The Challenge
Turn overwhelming data into decisive engineering action
Toyota’s legacy of quality, safety, and continuous improvement (or “Kaizen”) shapes how millions experience mobility. The Woven by Toyota team brings that same discipline to software, creating the engineering tools and systems that support autonomous and next-generation vehicle development. As the automotive industry dives further into software run vehicles that can operate in conventional, assisted, or fully autonomous modes, the engineering complexity is driving new use cases to ensure system reliability, quality and safety.
Furthering that complexity, the people responsible for isolating, tracking, and resolving issues in the software ranged from novice to expert. Current tools don’t account for this range of user expertise. At Woven by Toyota, a small team from the ADAS (Advanced Driver Assistance Systems) group began working to adapt an open source tool to meet Toyota’s future needs, and Visual Logic was brought on to support UX and product teams take that idea and turn it into a product roadmap.
In the early phases, we identified their early adopted framework had no connection to their existing design system, critical features were difficult to find, and had manual/laborious configuration of “Views” in order to begin analyzing a new set of data. Current workflows and tooling were not supporting a shift towards Continuous Integration and Continuous Delivery. When you’re working on an assembly line and have hundreds of tests to review to ensure quality and safety, additional minutes of setup aren’t just frustrating—they can (and have) delayed vehicle production by months.
The Work
Here’s how we helped the Toyota Woven Team
Our team bolstered their talented, but already busy UX team in a complimentary way—adding in some outside perspective. Our work started more tactical in our initiative, focused on quick “UX wins” and Design System compliance. From there, we quickly transitioned to complete system redesign based on User Research, stakeholder strategy, and business needs. Within the Toyota ecosystem, there was a broad range of user type, each with their own set of tasking. Were they viewing live telematics, recorded data or simulating new data? What stage of production are they in? What dependencies did they have on other engineering teams? Could AI support the process and in what way—how might we ensure a human-in-the-loop approach? What expertise does each group of users have in data analytics?
Two important workflow distinctions operate around SIL (Software-in-the-loop) and HIL (Hardware-in-the-loop) testing scenarios. SIL is a simulation technique used to validate and test the functionality of software, running the generated code against a virtual simulation of the system it will control on a computer before it’s deployed to hardware. While HIL offers real-time simulation by connecting the real control hardware to a simulated environment .
Our research uncovered that engineers across workflows were not only struggling to find out why an issue was occurring, but what team or whose responsibility it was to address the concern. Their current tool didn’t enable collaboration, nor support independent troubleshooting to determine root cause.
Key highlights:
Design System Implementation and Improvements
Product Vision & Strategy
Prototyping & High Fidelity UI Design
User Research & Journey Mapping
Framework Redesign
From quick wins, to strategic north star vision
We began by addressing high priority UX concerns with their existing tool. Outside of implementing their existing design system specifications, diving into the micro and macro interaction frameworks, terminology and iconography. All in an effort to feed development teams now, while we imagine the ideal future for the tool.
User Research spanned many automotive verticals, including Subject Matter Experts (S.M.E) across the assembly line, Vehicle Quality Divisions, advanced system development like Automated Driving (ADAS), and even diving into racing applications. We learned that the Developer Experience (DX) spanned multiple engineers, in each of the groups we spoke to, emphasizing the need for a system that enables collaboration rather than treating it as an afterthought. We took these inputs to visualize workflows and journeys that drove the design of our north-star system.
We focused on streamlining user flows from efficient fault isolation to subsequent root cause analysis, and designed for a new paradigm of collaboration, encouraging domain experts to pick up where others may lack expertise, and ensuring that hand-off was seamless and traceable.
Embedding with existing teams, rituals and internal partners
We worked iteratively with product, engineering, UX and other lead stakeholders to drive us towards a product north-star that offered alternatives to existing engineering workflows. The new solution was designed to speed up the debugging loops with new features and data visualizations, consolidate workflows, and offer a more collaborative tool environment to bring more clarity into the process.
The Impact
A foundation for safer vehicles.
We cast a vision for a future where domain experts are able to quickly and efficiently identify issues, rapidly develop and test their code, then hand it off for eventual production. We offered alternatives to existing engineering workflows—speeding up the debugging loops with new features and data visualizations, consolidating workflows, and offering a more collaborative tool environment to bring more clarity into the process.
In addition to workflow optimization, this product hopes to bring long-term cost savings by reducing dependencies on costly 3rd party tools, and fragmented homegrown solutions.
