Sensors Simulation for Autonomous Systems

Explore SimXai, a multi-domain simulator for autonomous vehicles and robots, design environments with an open environment editor, configure vehicle dynamics, and stream sensor data via the LabVIEW SDK.

Explore a physics-based xd simulator for modeling radar, lidar, and camera sensors, featuring the lite version with predefined maps and assets, designed for easy use and interaction via flint.

Create a vi in LabVIEW, use the front panel as the UI to add controls and indicators, and discover the block diagram and function palette.

Launch LabVIEW to create a new project or open an existing one, organize files in the project explorer, set the target, and configure dependencies and build specification.

learn to create a simple LabVIEW front panel with two numeric controls and a numeric indicator, wire them through an add function on the block diagram, and run the VI.

Identify wiring issues that prevent code from running, then use the error and warning list accessed via the broken arrow to locate and fix the error.

Demonstrates data flow in LabVIEW, showing how execution waits for the add function to finish before the multiply function starts, highlighting the sequence of operations in view code.

Explore core data types in Levi, including numeric (double and integers), boolean, string, arrays, and clusters, and learn 64-bit floating point representation and signed versus unsigned integers.

Explore how a for loop executes a fixed number of iterations, starting at zero and running 100 times, with indicators and controls passed outside the loop.

Learn how shift registers transfer data from one loop iteration to the next, storing previous values to compute an average of random numbers across 100 iterations.

Explore radar principles and FMCW radar used in automotive autonomous systems; learn how emitted signals reflect to measure range and velocity, and review transmitter, amplifier, antenna, mixer, and processing unit.

Construct the 3D radar cube from multi-pulse, multi-element signals, then apply a Hanning window, convert to the time and frequency domains, and extract range and velocity for targets.

Learn how radar estimates target range for four targets using fmcw radar, beat frequency, and time-of-flight calculations with the speed of light.

Apply a two-dimensional fft to radar data to extract target range and velocity, using column and row ffts to calculate doppler velocity and visualize results in a 3d radar cube.

Demonstrate simulating the Velodyne VLP-16 LiDAR with a 100-meter range and 16 laser layers, using Vellavu and vital view to visualize live point-cloud data from the Eagle vehicle.

Explore Velodyne LiDAR point cloud data, with x, y, z coordinates and intensity, visualize laser reflections across vehicle materials, and configure materials via API and the calibrated materials database.

Convert polar LiDAR data from Velodyne into Cartesian coordinates for a point cloud, using azimuth and elevation, converting to radians, and applying sine and cosine to compute x, y, z.

Learn to segment point cloud data to identify free space and obstacles using Velodyne LiDAR in Matlab, including ground plane, ego vehicle, and nearby obstacles.

Navigate the scenario editor to place vehicles and pedestrians, define and refine vehicle behavior in environment simulation, and generate a Sam open scenario file using the tagging system.

Navigate the scenario editor’s 3D viewport to place vehicles, pedestrians, and signs, manage assets in the content browser and world outliner, and adjust position, rotation, and scale.

Place a vehicle in the scenario editor from the Content Browser, drag it into position, and assign it to follow the nearest lane; press play to observe its behavior.

Refine vehicle behaviour in sensors simulation by adjusting set speed, initial speed, and obeying speed limits, then tune search forward backward distance and lane change to evaluate steering accuracy.

Generate scenario files using the scenario tool widget, save and load scenarios with actors, static objects, and the ego vehicle, and replay dynamic actors in a running simulator.

Use vehicle trigger boxes to define actions when vehicles or intersections interact. Drag and drop triggers from search results and set actions such as changing lanes or staying in lane.

Learn to simulate sensors and design scenarios for autonomous systems, guiding the journey from assisted to fully automated. Stay tuned for more modules and updates.