（文/观察者网 张家栋 编辑/高莘） 借助电动化带来的产业赋能加持，中国汽车从4年前开始重新开启出口增量的新征程，2023年，中国首 次超越日本，成为全球最大的汽车出口国。 但长时间以来，"出海"几乎成了中国汽车全球化的代名词，在此过程中，无论是整车出口，还是在海外 市场建厂，全球各地消费者看到的，仍是一辆辆来自中国的汽车，但如何深入地向海外消费者展现品牌 价值，又如何结合海外市场的本土化来打造技术优势，仍是中国车企在不断增长的出口数字中，需要探 寻的全球化道路。 立足沃尔沃对于安全的开发理念和标准，瑞典研发中心在安全技术领域的研发成果尤为显著，结合中国 智能制造的技术与品质，吉利也就此打造出独有的全域安全体系。 在直播中，吉利展示了极氪9X的部分安全设计与参数，该车型车身扭转刚度达41600N·m/deg，搭载"十 宫格"门槛梁、"第三吸能盒"等专利安全设计，在50km/h正面柱碰、105km/h后碰等严苛测试中均表现优 异。 对于智能化时代的安全考量，瑞典研发中心则打造出"失效安全"方案，以及基于现代加密算法的车辆网 络安全保护系统。 而作为中国汽车全球化布局的代表，在全球拥有5大造型中心、五大工程研发中 ...

Core Insights - The article introduces VLN-PE, a physically realistic platform for Vision-Language Navigation (VLN), addressing the gap between simulated models and real-world deployment challenges [3][10][15] - The study highlights the significant performance drop (34%) when transferring existing VLN models from simulation to physical environments, emphasizing the need for improved adaptability [15][30] - The research identifies the impact of various factors such as robot type, environmental conditions, and the use of physical controllers on model performance [15][32][38] Background - VLN has emerged as a critical task in embodied AI, requiring agents to navigate complex environments based on natural language instructions [6][8] - Previous models relied on idealized simulations, which do not account for the physical constraints and challenges faced by real robots [9][10] VLN-PE Platform - VLN-PE is built on GRUTopia, supporting various robot types and integrating high-quality synthetic and 3D rendered environments for comprehensive evaluation [10][13] - The platform allows for seamless integration of new scenes, enhancing the scope of VLN research and assessment [10][14] Experimental Findings - The experiments reveal that existing models show a 34% decrease in success rates when transitioning from simulated to physical environments, indicating a significant gap in performance [15][30] - The study emphasizes the importance of multi-modal robustness, with RGB-D models performing better under low-light conditions compared to RGB-only models [15][38] - The findings suggest that training on diverse datasets can improve the generalization capabilities of VLN models across different environments [29][39] Methodologies - The article evaluates various methodologies, including single-step discrete action classification models and multi-step continuous prediction methods, highlighting the potential of diffusion strategies in VLN [20][21] - The research also explores the effectiveness of map-based zero-shot large language models (LLMs) for navigation tasks, demonstrating their potential in VLN applications [24][25] Performance Metrics - The study employs standard VLN evaluation metrics, including trajectory length, navigation error, success rate, and others, to assess model performance [18][19] - Additional metrics are introduced to account for physical realism, such as fall rate and stuck rate, which are critical for evaluating robot performance in real-world scenarios [18][19] Cross-Embodiment Training - The research indicates that cross-embodiment training can enhance model performance, allowing a unified model to generalize across different robot types [36][39] - The findings suggest that using data from multiple robot types during training leads to improved adaptability and performance in various environments [36][39]