Xiaobo Ren

Title: Innovations by Xiaobo Ren in Vehicle Operation Planning

Introduction

Xiaobo Ren is an accomplished inventor based in Fremont, CA. He has made significant contributions to the field of vehicle operation planning through his innovative patent. His work focuses on enhancing the relevance of objects in relation to vehicle actions, which is crucial for the advancement of autonomous driving technologies.

Latest Patents

Xiaobo Ren holds a patent for a "Machine-learned model for detecting object relevance to vehicle operation planning." This patent describes a machine-learned architecture that utilizes a convolutional neural network, graph neural network, and/or multi-layer perceptron. The architecture determines a relevance score associated with an object, indicating whether it is likely to impact the operations of a vehicle. The model can leverage scene information and object tracking to assess the relevance score effectively.

Career Highlights

Xiaobo Ren is currently employed at Zoox, Inc., a company dedicated to developing autonomous vehicle technologies. His work at Zoox has positioned him as a key player in the innovation of machine learning applications in the automotive industry. With a focus on improving vehicle operation planning, he continues to push the boundaries of what is possible in this rapidly evolving field.

Collaborations

Xiaobo has collaborated with notable colleagues such as Samir Parikh and Gowtham Garimella. Their combined expertise contributes to the innovative environment at Zoox, fostering advancements in vehicle technology.

Conclusion

Xiaobo Ren's contributions to vehicle operation planning through his innovative patent exemplify the potential of machine learning in the automotive industry. His work not only enhances vehicle safety but also paves the way for future advancements in autonomous driving.