Cruz Control
We are Cruz Control, the autonomous vehicle subteam of Slugbotics. While we previously competed in the F1TENTH autonomous racing competition, we've evolved into a research-oriented team focused on bridging the gap between simulation and real-world autonomous driving.
Our mission is to develop safe, reliable autonomous vehicle systems that can operate in both controlled environments and real-world conditions. We combine cutting-edge control algorithms with machine learning to create systems that can handle the complexity of real-world driving scenarios.
What We Build
- Autonomous vehicle platforms with state-of-the-art control systems
- Safe machine learning models for autonomous decision-making
- Simulation-to-reality transfer systems for real-world deployment
Our systems use advanced robotics and autonomous vehicle technologies: - Computer vision and sensor fusion - Model Predictive Control (MPC) and other advanced control algorithms - Machine learning frameworks for safe autonomous driving - Real-time control systems and embedded software
What We Do
- Research autonomous control algorithms for vehicle navigation
- Develop safe machine learning models in collaboration with UCSC's AIEA Lab
- Bridge simulation and reality by transferring algorithms from scale models to full-sized vehicles
- Test and validate autonomous systems in controlled environments
- Collaborate with faculty on cutting-edge autonomous vehicle research
Our Research Focus
Hardware & Embedded Systems
We work on both manual control systems (Xbox controller integration) and fully autonomous control using state-of-the-art algorithms. Our platform serves as a testbed for developing and validating control strategies that can be scaled to real-world applications.
Software & Machine Learning
In collaboration with Professor Leilani Gilpin's AIEA Lab, we're developing safe machine learning models for autonomous vehicle control. Our research focuses on creating systems that can reliably control full-sized vehicles while maintaining safety standards.
Simulation-to-Reality Transfer
Our ultimate goal is to bridge the gap between autonomous systems that work "in simulation" (like our scale model vehicles) and real vehicles that operate on actual roads. This involves developing robust algorithms that can handle the uncertainties and complexities of real-world environments.
How to Join
- Join our Discord to connect with current team members
- Attend our meetings to learn about current projects and research areas
- Choose your focus area: hardware/embedded systems, control algorithms, or machine learning
- Contribute to ongoing research and help develop our autonomous systems
We welcome students from various backgrounds: - Computer Science students interested in autonomous systems and machine learning - Electrical Engineering students wanting to work on embedded systems and control - Mechanical Engineering oriented students interested in vehicle dynamics, vehicle design, and robotics broadly - Mathematics students with an interest in control theory and optimization - [insert your major here]
No prior autonomous vehicle experience required - we provide training and mentorship for all skill levels.
Follow us on Instagram or join our Discord to get started.
🚗 Drive Innovation. Build the Future. Join Cruz Control! 🚗