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Siemens Introduces Validation Program for Autonomous Vehicle Development

Siemens introduces the PAVE360 pre-silicon autonomous validation environment, a program established to enable and accelerate the development of innovative autonomous vehicle platforms.
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Siemens introduces the PAVE360 pre-silicon autonomous validation environment, a program established to enable and accelerate the development of innovative autonomous vehicle platforms. The program provides a comprehensive environment for multi-supplier collaboration across the automotive ecosystem for the development of next-generation automotive chips. It also extends digital twin simulation beyond processors to include automotive hardware and software sub-systems, full vehicle models, fusion of sensor data, traffic flows and even the simulation of smart cities through which self-driving cars will ultimately travel.

PAVE360 enables capabilities for full, closed-loop validation of the sensing/decision-making/actuating paradigm at the heart of all automated driving systems. This principle hinges on rigorous pre-silicon validation of deterministic (rules-based) and non-deterministic (AI-based) approaches to safe self-driving in the context of the full digital twin.

With this program, chip design can be democratized, enabling carmakers, chipmakers, tier one suppliers, software houses and other vendors to collaborate on the development and customization of complex silicon devices for autonomous vehicles. PAVE360 helps to speed chip design and software validation, enabling the creation of model-specific silicon for the first-generation of self-driving cars.

PAVE360 establishes a design-simulation-emulation solution that scales from individual blocks of a system-on-chip’s (SoC’s) IP, to hardware and software on the SoCs, to vehicle subsystems, and up through deployment of vehicles in smart cities.

Already on display in the Center for Practical Autonomy Lab in Novi, Michigan, PAVE360 is designed to serve as the industry-standard verification and validation program for modeling solutions in the automated driving ecosystem.

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