Autonomous Vehicle

VEHICLE

Autonomous

Autonomous Vehicle Simulation Solutions to Drive the Future

Traditional road and track tests are too costly and take too long to complete. OPAL-RT’s systems overcome obstacles the transportation industry faces when testing autonomous vehicle by migrating physical testbeds onto simulation platforms. OPAL-RT systems are flexible enough to integrate each new technology as it is introduced to the vehicle, from data fusion and deep learning to new sensors such as LIDAR.










Bringing Innovation to the Most Important Fields

OPAL-RT offers not only entry-level systems to universities, research laboratories and start-ups, but also the most advanced product and service line for the most in-demand fields that use Advanced Driver-Assistance Systems (ADAS) and Automated Driving (AD) systems. These include:


Automotive

Agricultural

eVTOLS

Rolling stock vehicles

Robotic systems

OPAL-RT at Every Stage of Your Project

OPAL-RT provides X-in-the-loop simulations technologies that allow engineers to accelerate their validation at every stage of their development cycle.






Model-in-the-Loop (MIL) / Software-in-the-Loop (SIL)

  • Simulated: Vehicle hardware (E.g. ECU and motion controllers)
  • Real: No real vehicle hardware
  • Objective: Validate the model, code and platform


Hardware-in-the-Loop (HIL)

  • Simulated: Vehicle components and environment
  • Real: Vehicle hardware (E.g. ECU and motion controllers)
  • Objective: Test vehicle’s hardware



Driver-in-the-Loop (DIL)

  • Simulated: Complete vehicle and environment
  • Real: Driver
  • Objective: Collect driver’s feedback for the development of the autonomous vehicle



Vehicle-in-the-Loop (VIL)

  • Simulated: Environment
  • Real: Driver and autonomous vehicle
  • Objective: Study human behavior and reactions when in an autonomous vehicle



Webinar | Development, Test and Validation of ADAS/AD Systems

In these webinars, we take a deep look into real-time simulation’s role to overcome obstacles the automotive industry faces when testing autonomous vehicle controls and ADAS by migrating physical testbeds onto simulation platforms.

















Autonomous Driving Solution Highlights

Sensors & Data Fusion

Simulate multiple sensors such as camera, GNSS, LiDARs and RADARs to estimate distances, velocities, and other data in a 3D enviroment. In addition, combine these different sources of data in order to make predictions or decisions.








Deep Learning

Reduce risks and accelarate time-to-market by simulating deep learning models. OPAL-RT’s solution helps engineers to improve the accuracy when the vehicle is reading and detecting pedestrians, environment signs and objects.






Communication Protocols

Emulate industry-standard communication protocols such as CAN/CAN-FD/CANopen, Lin, Automotive Ethernet, and perform the most realistic real-time simulations possible.






An All-In-One Test Environment

RT-LAB is is an all-in-one test environment that enables models to interact with the real world for complete desiging, integration, testing, and validation of autonomous vehicles in a virtual environment. Thus, costs, time-to-market and the inherent risks of the project will be reduced.

Fully integrated with MATLAB/Simulink®, CARLA, PyTorch and others, RT-LAB is ideal for engineers to rapidly develop and validate their applications, regardless of their complexity.



Software Highlights and Benefits

Unique features

RT-LAB has a robust framework that allows:

  • Step by step debugging
  • Recording massive data
  • Customized test scenarios
  • Input, output and internal signals accessibility during simulation

Co-simulation

Better model integration means better performance. RT-LAB also includes an easy co-simulation toolbox (Orchestra) that allows the integration of models from different programming languages and providers.







Digital assets

Explore open digital assets (urban layouts, buildings, vehicles) with CARLA. It supports flexible specification of sensor suites, environmental conditions, full control of all static and dynamic actors, map generation and much more.







Machine Learning

Improve the performance of deep learning models with PyTorch. A machine learning framework that offers a rich ecosystem of tools and libraries for users to accelerate the path from research prototyping to production deployment.







Meet OPAL-RT’s Autonomous Vehicle Hardware Platform

OPAL-RT introduces the OP5042XG, the cost effective, yet high performance digital simulator created to meet the increasing needs of engineers and researches who are working with autonomous vehicle design, integration, test and validation.

Hardware Overview

  • Processor: 2x Xeon Silver 4112 (2.60GHz)
  • Graphic card: 1x MSI GeForce RTX 3060
  • Connectivity: Ethernet, RS-232, VGA, 2x USB 2.0 ports
  • Expansion capabilities: 6 PCI Express slots
  • Dimensions: 16.93″ (W) x 6.93″ (H) x 18.43″ (D)








Expanding Users’ Capabilities

The Autonomous Vehicle Platform (OP5042XG) is compatible with a wide range of real-time simulator platforms for users to expand the amount of I/Os or communication networks.








OP5707XG | High-end performance

Connect real devices via the OP5707XG I/Os connection. OP5707XG offers an unequalled level of high-end Intel® multi-core processor, FPGA performance and optical connectivity to meet top-level requirements.










NI PXI | National Instruments

Connect I/Os and multiple sensors such as camera, GNSS, LiDARs and RADARs. OPAL-RT systems are compatible with a variety of NI’s PXI and PXI Express chassis for automated test and automated measurement applications.

The Power of Co-Simulation

Orchestra, RT-LAB’s co-simulation toolbox, offers an extensive list of simulation tools already tested and integrated into its suite of intelligent vehicle solutions. For those seeking more customization, OPAL-RT provides the expertise to integrate models from different programming languagues, providers,  and extend the capabilities of users’ system.







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