第三十一届中国汽车工程学会年会暨展览会（SAECCE 2024）定于 2024年11月11-14日在 中国重庆·科学会堂召开。

SAECCE 2024以 “智能涌现，迈进加速变革新阶段”为主题，将聚焦汽车产业创新重大需求，瞄准未来的前沿性、颠覆性技术发展，重点突出汽车科技前沿、关键技术创新、工程技术研发与应用实践以及产业合作等，着力推动汽车科技进步、跨领域技术融合创新及产业合作，加速汽车人才发展，引领汽车产业高质量发展。

SAECCE 2024预计举办 1场开幕式及全体大会、 4场主论坛、近 90场专题论坛及技术研讨会、同期举办2024中国汽车工程学会科学技术奖励大会、2024中国汽车技术首脑（CTO）闭门峰会、2024电动汽车智能底盘大会、2024国际汽车智能共享出行大会等 10余场同期会议。

技术展览面积 15000平米，预计吸引来自政府、高校及科研机构、整车、汽车零部件上下游企业及科技创新企业参会代表及专业观众 5000余人，展览观众达到 20000人次。

SAECCE 2024

演讲预告

重磅嘉宾 美国宾夕法尼亚州立大学Sharon X. Huang教授确认出席SAECCE 2024，并将在 “人工智能主论坛-AI+EV：

人工智能与未来移动出行革命的碰撞”发表主旨演讲。

Speaker’s Profile

Dr. Sharon X. Huang is a distinguished researcher and professor at Penn State’s College of Information Sciences and Technology, where she holds the David Reese Professorship. Her research focuses on artificial intelligence, particularly in computer vision and generative AI, with significant contributions to image and video generation, salient object detection, 3D scene reconstruction, and multimodal AI. Dr. Huang has published over 200 scholarly papers, with over 21,300 citations, achieving an h-index of 46 and an i10-index of 124. She serves as co-general chair for the WACV 2025 conference and has held area chair and editorial roles for leading AI, computer vision, and medical imaging conferences and journals, including CVPR, ECCV, AAAI, MICCAI, MedIA, and CMIG. Dr. Huang earned her bachelor’s degree in computer science from Tsinghua University and both her master’s and doctoral degrees in computer science from Rutgers University.

Key Points

1. Advancing from Task-Oriented AI to AGI: Transitioning from narrow, task-specific AI to AGI involves overcoming challenges related to adaptability, memory, and reasoning to achieve human-like cognitive flexibility.

2. Challenges in AGI Development: Key obstacles include developing flexible learning, autonomous improvement, ethical frameworks, and scalable computational resources necessary for general intelligence.

3. Role of Prompt Engineering in AGI: Prompt words currently guide AI responses but hold potential for broader applications in AGI, where they could support more adaptive, multi-task systems.

4. Autonomous Driving as a Path to AGI: Autonomous driving exemplifies AGI requirements, combining scene understanding, real-time adaptability, and dynamic decision-making in complex environments.

5. 3D Scene Understanding in AGI: Scene understanding, including object detection and interpretation, is fundamental for AGI, enabling AI to process visual information and make contextually aware decisions.

大会日程概览

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会议日程：中国汽车工程学会 赵萌许

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