Overview

At the center of the Self-Driving Car Studio, the QCar, is an open-architecture scaled model vehicle, powered with NVIDIA® Jetson™ TX2 supercomputer, and equipped with a wide range of sensors, cameras, encoders, and user-expandable IO.

The Self-Driving Car Studio’s open software architecture is designed to provide an accessible and customizable platform for teaching while guiding and accommodating a wide range of key functionalities required for multi-vehicle research through a variety of customizable modules. With a rich collection of teaching material in Python and ROS2, as well as an expansive set of software tools, including Simulink ®, Python™, C++, and ROS examples, the studio enables researchers to build high-level applications and reconfigure low-level processes.

 

  • 1 x high-performance preconfigured turnkey PC to serve as a testbed and infrastructure station
  • 3 x high-definition monitors
  • 1 x game controller
  • 2 x floor maps (15.75’ x 9.2’/4.8m x 2.8m and 15.75’ x 20’/4.8m x 6.1m) for testing various driving scenarios (images in Gallery)
  • 2 x custom PVC borders for branding and environmental mapping
  • 4 x reprogrammable traffic lights with batteries
  • 1 x set of accessories including multiple options for North American and European scale signs and ten traffic pylons
  • 1 x car stand
  • 1 x preconfigured router for high-speed wireless communication
  • 1 year license for the QLabs Virtual QCar module

 

The Quanser Virtual QCar is a fully instrumented, dynamically accurate digital twin of the Quanser QCar system. It behaves the same way as the physical hardware and can be measured and controller using Python, ROS, or MATLAB and Simulink. 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 course.

Same as the physical QCar, the virtual system is a self-driving teaching and research platform complete with industrially relevant sensors such as
Lidar and RGBD cameras.

QLabs Virtual QCar is available as a 12-month multi-seat subscription. The platform is compatible with the physical QCar content, covering
examples such as 360 vision, RGBD imaging, autonomous driving and more. The platform also integrates with the self-driving teaching content available with the Self-Driving Car Studio.

Product Details

Dimensions 39 x 21 x 21 cm
Weight (with batteries) 2.7 kg
Power 3S 11.1 V LiPo (3300 mAh) with XT60 connector
Operation time (approximate)  ~2 hours 11 m (stationary, with sensors feedback)
30 m (driving, with sensor feedback)
Onboard computer NVIDIA® Jetson™ TX2
CPU: 1.2 GHz quad-core ARM Cortex-A57 64-bit + 1.2 GHz Dual-Core NVIDIA Denver2 64-bit
GPU: 256-core NVIDIA Pascal™ GPU architecture, 1.3 TFLOPS (FP16)
Memory: 8GB 128-bit LPDDR4 @ 1866 MHz, 59.7 GB/s
LIDAR LIDAR with 2k-8k resolution, 10-15Hz scan rate, 12m range
Cameras Intel D435 RGBD Camera
360° 2D CSI Cameras using 4x 160° FOV wide angle lenses, 21fps to 120fps
Encoders 720 count motor encoder pre-gearing with hardware digital tachometer
IMU 9 axis IMU sensor (gyro, accelerometer, magnetometer)
Safety features Hardware “safe” shutdown button
Auto-power off to protect batteries
Expandable IO 2x SPI
4x I2C
40x GPIO (digital)
4x USB 3.0 ports
1x USB 2.0 OTG port
3x Serial
4x Additional encoders with hardware digital tachometer
4x Unipolar analog input, 12 bit, 3.3V
2x CAN Bus
8x PWM (shared with GPIO)
Connectivity WiFi 802.11a/b/g/n/ac 867Mbps with dual antennas
2x HDMI ports for dual monitor support
1x 10/100/1000 BASE-T Ethernet
Additional QCar feautres Headlights, brake lights, turn signals, and reverse lights (with intensity control)
Dual microphones
Speaker
LCD diagnostic monitoring, battery voltage, and custom text support

 

Supported Software and APIs QUARC Autonomous Software License
Quanser APIs
TensorFlow
TensorRT
Python™ 2.7 & 3
ROS 1 & 2
CUDA®
cuDNN
OpenCV
Deep Stream SDK
VisionWorks®
VPI™
GStreamer
Jetson Multimedia APIs
Docker containers with GPU support
Simulink® with Simulink Coder
Simulation and virtual training environments (Gazebo, QuanserSim)
Multi-language development supported with Quanser Stream APIs for inter-process communication
Unreal Engine

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