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Open nowPosted 7 hours ago

自动驾驶导航认知算法工程师

li/referral790 open roles

Where
Beijing, Chinese Mainland
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Your applicationOpen now自动驾驶导航认知算法工程师li/referral · Beijing, Chinese Mainland

  1. YouYes, apply to this one.

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The clock on this job

Early applications get read.

12.3% of postings close within 7 days. Measured by our own scanner across the market. li/referral postings stay open a median of 1 days.

Share of postings closed within
  1. 2.1%1 day
  2. 4.5%3 days
  3. 12.3%7 days
  4. 19.7%14 days
  5. 35.6%30 days
  6. 58.0%60 days
This job: posted 7 hours ago

li/referral median: 1 days open

The posting

1. 负责面向自动驾驶导航任务的 VLA（视觉-语言-动作）场景认知模型研发，开展模型训练、微调与优化，使模型能够联合理解视觉场景、地图、导航路线与驾驶任务，将导航意图与实际道路环境准确关联。 2. 设计并构建引导 VLA 跟随导航完成驾驶动作所需的中间结果与导航引导信息，包括路线与车道对应关系、目标道路与目标车道、关键路口及转向意图、变道时机与可行区间、道路通行约束等，为驾驶动作生成提供可学习、可评测的结构化依据。 3. 负责 SD 地图、高精地图及不同层级导航信息的接入、解析、表征与融合，探索模型对道路、车道、拓扑、交通标志等地图元素的理解与生成，提升地图信息与车辆位置、实时视觉观测之间的时空一致性。 4. 建设在线实时地图理解能力，结合车端感知补充施工、封路、临时交通管制、车道占用等动态信息，处理地图缺失、过期或与实际道路不一致的问题，为导航执行提供实时、可信的场景认知结果。 5. 建设基于大模型的导航 AutoLabel（自动标注）与数据闭环，融合驾驶视频、车辆轨迹、地图和导航信息，生成路线关联、车道选择、路口通行、导航遵循及偏航事件等训练标签，并建立质量校验、难例挖掘与迭代机制。 6. 针对跟随导航、复杂路口通行、提前选道、错过路口、导航偏航及路线重规划等问题，探索地图与视觉融合、时序建模、多模态对齐等创新方法，提升模型对导航意图的理解、执行与偏航恢复能力。 7. 与地图导航、感知、规划控制、数据闭环及仿真评测团队协同，建立导航理解、中间结果准确性、路线遵循率与偏航恢复等评测体系，推动模型能力在闭环驾驶场景中的验证、优化与交付。

1. 计算机、人工智能、自动化、车辆工程、电子、数学、地理信息等相关专业本科及以上学历。 2. 具备大模型或自动驾驶项目交付经验，能够围绕真实业务问题完成方案设计、数据建设、模型训练、评测分析与工程落地，并对交付效果负责。 3. 具备充足的地图与导航业务实践经验，熟悉 SD 地图、高精地图的常见数据格式、组织方式及道路与车道拓扑表达，理解坐标系转换、定位匹配、路线关联、地图更新及地图质量问题。 4. 熟悉路线级、道路级、车道级等不同层级地图导航信息的使用方式，理解不同地图精度、信息完整性及导航粒度下的能力边界，能够设计适配不同地图与导航条件的模型输入和引导信息。 5. 在模型处理或生成地图元素、在线实时地图理解、感知与地图融合、动态地图信息补充等方向具备深入实践，能够处理地图与观测不一致、拓扑歧义、临时道路变化及信息置信度等问题。 6. 熟悉大模型驱动的导航 AutoLabel 方法，具备多源数据对齐、自动标注、标签质量评估与数据闭环经验；对如何通过创新方法提升导航遵循、偏航识别与偏航恢复能力有独到见解，能够设计实验并验证效果。 7. 具备扎实的机器学习与深度学习基础，熟悉 VLA、VLM 或其他多模态模型的训练、微调与评测方法，熟练使用 Python、PyTorch 等工具，具备较强的编码、实验设计和问题定位能力。 8. 能够与上下游团队高效协作，将地图导航和驾驶场景中的业务问题转化为清晰的模型任务、数据需求与评测指标。 加分项 1. 有导航辅助驾驶、城市 NOA、端到端自动驾驶或 VLA 项目的量产与交付经验，参与过导航遵循、路口选道、偏航恢复等能力建设。 2. 有 SD / 高精地图生产、更新或应用经验，熟悉 OpenDRIVE、Lanelet2 或商业地图供应商的数据格式与接口。 3. 有地图元素生成、道路与车道拓扑推理、在线建图或轻地图 / 无高精地图方案经验。 4. 有大规模多模态数据自动标注、教师模型蒸馏、指令微调、多模态对齐或闭环训练经验，并形成过可衡量的业务收益。 5. 有复杂路口、匝道、施工改道、导航与感知冲突等长尾场景的专项优化经验，能够结合仿真、回放与实车结果持续改进模型。 我们希望你是这样的人 - 能从“如何让车辆理解并可靠执行导航”这一业务目标出发，贯通地图、感知、认知与驾驶动作，而不局限于单一模块。 - 对大模型与地图导航结合有自己的判断，既敢于提出创新方法，也重视数据质量、实验验证和实际交付。 - 能正视地图与真实道路的差异，在复杂、动态和长尾场景中持续打磨模型能力，兼顾导航目标、道路约束与驾驶安全。

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