Knowledge Center

Sensor Fusion and Motion Control for Autonomous Racing Cars

Learn how a research team from the Technical University of Munich uses Speedgoat real-time solutions, MATLAB^® and Simulink^® to develop and test an autonomous driving software stack, capable of operating a racing vehicle close to its physical limits. As an integral part of the framework, sensor fusion and motion control algorithms are optimized and validated for safe and accurate real-time operation, using Rapid Control Prototyping and Hardware-in-the-Loop simulation.

10 Ways to Speed Design for Digitally Controlled Power Converters with Simulink

This whitepaper highlights ways to accelerate digital control development for power converters with system-level simulation, how to validate control code on the processor without damaging electrical system hardware and developing real-time simulations of your electrical system.

ClearMotion

A ground-breaking active suspension system to transform the driving experience.

Flanders Make

Development of a High Precision Vehicle Drivetrain Test Bench.

GreenTeam

Formula Student racing success achieved through powertrain innovation.

Mobileye

Driving technology towards a fully autonomous vehicle.

Stanford University

Reducing emissions with low-carbon fuels.

ZKW Lichtsysteme GmbH

Developing Intelligent Lighting Systems for the Next Generation of Vehicles.

Incova

Designing intelligent valve-control system for a 20 ton excavator

Ponsse

Cutting development time for harvester control software by at least a year with model-based design.

Motion Planning and Experimental Validation for an Autonomous Bicycle

This paper introduces a prototype autonomous two-wheeled vehicle developed for experimental verification of motion planning and control algorithms. Finally, it presents and discusses experiments run on the actual vehicle for a particular maneuver. It emphasizes the differences between the trajectories created by different vehicle models.

Publication on ieeexplore.ieee.org