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
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SimX Academy
Sensors Simulation for Autonomous Systems
Rating Distribution
- 1 stars: 0 votes
- 2 stars: 1 votes
- 3 stars: 2 votes
- 4 stars: 6 votes
- 5 stars: 6 votes
Frequently Asked Questions
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Can I take my courses with me wherever I go?
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