AVS.AUTO

AVS is a new standard for describing and visualizing autonomous vehicle perception, motion, and planning data, offering a powerful web-based toolkit to build applications for exploring, interacting and, most critically, making important development decisions with that data. The AVS projects are designed for applications and workflows that autonomy engineers and operators work with to explore, analyze, and compare autonomous systems behavior and performance. As a stand-alone, standardized visualization layer, AVS frees developers from having to build custom visualization software for their autonomous vehicles. With AVS abstracting visualization, developers can focus on core autonomy capabilities for drive systems, remote assistance, mapping, and simulation. We built our system around two key pieces: XVIZ provides the data (including management and specification) while streetscape.gl is the component toolkit to power web applications. AVS was designed to be open and modular, welcoming contributions from internal teams since the beginning of its development to enable decoupling. Architecturally, it provides a layered approach where the coupling between components of the autonomous stack is minimized and offers clear definitions for the exchange of data. Each layer can evolve as needed without requiring system-wide changes, and layers can be tailored for a specific context or us -case. This guiding principle helps set AVS apart from current solutions. Specifically, AVS’ architecture distinguishes itself because: It was designed with an intentional separation of data from any underlying platform Its limited, small specifications make tools easier to develop Its data format requirements result in fast transfer and processing Additionally, we created AVS to meet the needs of everyone in the autonomy ecosystem, including engineers, vehicle operators, analysts, and specialist developers. Autonomous engineers can describe their systems with XVIZ easily, and then test and visualize their expectations with limited overhead. Specialist developers can quickly build data source-agnostic applications with strong performance characteristics and simplified integration using streetscape.gl. Lastly, operators can view the data in standard visual formats, including videos, across multiple applications, leading to easier collaboration, knowledge understanding, deeper analysis, and overall trust in the data quality. By open sourcing it to the industry, we encourage more to contribute and build upon this initial set of ideas.