驭模有道+智胜未来+-+吉利在2024GTC上分享-04-自动驾驶

陈勇 博士Dr. Yong Chen吉利汽车 GEELY AUTO驭模有道 智胜未来G u i d e t h e m o d e l w i t h w i s d o m s u r p a s s i n g t h e f u t u r e大模型助力智能化变革F o u n d a t i o nm o d e lf a c i l i t a t et h ei n t e l l i g e n tt r a n s f o r m a t i o n由粗犷的硬件驱动体验转向数据算法驱动体验Shifting from a raw hardware-driven experience to a data algorithm-driven experience.基于用户体验驱动技术价值创造，使智能化设计回归理性Create technological value driven by user experience, bringing intelligent design back to rationality.技术驱动创新Technology-driven innovation系统集成化System integrationAI算法迭代AI algorithm iteration数据闭环Data Closure Loop大模型Foundation model……1R1V3R1V5R5V5R9V5R10V5R11V+1L/3L5R12V+2L5R13V+3L30T100-200T200-500T500-800T1000T+1000+TOPSComputational PowerHighBeginnerMiddle硬件驱动体验Hardware-driven experience数据算法驱动体验Data-driven experience algorithmsBEV去硬件Hardware reduction轻地图Reduce high-precision mapsE2EODD increaseMore…大模型发展核心四要素:3+1The four core elements of the development of foundation models: 3+1人工智能时代：涌现式+继承式A r t i f i c i a lI n t e l l i g e n c eE r a ：E m e r g e n t+I n h e r i t e d◼大数据平台◼Big Data Platform◼星睿智算中心◼Xingrui Intelligence Computing Center◼LLM / Multimodal Model◼AI-DRIVE智能驾驶大模型◼AI-DRIVE Intelligent Driving Foundation Model◼汽车行业的知识积累◼Knowledge Accumulation in the Automotive Industry数据Data算力Computational Power算法Algorithm先 验 知 识Previous Knowledge大模型助力智能化变革Foundation models boost the intelligent transformation◼大语言模型 LLM◼多模态模型 Multimodal Model ◼GUI+MUI◼VUI+NUI◼代码生成 Code Generation◼摘要生成/知识问答 Abstract generation /Knowledge Q&A◼ + LM◼LM +丰富的生成内容Rich Generative Content全新的交互体验Completely new interactive experience先进的生产力工具Advanced productivity tools新的开发范式New development paradigm百 模 大 战Battle of a Hundred Molds产品需要有市场和价值定位The product needs to have a market and value positioning新技术是来解决问题的或创造价值增量的New technologies are developed to solve problems or to create incremental value.用户场景决定技术价值User scenarios determine the value of technologyW ea l ln e e df o u n d a t i o nm o d e l sD ow ea l ln e e df o u n d a t i o nm o d e l s ?智能驾驶核心要素Key Elements of Intelligent Driving◼ 在复杂道路和拥堵的交通流条件下，接管率高High takeover rate in complex road and congested traffic conditions.◼ 智能驾驶体验未实现全驾驶场景覆盖，体验不连贯Incomplete coverage of driving scenarios in intelligent driving experience, leading to inconsistent experiences.◼ 大量冗余传感器及技术，系统成本居高不下High system costs due to redundant sensors and technologies.◼ 大规模的数据采集标注、软硬件设计开发Large-scale data collection, annotation, software, and hardware design and development.◼ 智能驾驶长尾效应带来的安全困境Safety Dilemma Caused by the Long Tail Effect of Intelligent Driving.◼ 感知大多数还停留在标注阶段，缺少认识能力Most perception remains in the annotation stage, lacking cognitive ability.安全：安全≠安全感Safety: Safety ≠ Sense of Safety体验：有没有≠好不好Experience: Presence ≠ Quality成本：去冗余≠去体验Cost: Redundancy ≠ Experience数据驱动模型迭代和体验升级D a t a - d r i v e nm o d e li t e r a t i o na n de x p e r i e n c ee n h a n c e m e n t智能驾驶大模型应用Applications of the foundation model on autonomous driving解 决 关 键 核 心 问 题 并 创 造 价 值Addressing Key Core Issues and C