Shengjun Chen

Title: Innovations by Shengjun Chen in Autonomous Driving Technology

Introduction

Shengjun Chen is a prominent inventor based in Guangzhou, China. He has made significant contributions to the field of autonomous driving technology, holding 2 patents that focus on path planning and obstacle detection systems. His work is pivotal in enhancing the safety and efficiency of autonomous vehicles.

Latest Patents

One of his latest patents is a "Path planning method, system and device for autonomous driving." This invention involves acquiring environment perception information and vehicle positioning data to generate sub-paths that meet vehicle constraints. The method includes performing collision detection on candidate sub-paths and utilizing an A* search algorithm to obtain a local path for the vehicle.

Another notable patent is the "Method, apparatus and system for detecting obstacle collision in automatic parking path." This invention provides a geometric approach to detect obstacle collisions in both circular arc and straight line paths. The system includes a collision detection module that utilizes path, vehicle, and obstacle contours to ensure safe navigation during automatic parking.

Career Highlights

Shengjun Chen has worked with notable companies such as Guangzhou Xiaopeng Motors Technology Co., Ltd. and Guangzhou Xiaopeng Autopilot Technology Co., Ltd. His experience in these organizations has allowed him to apply his innovative ideas in real-world applications, contributing to advancements in the automotive industry.

Collaborations

Throughout his career, Shengjun has collaborated with talented individuals, including Zhiguang Xiao and Jianming Lai. These partnerships have fostered a creative environment that encourages the development of cutting-edge technologies in autonomous driving.

Conclusion

Shengjun Chen's contributions to autonomous driving technology through his patents and career experiences highlight his role as an influential inventor in the field. His innovative methods for path planning and obstacle detection are essential for the future of safe and efficient autonomous vehicles.