - A proposed third route, "cognitive-driven," aims to combine the interpretability of rule-driven systems with the learning capabilities of data-driven systems, enhancing adaptability and transparency [4][5] Group 3: Cognitive-Driven Architecture - The cognitive-driven approach is based on a deep understanding of the interactions between humans, vehicles, and roads, leading to accurate modeling and digital representation of system characteristics [5] - The architecture consists of three layers: perception, cognition, and decision-making, integrating physical state estimation, semantic understanding, and human-like adaptive decision generation [5][6] Group 4: Future Trends and Goals - The evolution of autonomous driving is shifting from rule-driven and data-driven methods to cognitive-driven systems, focusing on human-like cognition, learning, and evolution [5] - A new paradigm of "self-learning + prior knowledge" is necessary to enhance environmental understanding and reasoning capabilities, improving safety and generalization in long-tail scenarios [5] - The ultimate goal is to develop a high-level intelligent driving system that possesses self-learning, self-reflection, and adaptive capabilities, ensuring safety and verifiability [6]