⾃动驾驶算法研究与开发（战略实习⽣）-CR

1. 01

   Investigate feasible technologies based on project

, including behavior prediction, trajectory generation, latent representation learning, generative models, motion planning, and safety-aware decision-making for autonomous driving.

2. 02

   Explore latent model-based methods for autonomous driving, including semantic latent space modeling, intent representation, risk-aware behavior encoding, controllable trajectory generation, and prior-posterior representation alignment.

3. 03

   Explore the integration of generative trajectory models with planning modules, including candidate trajectory generation, trajectory scoring, risk evaluation, safety constraint modeling, and planner output refinement.

4. 04

   Investigate the application of behavior generation and planning algorithms in safety-critical driving functions, such as AEB, AES, collision avoidance, risk-aware maneuver generation, and emergency trajectory planning.

5. 05

   Implement, train, and evaluate relevant algorithms on autonomous driving datasets or simulation platforms. Conduct experiments including baseline reproduction, ablation studies, metric evaluation, visualization, and failure case analysis.

6. 06

   Synthesize research and development outcomes into patents, technical reports, and academic papers.

7. 07

   Provide timely feedback on development progress and technical issues, actively discuss solutions with the team, and document key findings, experiments, and conclusions.

8. 01

   基于项⽬需求，调研⾃动驾驶⾏为预测、轨迹⽣成、潜空间建模、⽣成式模型、运动规划以及安全决策等相关技术路 线。

9. 02

   探索基于 Latent Model 的⾃动驾驶建模⽅法，包括语义潜空间建模、⾏为意图表⽰、⻛险感知⾏为编码、可控轨迹⽣ 成以及先验-后验表⽰对⻬等⽅向。

10. 03

    探索⽣成式轨迹模型与 Planner 的结合⽅式，包括候选轨迹⽣成、轨迹打分、⻛险评估、安全约束建模以及规划结果 优化等。

11. 04

    探索⾏为⽣成与轨迹规划算法在安全关键功能中的应⽤，包括 AEB、AES、碰撞规避、⻛险感知机动⽣成以及紧急避 险轨迹规划等场景。

12. 05

    完成相关算法的实现、训练与评估，在⾃动驾驶数据集或仿真平台上开展实验，包括基线复现、消融实验、指标评 估、结果可视化以及失败案例分析。

13. 06

    汇总研究和开发成果，形成专利、技术报告以及学术论⽂。

14. 07

    及时反馈开发进度和遇到的问题，与团队积极讨论解决⽅案，并记录关键实验结果、技术结论和经验沉淀。 岗位要求: 具备以下任⼀⽅向的研究或项⽬经历：深度学习、计算机视觉、⾃动驾驶、轨迹预测、运动规划、⽣成式模型或 Latent Model 相关⽅法。 具备较扎实的机器学习与深度学习基础。 具备良好的编程思维和⼯程实现能⼒，熟悉 Python、PyTorch 以及 AI-assisted coding / Vibe Coding ⼯作流者优先。 熟悉⾄少⼀种相关⼯具、数据集或平台者优先，如 OpenCV、ROS、CARLA、nuScenes、Waymo、Argoverse、 nuPlan 或类似资源。 计算机、⼈⼯智能、⾃动化、机器⼈、电⼦⼯程、数学、⻋辆⼯程或相关理⼯科专业背景。 乐于学习新的研究⽅向、⼯具和平台，具备较强的独⽴性、主动性和问题解决能⼒。 具备良好的英⽂读写能⼒，尤其是学术论⽂阅读和技术⽂档整理能⼒。 能尽快⼊职者优先，能够连续实习 6 个⽉以上者优先。 Requirements: Experience with at least one of the following areas: deep learning, computer vision, autonomous driving, trajectory prediction, motion planning, generative models, or latent model-based methods. Solid understanding of machine learning and deep learning fundamentals. Possess strong programming thinking and engineering implementation ability. Familiarity with Python, PyTorch, and AI-assisted coding / Vibe Coding workflows is preferred. Familiarity with at least one related tool, dataset, or platform, such as OpenCV, ROS, CARLA, nuScenes, Waymo, Argoverse, nuPlan, or similar resources, is preferred. Major in computer science, artificial intelligence, automation, robotics, electronic engineering, mathematics, vehicle engineering, or related fields. Willing to learn new research directions, tools, and platforms. Self-dependent, highly motivated, and with a problemsolving mentality. Good English reading and writing capability is required, especially for academic paper reading and technical documentation. Ability to join as soon as possible is preferred. Candidates who can stay for more than 6 months will be strongly preferred. 福利: 500强外企，跨文化交流，大平台新视野。 公司有食堂，工作氛围轻松愉快，可优先接触内部转正机会！ 与行业专家一对一指导，快速提升专业技能和职场竞争力。 在世界百强外企中积累宝贵的实习经验，为你的简历增色。 投递岗位即默认同意博世集团网上招聘相关隐私协议 协议地址：请通过访问博世中国官方主页，博世在中国＆gt;加入博世＆gt;工作机会，在网上招聘数据隐私声明