SIMulation Workbench
Hardware-in-the-loop simulation is a trusted, cost-effective alternative for executing tests on actual equipment. It is ideal for proving designs and performing tests of complex equipment such as in automobiles, airplanes, missiles, satellites, rockets and locomotives. Testing is executed in a virtual test scenario instead of on the road or in real-devices. Much of the test environment is replaced by mathematical models, so components can be inserted into a closed loop. This makes for tests that are reproducible, systematic and fast, as well as more reliable. Concurrent hardware-in-the-loop simulation solutions feature the SIMulation Workbench real-time modeling environment running on the RedHawk Linux operating system. SIMulation Workbench provides a complete framework that makes it easy to develop and execute hardware-in-the-loop simulations in real-time.
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Apollo Autonomous Vehicle Platform
Various sensors, such as LiDAR, cameras and radar collect environmental data surrounding the vehicle. Using sensor fusion technology perception algorithms can determine in real time the type, location, velocity and orientation of objects on the road. This autonomous perception system is backed by both Baidu’s big data and deep learning technologies, as well as a vast collection of real world labeled driving data. The large-scale deep-learning platform and GPU clusters. Simulation provides the ability to virtually drive millions of kilometers daily using an array of real world traffic and autonomous driving data. Through the simulation service, partners gain access to a large number of autonomous driving scenes to quickly test, validate, and optimize models with comprehensive coverage in a way that is safe and efficient.
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Parallel Domain Replica Sim
Parallel Domain Replica Sim enables the creation of high-fidelity, fully annotated, simulation-ready environments from users’ own captured data (photos, videos, scans). With PD Replica, you can generate near-pixel-perfect reconstructions of real-world scenes, transforming them into virtual environments that preserve visual detail and realism. PD Sim provides a Python API through which perception, machine learning, and autonomy teams can configure and run large-scale test scenarios and simulate sensor inputs (camera, lidar, radar, etc.) in either open- or closed-loop mode. These simulated sensor feeds come with full annotations, so developers can test their perception systems under a wide variety of conditions, lighting, weather, object configurations, and edge cases, without needing to collect real-world data for every scenario.
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WaveFarer
WaveFarer is a high-fidelity radar simulator that accounts for multipath and scattering from structures and vehicles in the immediate environment of a radar system as well as key atmospheric and scattering effects for frequencies up to and beyond 100 GHz. Applications include simulation of automotive drive scenarios, indoor sensors, and far-field radar cross section (RCS). WaveFarer’s features enable fast and accurate analysis of scenarios with radars in close proximity to structures, targets, and other features in a simulated environment. WaveFarer is designed to support all applications relevant to the simulation and analysis of a radar system. For automotive radar, this includes evaluating radar sensor placement and target returns within a simulated drive scenario environment. For surveillance radar applications, this includes analysis of target radar cross section (RCS) and the impact of materials on results.
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