Make Autonomy Safer

At Edge Case Research, we’re committed to building safety into autonomous systems from the ground up.  

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UL 4600

First Comprehensive Autonomous Product Safety Standard (UL 4600)


Safety isn’t about nominal performance, it’s about how safely you handle edge cases. What edge cases put your autonomous perception software at risk? You can’t know until you understand what’s in your sensor data. Hologram is a powerful “big data” testing and analytics platform that unlocks the value of petabytes of recorded road data to accelerate the development of safer perception software.



Traditional safety processes can’t keep up with modern software development. Too often, safety analyses are out of date before they’re even complete. MISSA bridges the gap to better safety programs. It empowers customers to build robust autonomy architectures, perform comprehensive hazard
analysis, and develop solid safety cases.



Achieving the promise of autonomy demands the ability to operate everywhere and safely handle unforeseen situations. The real world has a knack for presenting scenarios that you can’t imagine and don’t test for. Switchboard robustness testing helps you prepare your software for unexpected
situations before it hits the field.


Safety usually isn’t about how you handle nominal cases. It’s about how well you handle edge cases.

Prof. Philip Koopman


Edge Case Research advocates a holistic, “full stack” approach to safety. . How will a new technology be deployed in the real world?  What are the safety risks of deployment?  What behaviors must the technology exhibit to stay safe? In which environments and scenarios must it operate reliably?

Our methodology seeks answers to these questions and more. Safe autonomy does not just count on reliable hardware and defect-free software. It also depends on the suitability of autonomous behaviors and machine learning to real-world environments.

This complexity is hard to tackle with manual, ad hoc processes and tools.  Our autonomous future depends on reliable automation to multiply the skill of safety experts.

ECR was founded by global leaders in safety and autonomy.  We combine scalable, data-centric analysis products with deep expertise in software architecture, perception, machine learning, and risk analysis. 

Our mission demands strong partnerships between AI, robotics, automotive, and industrial safety experts across many organizations. Partnering with ECR allows customers to innovate rapidly without sacrificing safety.  


Perception & MACHINE LEARNING Intelligent machines count on unbelievably complex algorithms and machine learning to understand the world around them. While these AI techniques are beginning to meet and exceed human performance in many areas, they suffer from very real safety risks such as fragility in real-world conditions. Our team understands the risks involved and is pioneering ways to make such systems more resilient and robust.

Our team has decades of experience building autonomous vehicles and robots. We understand how to architect these systems from end-to-end and know the challenges of deploying autonomy into unstructured environments.

System safety begins with a clear, actionable assessment of risks. Gaps in risk analyses can lead to blind spots even in mature safety processes. Unfortunately, risk analyses often rely too heavily on oversimplifications and analogies to previously-deployed technology. We know this stands in the way of creating innovative, complex products. That’s why we’re developing systematic tools for modeling, tracking, and validating risks throughout development and after deployment.

Software doesn’t “wear out” like hardware does.  Software safety risks are posed by defects that can arise at a variety of points within the development process. Our team has decades of experience researching and delivering safe software. We are expert in software-safety approaches such as standards-based “V” processes, which begin with high-quality safety requirements that are refined into an implementation. This ultimately produces a chain of evidence that connects final test results back to the safety-relevant system requirements.


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