Play multiple files from start to end as if they’re a single recording and stay organized with layout folders. Available now with release 2.17. View the full changelog here.
Play multiple files from start to end as if they’re a single recording and stay organized with layout folders. Available now with release 2.17. View the full changelog here.
Mission: Developing self-driving cars with an AI-first approach.
Founded: 2017 in Cambridge, UK
Size: ~300 people (Series C)
Hours or days to minutes or seconds reduced time to triage an event
Centralized tooling for experimentation fever internal developer tools
Instead of relying on hand-coded rules or HD maps like industry incumbents, Wayve is building a self-driving system powered entirely by data and machine learning. By developing end-to-end AI software that can generalize across different geographies and vehicle platforms, Wayve aims to be the first autonomous vehicle company to deploy its technology in 100 cities.
As an AI-focused company, Wayve continually seeks ways to increase the volume of experiments the team can run in each development cycle—more experiments mean more data to feed into their foundation models and faster improvements for their autonomy software. As Wayve’s operations scaled, they needed an observability platform to efficiently understand what was happening under the hood of their vehicles.
By adopting Foxglove and the MCAP file format, Wayve has consolidated its tooling, streamlined debugging, and gained faster, actionable insights from their experiments—propelling them towards their mission of deploying self-driving technology globally.
Wayve began pioneering a new approach to self-driving in 2017, developing an end-to-end AI model that processes sensor data and outputs driving controls. As they made rapid progress in building a self-driving system capable of overcoming traditional scalability hurdles, they needed a partner to support their engineering workloads. Specifically, they were looking for a partner that provided scalability, allowed Wayve’s expert engineers to remain focused on building their self-driving AI, and created efficiencies between tools.
After integrating their data into the Foxglove platform, Wayve has reduced the triage process from days to minutes.
With centralized tooling that everyone can contribute to and reference, team members can now build on each other’s work more effectively to resolve issues. Both engineers and vehicle safety operators can collaborate on tagging points of interest as Foxglove events and add relevant metadata for easier categorization and search.
“Foxglove helped us to supercharge our processes. We went from days to minutes when finding the root cause of an issue.” J’aime Laurenson, Product Lead at Wayve
Now, triage engineers can filter for specific event types (e.g., human operator intervention), prioritize the results by impact and urgency, and visualize each one with a single click. Each event contains all relevant information in one place, allowing team members to begin debugging faster than usual.
Wayve has also set up common Foxglove visualization dashboards for engineers to use when investigating different types of interventions, along with playbooks to help them interpret key indicators—such as comparing specific series in a graph or referencing certain values. These playbooks became so granular and clear that Wayve was able to codify some of these pro tips and automate them in a post-processing step, completely bypassing what could otherwise take hours of debugging.
“We’re at a critical juncture as we deliver the first Embodied AI product set to automotive customers - so shaving days off our iteration cycle with Foxglove is a huge advantage.” J’aime Laurenson, Product Lead at Wayve
After adopting Foxglove, Wayve was able to deprecate several internal developer tools. Thanks to Foxglove’s flexible data visualization panels, these tasks could now be accomplished using generic tools with unique configurations, all within one integrated development environment.
Wayve engineers can now share Foxglove links to specific data segments, preloaded with a particular visualization layout. Engineers can simply send a link to share the full trace for a given issue. In addition to fostering collaboration and knowledge-sharing across the company, this feature has helped Foxglove spread organically throughout the organization. Wayve integrated Foxglove links into their existing internal tooling before gradually transitioning completely to Foxglove, making the migration seamless.
Reducing the time to find, visualize, and triage specific behaviors can significantly accelerate the speed of each iteration cycle. With Foxglove’s web-first platform, Wayve can rely on easy-to-access tools to streamline many aspects of their workflow. This reduces the need for engineering resources to maintain data load times, rendering performance, or general tool efficiency.
Wayve can also write lightweight Foxglove user scripts to run quick experiments, such as calculating simple unit conversions or overlaying driving plan visualizations on camera images. If these experiments prove useful long-term, Wayve can eventually migrate these scripts to a back-end processing step.
“Insights from Foxglove have been instrumental in being able to quickly debug issues on our new vehicle platforms.” J’aime Laurenson, Product Lead at Wayve
Nearly 40% of the Wayve engineering team now uses Foxglove in their daily workflows. To achieve their goal of deploying self-driving cars globally, Wayve needs crystal-clear visibility into what is happening under the hood—a critical competency for any self-driving car company. With Foxglove, Wayve has gained this insight through an easy-to-use, lightning-fast, web-based platform that provides top-to-bottom, team-wide visibility.
With Foxglove in their toolkit, Wayve can meet their growing engineering needs. Knowing that Foxglove’s capabilities can scale alongside their own empowers the team to accelerate their iteration speed—fitting for a company striving to change the world.
