LeddarTech®, an automotive software company that provides patented and disruptive artificial intelligence low-level sensor fusion and perception software technology for advanced driver assistance systems ( ADAS ) and autonomous driving (AD) , has partnered with TTTech Auto, a technology leader in automotive safety software are pleased to announce the completion of a joint demonstrator of a fail-operational architecture based on LeddarTech's LeddarVision™ software and TTTech Auto's Safety Co-Pilot solution.
TTTech Auto's innovative Safety Co-Pilot solution addresses the challenges of safety architecture for autonomous driving at SAE Level 3 and above. It is based on an execution channel that performs the main driver tasks and an independent inspection channel. When an abnormality is discovered in the inspection lane, the execution lane is seamlessly covered, while the back-up lane allows the vehicle to stop safely.
In this collaboration, TTTech Auto leverages its expertise in safety mechanisms and automotive platforms, complemented by LeddarTech’s advanced fusion and perception capabilities. TTTech Auto provides operating platforms and frameworks that facilitate the development of secure application disaggregation architectures. LeddarTech enhances the partnership with its high-performance fusion and perception stack based on low-level fusion. Provides a valuable data set for the demonstration of agile architecture exploration and decomposition-aware paths. Both companies embody agile methods, using risk assessments and feasibility assessments of safety-critical events to demonstrate potential solutions. This collaboration is designed to support automotive original equipment manufacturers (OEMs) building their SAE Level 2-3 safety solutions.
To demonstrate the feasibility of this approach and its potential for vehicle safety and cost-effectiveness, TTTech Auto and LeddarTech collaborated to combine LeddarTech’s LeddarVision with TTTech Auto’s Safety Co-Pilot to create a demonstrator focused on inspection lanes. Key features include:
LeddarVision by LeddarTech: A powerful artificial intelligence low-level fusion and perception software stack that leverages raw sensor data from cameras and radars to build environment models. Depending on the sensor group and diversity concept, LeddarVision can be deployed in either the execution channel or the inspection channel.
Safety Co-Pilot by TTTech Auto: A versatile solution for inspection lanes, equipped with a set of verification modules, each containing inspections related to key safety requirements. The demonstrator showcases validation modules that address probabilistic risks associated with physically infeasible trajectories and potentially hazardous collisions. A key element of security is that these checks are performed based on a separate environment model (in this case, LeddarVision), ensuring complete independence from the execution channel.
Testing of the demonstrator was conducted based on data collected from highways in Germany and Israel. The results show that LeddarTech's high-quality environmental models and TTTech Auto's probabilistic inspection enable an efficient and reliable inspection pipeline. This development provides a strong safety argument for AD systems operating at SAE Level 3 and above.
“We are paving the way for autonomous driving safety with a truly fail-operational architecture,” said Stefan Poledna, chief technology officer at TTTech Auto . “TTTech Auto’s breakthrough architecture combined with LeddarTech’s LeddarVision validates our commitment to SAE The promise of Level 3 and Level 4 autonomous driving.”
Frantz Saintellemy, President and CEO of LeddarTech, added: “Through our collaboration with TTTech Auto, combining AI-based LeddarVision software and TTTech Auto’s Safety Co-Pilot, our shared goal has been to demonstrate the potential for safe driving automation. Advanced Failed Operational Architecture. This collaboration aligns with our belief in accelerating technology deployment and adoption through win-win partnerships with other industry leaders.”
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