This webinar series presents end-to-end MATLAB, Simulink, Simscape, and Vehicle Dynamics Blockset workflows tailored for Automotive Student Competition teams. It begins with core automotive topics such as vehicle dynamics, suspension and tire modeling, and powertrain system design, leading to full-vehicle simulation and performance analysis.

Building on these fundamentals, the series extends into driverless system development, covering perception and system integration workflows. Topics include vision- and LiDAR-based cone detection using images and point clouds, data labeling and model training, and co-simulation with ROS and Unreal Engine. The sessions conclude with production-oriented embedded workflows, including model-based design, continuous integration testing, automatic code generation, and deployment to hardware using automotive communication protocols such as CAN.

Highlights

• Overview of vehicle dynamics modeling for Formula Student applications, including longitudinal and lateral behavior and common evaluation maneuvers
• Overview of software development workflows in MATLAB and Simulink for controls applications 
• Overview of object detection workflows using deep learning
• Overview of what’s new in Automated Driving with MATLAB/Simulink, and RoadRunner

Full-Vehicle Modeling and Simulation for Formula Student Cars

Veer Alakshendra, MathWorks
Nobu Iijima, MathWorks