Requirements
● Bachelor's or Master's degree in Computer Engineering, Automation, Automobile, Aviation, or other related fields.
● Familiar with typical automatic driving system architecture or robot system, proficient in decision-making, planning, and control-related algorithms, understanding its advantages and disadvantages and applicable scenarios.
● Knowledge of convex optimization, and numerical optimization.
● Knowledge of dynamic programming, A*, D*, HybridA*.
● Knowledge of POMDP/MDP-based approaches.
● Knowledge of probabilistic graphic models, including factor graphs, bayesian networks, and conditional random fields.
● Familiar with basic robot system software development tools (Linux, Git, ROS, gdb) and methods (OOP, unit testing, CI/CD).
● Proficient in C/C++ programming and common data structure algorithms.
● Understand deep learning and reinforcement learning-related theories.
● Those who have read multiple open-source libraries or have written their own trajectory planning-related libraries are preferred.