Parallel Trajectory Optimization

Parallel trajectory optimization aims to efficiently compute multiple potential trajectories simultaneously, improving the speed and robustness of motion planning, particularly in complex or uncertain environments like autonomous driving. Current research focuses on incorporating safety constraints using barrier functions and homotopic methods, leveraging parallel algorithms like ADMM and employing advanced preconditioning techniques to accelerate the underlying linear system solves. These advancements enhance the safety, efficiency, and reliability of autonomous systems by enabling real-time decision-making and planning in dynamic scenarios, with applications ranging from robotics to autonomous vehicles.