Ludong Sun
About Ludong Sun
Ludong Sun is a Principal Engineer and Team Lead in Vehicle Control at Motional, with extensive experience in designing control systems for autonomous vehicles. He has held various engineering roles at Aptiv, Delphi, and Stanford University, and has a strong academic background with degrees from the University of Minnesota and Stanford University.
Current Role at Motional
Ludong Sun serves as a Principal Engineer and Team Lead in Vehicle Control at Motional. He has been in this position since 2020, contributing to the development of advanced vehicle control systems. His role involves overseeing projects related to trajectory and path following for autonomous vehicles, ensuring the implementation of effective control strategies.
Previous Experience at Aptiv
Prior to his current role, Ludong Sun worked at Aptiv as a Senior Engineer and Team Lead in Vehicle Control from 2017 to 2019. He was based in Pittsburgh, Pennsylvania, where he focused on vehicle dynamics and chassis control systems. His experience at Aptiv laid a strong foundation for his expertise in autonomous vehicle technology.
Background in Engineering and Research
Ludong Sun has a diverse background in engineering and research. He worked at Delphi as a System Engineer from 2015 to 2016 and later as an Engineer in Vehicle Control from 2016 to 2017. His early career included a role as an Undergraduate Research Assistant at the University of Minnesota from 2011 to 2013, where he gained valuable research experience.
Education and Specialization
Ludong Sun holds a Bachelor of Science degree from the University of Minnesota-Twin Cities, which he completed from 2009 to 2013. He furthered his education by obtaining a Master of Science from Stanford University between 2013 and 2015. His studies focused on control systems, vehicle dynamics, and mechatronics, equipping him with specialized knowledge in designing control systems for autonomous vehicles.
Technical Skills and Expertise
Ludong Sun specializes in designing control systems using classical, optimal, and model predictive techniques. He is skilled in functional safety and redundancy systems for full automation. His technical expertise includes embedded software and hardware development, particularly with vehicle ECUs and Infineon Aurix. He is also experienced in using AUTOSAR for automotive software architecture and conducts hands-on hardware-in-the-loop (HIL) testing and vehicle calibration.