Part No. QLabs Virtual QCar 2
QLabs Virtual QCar 2 is a digital twin of the 1/10 scale self-driving car, enabling safe development and validation of autonomous algorithms with MATLAB or Python.
The Quanser Virtual QCar 2 is a fully instrumented, dynamically accurate digital twin of the physical QCar 2 1/10 scale self-driving car. It behaves the same way as the physical hardware and can be measured and controlled using MATLAB Simulink® or Python development environments. It can enrich your lectures and activities in traditional labs, or bring credible, authentic model-based lab experiences into your distance and online self-driving courses.
Same as the physical Qcar, the virtual system is a self-driving teaching and research platform complete with industrially relevant sensors such as LiDAR, 360° CSI cameras, an RGB-D camera, and inertial and odometric sensors.
The Quanser Virtual QCar 2 is a fully instrumented, dynamically accurate digital twin of the physical QCar 2 1/10 scale self-driving car. It behaves the same way as the physical hardware and can be measured and controlled using MATLAB Simulink® or Python development environments. It can enrich your lectures and activities in traditional labs, or bring credible, authentic model-based lab experiences into your distance and online self-driving courses.
Same as the physical Qcar, the virtual system is a self-driving teaching and research platform complete with industrially relevant sensors such as LiDAR, 360° CSI cameras, an RGB-D camera, and inertial and odometric sensors.
| Specifications |
|
| CSI Cameras | 4×160° FOV @820×410 resolution @30Hz |
| 2D LiDAR | 360° 384 points per scan @15Hz |
| RealSense RGB-D Camera | RGB and Depth @640×480 resolution @30Hz |
| IMU | 3-axis gyroscope and accelerometer |
| Minimum Specifications | • Intel Core Ultra 5, Intel Core i5, Ryzen 5 • 8GB RAM • Intel Iris or Arc integrated GPU |
| Recommended Specifications | • Intel Core Ultra 7, Intel Core i7, Ryzen 7 • 16GB RAM • Intel Arc integrated GPU, or discrete GPU (e.g. NVIDIA GeForce RTX 3050) |
| Recommended Research Specifications | • Intel Core Ultra 7, Intel Core i7, Ryzen 7 • 32GB RAM • Discrete GPU (e.g. NVIDIA GeForce RTX 3050) |
• Academically appropriate
High-fidelity, credible lab experiences equivalent to using the physical lab equipment
• Comprehensive Resources
Innovative curriculum and research resources
• Open Access
Full access to the system through MATLAB Simulink® or PythonTM
• Scalable
12-month multi-seat subscription
• Sensor interfacing and kinematic modeling
• Occupancy grid mapping
• Sensor fusion
• Vehicle lateral and longitudinal control
• Image acquisition and camera interfacing
• Line detection
• Lane detection and keeping
• Object detection and classification
Quanser QBot Platform is an autonomous ground robot with LiDAR, cameras, and NVIDIA Jetson Orin Nano for teaching and research in mobile robotics.
Quanser Rotary Servo Base Unit is a modular control experiment platform for teaching servo motor control concepts using a DC motor and encoder.
Quanser Interactive Labs delivers virtual hardware-based lab activities for engineering courses, supporting interactive and scalable remote learning.
Quanser STEM Interactive Exhibit showcases self-driving and AI technologies through hands-on activities, helping audiences explore intelligent vehicle systems.
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