Log data is one of the most essential building blocks of autonomous systems development. Our log management handbook discusses the journey of a log file from inception to storage and lays out common practices for autonomy programs across industries.
Part 2 of our verification and validation (V&V) handbook series explains how autonomy programs typically approach scenario creation and test execution depending on their development stage and how they can address common challenges.
Safely developing and deploying autonomous systems is a challenging task. Applied Intuition’s V&V handbook aims to provide autonomy programs with an active resource to safely develop, test, and deploy autonomous systems for commercialization.
Suspension models are crucial for off-road simulation because off-road vehicles often need to handle uneven terrain. Simulations that don’t account for a vehicle’s suspension might lead to results that aren’t fully representative of the real world.
Applied Intuition’s perception team has conducted a case study that uses Spectral synthetic data to improve a perception algorithm’s object detection performance on underrepresented classes in a real-world dataset.
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.
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.
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.
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.
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.
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.