Bosch China Innovation and Software Development Campus

博世创新与软件开发中心

    博世创新软件开发（无锡）有限公司针对交通出行的电动化、自动化、互联化、个性化，提供面向未来的创新技术和前沿解决方案，加速针对中国市场的技术战略的实现和发展。博世软件中心主要发展方向包括智能网联汽车、智能座舱、自动驾驶、车路云协同、工业4.0、人工智能大数据、智能家居、嵌入式软件服务。博世中国创新与软件开发中心以软件为客户赋能，在汽车自动驾驶、氢燃料电池、重卡电驱动桥、多合一的控制器等多个领域取得创新研发成果。

Highly experienced AI & Autonomous Driving Engineer specializing in Lon-decision and trajectory-making algorithms for autonomous driving especially for mapless. Expertise in planning, control, and optimization techniques such as MPC/LQR/QP, POMDP, Monte Carlo Tree Search (MCTS), Reinforcement Learning (RL), and AI-based search methods. Strong background in multi-threaded C++ development, numerical optimization, and embedded systems. Passionate about developing robust and efficient decision-making algorithms to advance L2++ autonomy.

1. Design and implement decision planning algorithms on mapless for lon decision and lon trajectory calculation, aimed to have a algothrim-based method to solve lon problems, especially for merging\intersection\open space environment.
2. Collaborate closely with cross-functional teams to seamlessly integrate decision planning and trajectory algorithms into the overall autonomous driving system architecture.
3. Analyze and address real-world challenges related to L2++ autonomy, including noisy input, unstable prediction, multiple predictions, with best result for the current frame and a stable/robust interactive decision.
4. Continuously optimize and enhance gaming algorithms\optimization lon trajectory to improve overall system efficiency, adaptability, and reliability.
5. Participate in project planning, milestone setting, and progress tracking to ensure timely delivery of autonomous driving features for mass production.
6. Ability and self-awareness to read and reproduce papers to solve complex problems.

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• Master’s degree or above in computer science, electronic engineering, mathematics, or related fields.
• Senior AI & Autonomous Driving Engineer with expertise in decision-making algorithms for lon decision, specializing in POMDP, MCTS, Tree search\graph search.
• Strong background in C++ multi-threading, embedded system. Familiarity with A* Search, Dynamic Programming (DP), Game Theory for Multi-Agent Systems and Graph search/Tree search
• Need to have a deep think of the lon trajectory planning, especially for system-level KPI and Sub-system KPI.
• Familiarity with numerical Optimization Principles and Engineering Implementation, e.g. such as Graph Search, Tree Search
• Proficiency in programming languages such as C++, Python, etc., with strong coding skills for algorithm development and implementation.