This blog post discusses how open-loop log replay and re-simulation evaluate the performance of perception and localization systems and motion planning and control systems, respectively, to comprehensively verify and validate a full AV stack against disengagements.
Abstract, logical, and concrete scenarios play an essential role in testing, validating, and certifying the safety of automated driving systems. OpenSCENARIO V2.0 will make it easier to create and transfer abstract scenarios between tools.
The log re-simulation tool Logstream enables AV and ADAS engineering teams to analyze disengagement events, evaluate stack performance, and bring safe autonomous systems to market faster.
It has been historically difficult to verify requirements using public road drive data. Applied Intuition’s “scenario search” automates the error-prone and time-consuming work involved in existing approaches.
The V&V platform Basis and the drive data exploration tool Strada allow AV and ADAS engineering teams to prepare for long-tail events and deploy safe automated driving systems to the public.
An efficient approach to find failing cases in exponentially large parameter spaces is required. Learn about an ML-based approach to complement your existing validation workflows.
There are increasingly more commercial applications for autonomous multi-robot systems. We discuss examples of use cases and testing considerations to ensure the safety and advancement of the system algorithms.
The Applied team is building Vulkan rendering capabilities. Learn how ray tracing can improve the fidelity and performance of sensor data for perception system development.
The Applied team evaluates the common perception simulation approaches against five key requirements. Simulated lidar can be almost indistinguishable from real data through detailed sensor validation.
Applied is a supporter of open standards for simulation tests and is an active participant in the ASAM projects. Here’s a look at how Applied works with the standards and addresses their limitations today.
While it is tempting to use formal methods ‘for everything’, it is more effective to use the right mix of methods at the required level of verification and validation for developing and deploying L3+ autonomous systems.
The Applied team discusses approaches taken by traditional and ML-driven companies to manage increasingly more complex requirements throughout the AV development lifecycle.
With large volumes of data generated in AV development from drives and simulations, making effective use of data can be challenging. The Applied team discusses how to effectively use data to track progress on known requirements and uncover gaps in coverage.
Annotating and curating datasets from the real-world driving data is a common approach for training AV algorithms, yet it is expensive, biased, prone to errors, and limited in scale. Learn how to use synthetic data to overcome these challenges.
No company is too early to implement simulation and automated CI testing regardless of their stage of autonomous vehicle development. The Applied team discusses best practices for setting up tests to speed up the deployment of autonomous algorithms.
Sensor simulation for autonomous driving systems is an exceedingly difficult task, requiring high-fidelity simulations that could be processed in real-time. There are techniques that could be used to accurately test your perception system.
With the limitations of requirements and scenario coverage approaches, domain coverage measured on ODDs may be a practical approach to evaluate progress in the autonomy roadmap.
We discuss how simulation fits into frameworks for building safety critical systems, recent standardization efforts around this technology, and the requirements for simulation tools to support this use case.
Toyota Research Institute - Advanced Development uses Applied’s synthetic data creation capabilities to develop realistic environments for the Lexus LF30 virtual reality experience at the Tokyo Motor Show.
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