变道决策规划专家-XC

Job Description

1. In the absence of map and SD map data, design and implement decision-making and planning algorithms for lane change based on search and game theory, aiming to propose an algorithm-based method to solve the optimal gap finding problem.

2. Work closely with cross-functional teams to integrate decision-making and planning algorithms into the overall autonomous driving system architecture.

3. Analyze and solve real-world challenges related to L2++ autonomy, including noisy inputs and unstable predictions, to obtain optimal results and stable, robust decision/gap selection logic in the current frame.

4. Continuously optimize and enhance decision-making and planning algorithms based on research papers 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. Possess the ability and self-awareness to read and reproduce papers to solve complex problems.

Qualifications

1. Master's degree or above in computer science, electronic engineering, mathematics, or a related major.

2. An expert in artificial intelligence and autonomous driving, proficient in autonomous lane change decision algorithms, skilled in POMDP, MCTS, reinforcement learning (RL), and AI search methods, tree, and graph search methods.

3. Possess a strong background in C++ multithreading and embedded systems. Familiar with A* search, dynamic programming (DP), game theory for multi-agent systems, and graph search/tree search.

4. Requires in-depth thinking about lane change KPIs, especially system-level KPIs and subsystem KPIs, and the ability to formulate systematic and scenario-based KPIs.

5. Familiar with numerical optimization principles and engineering implementation, such as graph search and tree search.

6. Proficient in programming languages such as C++ and Python, with strong algorithm development and implementation coding capabilities.