Sensors Simulation for Autonomous Systems

Sensors Simulation for Autonomous Systems, available at $199.99, has an average rating of 4.2, with 37 lectures, based on 15 reviews, and has 848 subscribers.

You will learn about Learn how to simulate Radar , Camera and Lidar sensors for autonomous systems using SimXai simulator Learn to detect obstacles in lidar point clouds through clustering and segmentation, apply thresholds and filters to radar data Process raw lidar data using Velodyne sensors with filtering, segmentation, and clustering to detect other vehicles on the road Analyze radar signatures to detect and track objects. Calculate velocity and orientation by correcting for radial velocity distortions, noise, and occlusions This course is ideal for individuals who are Sensor Fusion Engineer , Vehicle Test Engineer , ADAS/AV Function Owner It is particularly useful for Sensor Fusion Engineer , Vehicle Test Engineer , ADAS/AV Function Owner.

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Summary

Title: Sensors Simulation for Autonomous Systems

Price: $199.99

Average Rating: 4.2

Number of Lectures: 37

Number of Published Lectures: 37

Number of Curriculum Items: 37

Number of Published Curriculum Objects: 37

Original Price: $199.99

Quality Status: approved

Status: Live

What You Will Learn

Learn how to simulate Radar , Camera and Lidar sensors for autonomous systems using SimXai simulator

Learn to detect obstacles in lidar point clouds through clustering and segmentation, apply thresholds and filters to radar data

Process raw lidar data using Velodyne sensors with filtering, segmentation, and clustering to detect other vehicles on the road

Analyze radar signatures to detect and track objects. Calculate velocity and orientation by correcting for radial velocity distortions, noise, and occlusions

Who Should Attend

Sensor Fusion Engineer , Vehicle Test Engineer , ADAS/AV Function Owner

Target Audiences

Sensor Fusion Engineer , Vehicle Test Engineer , ADAS/AV Function Owner

Simulation is a key technologyfor developing, verifying and validating the behavior of highly automated vehicles in a variety of scenarios, environments, system configurations and driver characteristics. More and more engineers use this powerful technology in their daily work to solve multidimensional and interdisciplinary problems. Simulation is mainly used where classical experiments (under controlled conditions) are not possible due to the size, number and complexity or also because of the impact on the environment. The increasing product complexity of software-defined vehicles (SDV) and their mapping to digital twins (DT) also leads to deep supply chains in the simulation domain. To navigate this data ecosystem, it is not just about understanding the technology itself, but more importantly be able to confidently evaluate simulation models, methods and processes, know their limitations and optimize the relationship between business impact and resources used.

This course is designed to approach simulation-driven development of highly automated and self-driving cars from both a latest and a future technology perspective

Powered by NVIDIA  and Unreal Engine ,The course builds on standardization projects such as ASAM OpenX and covers software approaches to simplify participants’ entry into the technology area

Modules

A – Sensors Simulation (Radar , Camera , Lidar , Ultrasonic Sensors)

B – Scenarios & Driving Functions ( ASAM OpenDrive , ASAM OpenScenario )

Course Curriculum

Chapter 1: Introduction

Lecture 1: Introduction

Lecture 2: Course Content

Chapter 2: Introduction to SimXai Sensors Simulation

Lecture 1: SimXai Simulation

Lecture 2: SimXai Light Version

Chapter 3: Getting Started

Lecture 1: Hardware & Software Requirement

Lecture 2: Software Setup & Running Autonomous Vehicle Example

Chapter 4: LabVIEW Programming

Lecture 1: LabVIEW by NI Emerson

Lecture 2: LabVIEW Getting Started

Lecture 3: LabVIEW Project

Lecture 4: First LabVIEW Code

Lecture 5: Error Debugging

Lecture 6: Data Flow

Lecture 7: Data Types

Lecture 8: While Loop

Lecture 9: For Loop

Lecture 10: Shift Registers

Chapter 5: Radar

Lecture 1: Radar Principles

Lecture 2: Radar Simulation

Lecture 3: Radar Cross Section

Lecture 4: 3D Radar Cube

Lecture 5: Targets Range Estimation

Lecture 6: Fast Fourier Transform (FFT) for Range Calculation

Lecture 7: Doppler Estimation

Lecture 8: 2D FFT for Doppler Velocity Calculation

Chapter 6: LiDAR

Lecture 1: LiDAR Principles By Velodyne

Lecture 2: Velodyne VLP-16 LiDAR Simulation

Lecture 3: Point Cloud Data

Lecture 4: Polar & Cartesian Coordinates for Point Cloud

Lecture 5: Point Cloud Data Segmentation

Chapter 7: Vehicle Behavior and Environment Simulation.

Lecture 1: Module Content

Lecture 2: Navigating the Editor

Lecture 3: Placing Vehicles in the Scenario Editor

Lecture 4: Refining Vehicle Behaviour

Lecture 5: Tagging System

Lecture 6: Scenario File Generation

Lecture 7: Vehicle Trigger Boxes

Chapter 8: To Be Continued

Lecture 1: Not The End

Instructors

SimX Academy
Sensors Simulation for Autonomous Systems

Rating Distribution

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2 stars: 1 votes

3 stars: 2 votes

4 stars: 6 votes

5 stars: 6 votes

Frequently Asked Questions

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