34 implicitly read the context of the specific situation around the vehicle before gradually learning how to read and react to more hazardous situations. The training data was amassed by LOXO and some key collaborators, including public transport companies who agreed to let LOXO integrate sensors onto its vehicles for collecting pixels on their day-to-day routes, enabling vast quantities of data covering different levels of traffic and countless sporadic events on both roads and pavements. Meanwhile, LOXO’s engineers drove sensor-equipped road vehicles of their own to collect training data, including outside transport routes and hours. “You never know when you’ve gathered enough data, but it’s very important to note that transformer models like the one we use are much more intelligent than classic CNN-type deep-learning models,” Amini says. “That is to say, they’re much faster, and if you give both a CNN and transformer the same quantity of training data to consume, typically nowadays you will find the transformer gains much more intelligence than the former.” Safety software Despite all the advantages of the transformer-based LOXO Fuser technology for autonomous driving and quick, apt decision-making, LOXO notes that the AI model is not conducive to effectively demonstrating compliance with functional safety standards such as ISO 26262 to regulatory and certifying bodies (those bodies being especially strict in Europe). “That’s because around 40% of the functional safety requirements are, by the way they’re defined, not presently applicable to our AI model. Therefore, we’ve developed another subsystem in parallel with LOXO Fuser, which we call LOXO Guard,” Amini says. “LOXO Guard is purely deterministic software, which takes responsibility over implementing functional safety requirements. It doesn’t do the same tasks that LOXO Fuser does; instead, it monitors the latter’s command outputs before they get sent to the main control unit on the vehicle in a fashion that meets the outstanding regulatory definitions laid out across those functional safety requirements that the Fuser doesn’t precisely capture.” LOXO Guard additionally uses the onboard, CNN-based perception system in standard operations as a valuable point of reference, against which the LOXO Fuser’s decisions can be validated. In addition to LOXO Fuser, the company has developed its own supervision software (analogous to GCS software systems we have previously featured), to cover the remaining, outlier situations that the Alpha and other LDD-powered self-driving vehicles may not know how to handle. Using this software, a remote supervisor can monitor the vehicle’s surroundings and activities, and send decisions if needed. As a reminder, conventional image and pointcloud data are still generated by the sensors onboard the vehicle. These can be viewed by the supervisor at their station in real time, except for April/May 2025 | Uncrewed Systems Technology LOXO’s primary autonomy layer is a transformer-based algorithm, which reads the raw, unprocessed pixels of data from sensors and reacts based on which pixels matter the most for safe driving Processed sensor data can still be used by the vehicle and a remote supervisor to inform decisionmaking, although ultimate authority lies with the vehicle to prevent unsafe interventions
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