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Tackling development and testing tasks for AD and e-mobility

To tackle innovation challenges for autonomous driving (AD) and electric mobility, the development and test environment must support several key attributes. For AD applications, this means a development and test platform that supports fast, time-correlated datalogging; fully automated AI-powered image anonymization; sensor data annotation based on reliable ground-truth data; simulated scenario generation to perform thousands of safety-critical and realistic driving scenarios; the synchronous replay of sensor and vehicle bus data to validate AD components; one time-correlated data stream to support the testing of multiple sensor systems (i.e., camera, radar, lidar); and automated testing in the cloud for maximum test throughput in the shortest possible time.

For e-mobility applications, the platform should incorporate computing technology that supports open model libraries for developing and testing tasks; ready-to-use motor models for testing on the signal and power levels; mechanical test benches to allow for mechanical components to be brought into hardware-in-the-loop simulation tests; high-voltage electronic load modules to test power control systems; high-fidelity battery models and battery cell simulation to test battery management systems; and the latest communication protocols to test charging infrastructures and onboard power electronics.

dSpace has built a complete, end-to-end development and test execution platform to solve all these tasks for autonomous driving and e-mobility applications. Find out more at Automotive Testing Expo in Novi, Michigan.

Booth 4000 and AV210

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