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Open nowPosted 131 days ago

新平台感知算法工程师

xiaopeng/index1,586 open roles

Where
Guangzhou, Guangdong, Chinese Mainland
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Your applicationOpen now新平台感知算法工程师xiaopeng/index · Guangzhou, Guangdong, Chinese Mainland

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The clock on this job

Early applications get read.

11.8% of postings close within 7 days. Measured by our own scanner across the market. xiaopeng/index postings stay open a median of 1 days.

Share of postings closed within
  1. 2.1%1 day
  2. 4.4%3 days
  3. 11.8%7 days
  4. 18.5%14 days
  5. 35.5%30 days
  6. 45.1%60 days
This job: posted 131 days ago

xiaopeng/index median: 1 days open

The posting

1. 负责新平台下感知算法研发，主导多光谱视觉、毫米波雷达与声学传感器（超声波等）等多模态数据的前融合策略。解决异构数据在时空对齐与特征级融合中的技术瓶颈，构建全天候、多维度的环境感知能力； 2. 负责建立多模态数据的标注体系，定义标签分类、标注流程及质量评估指标。通过工具链管理标注任务，构建数据标注质量控制机制，确保训练数据的语义一致性与场景覆盖率； 3. 基于 BEV + Transformer 架构，重点解决多视角感知在复杂场景下的几何一致性问题；结合先验地图信息，输出高精度的实时环境表征，为下游规划控制提供可靠的时空基准； 4. 引入并落地 Occupancy Network (OCC) 技术，通过3D体素化建模突破传统检测框的高度信息缺失限制，实现对非结构化通用障碍物（如路面/水面/空中的异形漂浮物、低矮障碍）的精准识别与分割； 5. 针对高动态背景、强反射介质干扰及弱纹理等长尾场景，设计专项抗干扰算法与数据增强策略，显著提升系统在恶劣环境下的感知置信度与稳定性。

1. 计算机、自动化、机器人等相关专业硕士及以上学历，3年以上自动驾驶或机器人感知算法经验； 2. 核心技术栈： - 精通C++/Python，熟练掌握PyTorch/TensorFlow框架； - 必须具备 BEV + Transformer 实战经验，深入理解时序融合与空间对齐原理； - 熟悉 Occupancy Network / Voxel-based 感知范式，有相关落地经验者优先； 3. 融合与部署能力： - 熟悉多传感器标定与融合算法，有视觉+雷达/声学融合经验者极佳； - 具备扎实的模型部署能力，熟悉TensorRT/ONNX及嵌入式Linux开发环境； 4. 加分项： -有CVPR/ICCV/ECCV等顶会论文或知名开源项目贡献者优先； -有机器人、飞行器、航行器或特殊环境（雪地/矿区）感知项目经验优先。

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