Leo Kim

Leo Kim

ADAS Software Engineer - Motion Planning

Specialized in advanced driver assistance systems, autonomous vehicles, and mission planning algorithms. Currently leading collision avoidance systems development at General Motors.

4+

Years Experience

In ADAS Control & Software

97%

Vehicle Coverage

For MY25 & MY26 Models

71%

Performance Boost

In False Braking Reduction

Professional Experience

Embedded Software Engineer - Motion Planning

General Motors - ADAS Mission Planning Group

Feb 2023 – Present
  • Led front and rear impact mitigation ring for collision avoidance systems, covering 97% of 3 million MY25 & 26 vehicles with pedestrian, collision imminent, and intersection components in a CI/CD pipeline.
  • Developed vehicle trajectory estimation and object selection algorithms using MATLAB and C, reducing false braking events by 71% and improving system performance.
  • Analyzed vehicle data to improve component robustness against sensor inaccuracies, using MATLAB and Python.
  • Led virtual design simulation including HiL/MiL/SiL and performed system calibration and optimization.
  • Integrated Camera, Radar, and Ultrasonic sensor data using Kalman Filtering, optimizing object integrity estimation.

Controller Integration Engineer - Cruise

General Motors - Autonomous Vehicle Control Group

Aug 2022 – Feb 2023
  • Developed and optimized lane map fusion algorithms for blue line path generation and path confidence.
  • Validated HIL for Advanced Driving Integration Module of Cruise robotaxi and managed issue resolution and tracking.
  • Led design for the cruise enable engine control algorithm, optimizing vehicle control and performance.

Innovation Vehicle Engineer

General Motors - Advanced Vehicle Development Group

Feb 2022 – Aug 2022
  • Led the development of the Autonomous Innovation Vehicle door project, managing hardware integration, software development, and validation across all subcategories.
  • Developed embedded systems in C for door control, incorporating auto-stop functionality, hardware failure detection, and noise reduction using MPLAB X and PIC microcontrollers.