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]

证券研究报告 行业研究 / 行业点评 2025 年 06 月 18 日 机械设备 一年内行业指数与沪深 300 指数对比走势： 资料来源：聚源数据，爱建证券研究所 相关研究 《【爱建智造】周观点：存储厂景气延续，重 视清洗设备需求》2025-06-08 《【爱建智造】周观点：机器人执行层迭代， 走向场景实用适配》2025-06-03 《【爱建智造】周观点：机器人格斗赛带来动 捕系统新增量》2025-05-26 《Optimus 运动智能突破，T 链催化密集—— 爱建智能制造周报 （2025/05/12-2025/05/16）》2025-05-17 《国产半导体设备加速验证，关注先进制程投 产进度——爱建智能制造周报 （2025/05/06-2025/05/09）》2025-05-12 王凯 S0820524120002 021-32229888-25522 wangkai526@ajzq.com 行业及产业 具身认知智能与运动控制协同进阶 ——爱建智能制造周报（2025/06/09-2025/06/13） 投资建议： 1）建议关注 T 链中业绩确定性较强的核心公司，【拓普集团】【震裕科技】【隆盛科技】； 2） ...