Zhucheng Li

Master's

Computer Science

Momenta

R&D Intern

methods and boosting development efficiency by 5x. • Designed and implemented a Cumulative Decoder, reconstructing the Decoder Head for multimodal trajectories, resulting in a 48% improvement in recall and a 33% increase in mADE50 on target scenario datasets. • Created and integrated Landmark-Loss functions to optimize the end-to-end trajectory prediction model, resulting in a significant performance boost, with a 28% improvement in precision and a 36% increase in recall. • Addressed real-world challenges such as trajectory compliance and centering in road tests by employing data mining techniques and optimizing key features, completing self-closed-loop verification and deployment.