Verified current Job

ADAS仿真测试PO_XC

1.负责沟通协调测试计划,进度,问题,报告; 2.负责构建智能驾驶 数据回放场景库,建立标准化场景分类与标签规则; 3.基于实车数据,完成典型功能场景(AEB / LKA / ACC / NOA 等)提取与问题整理; 4.负责 Driving 数据在仿真/回放平台的测试回归...

Job Full source details
Bosch Group Lian Yun Gang Shi, Jiang Su Sheng, China Source published Aug 28, 2026 Verified 3 weeks ago Reference REF295055I
✓ 92% verification score · Source: Bosch Group Careers · Always confirm final requirements on the original source.
Complete source information imported The available role or programme description, requirements, benefits and source facts were imported from the public official endpoint and formatted for reading.
EmploymentFull-time
CountryChina
Job functionEngineering
IndustryComputer Software
Experience levelAssociate

Overview

1.负责沟通协调测试计划,进度,问题,报告; 2.负责构建智能驾驶 数据回放场景库,建立标准化场景分类与标签规则; 3.基于实车数据,完成典型功能场景(AEB / LKA / ACC / NOA 等)提取与问题整理; 4.负责 Driving 数据在仿真/回放平台的测试回归(支持 HIL / Replay 平台); 5.建立场景覆盖率统计与 KPI 评估体系,支撑功能验证回归与版本迭代; 6.设计并维护场景库数据解析自动化处理脚本; 7.有丰富的AEB经验加分;

Full job description

About the company

1.负责沟通协调测试计划,进度,问题,报告;

2.负责构建智能驾驶 数据回放场景库,建立标准化场景分类与标签规则;

3.基于实车数据,完成典型功能场景(AEB / LKA / ACC / NOA 等)提取与问题整理;

4.负责 Driving 数据在仿真/回放平台的测试回归(支持 HIL / Replay 平台);

5.建立场景覆盖率统计与 KPI 评估体系,支撑功能验证回归与版本迭代;

6.设计并维护场景库数据解析自动化处理脚本;

7.有丰富的AEB经验加分;

Full job description

1.本科及以上学历,车辆工程 / 自动化 / 电子 / 计算机等相关专业;

2.熟悉智能驾驶功能验证流程;

3.熟悉常见 ADAS 功能逻辑:AEB、ACC、LCC、LKA、NOA等;

4.熟练使用 Python 进行数据处理与自动化开发,熟悉 Excel 自动化处理(pandas / openpyxl);

5.熟悉常见数据格式:MF4、CAN、Rosbag;

6.有数据集构建经验,有回放仿真台架经验;

7.有高阶驾驶辅助量产经验。

Qualifications and requirements

  • 本科及以上学历,车辆工程 / 自动化 / 电子 / 计算机等相关专业
  • 熟悉智能驾驶功能验证流程
  • 熟悉常见 ADAS 功能逻辑:AEB、ACC、LCC、LKA、NOA等
  • 熟练使用 Python 进行数据处理与自动化开发,熟悉 Excel 自动化处理(pandas / openpyxl)
  • 熟悉常见数据格式:MF4、CAN、Rosbag
  • 有数据集构建经验,有回放仿真台架经验
  • 有高阶驾驶辅助量产经验

Requirements & qualifications

  • 本科及以上学历,车辆工程 / 自动化 / 电子 / 计算机等相关专业
  • 熟悉智能驾驶功能验证流程
  • 熟悉常见 ADAS 功能逻辑:AEB、ACC、LCC、LKA、NOA等
  • 熟练使用 Python 进行数据处理与自动化开发,熟悉 Excel 自动化处理(pandas / openpyxl)
  • 熟悉常见数据格式:MF4、CAN、Rosbag
  • 有数据集构建经验,有回放仿真台架经验
  • 有高阶驾驶辅助量产经验

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Verification notes

Discovered from the employer’s public SmartRecruiters Posting API. The public detail endpoint was fetched and its job-ad sections were normalized into a complete, safe candidate-facing description.

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