Arbe’s ultra-high-resolution radar captures rich environmental information at long range and in all conditions. Arbe’s AI-driven signal processing helps turn that dense radar data into a more usable input for vehicle perception, sensor fusion, and autonomous driving architectures.

Arbe’s perception pre-processing layer helps bridge the gap between radar and camera- or LiDAR-based perception workflows. By creating outputs that are easier to interpret, train on, and integrate, Arbe supports transfer learning and makes radar data more accessible to OEM AI teams.

Radar’s long-range capability is especially valuable for highway and high-speed autonomous driving. Arbe helps detect challenging road hazards such as lost cargo, including low-profile objects like a tire near a highly reflective guardrail, consistently and from long range. This gives the vehicle’s AI stack more time to plan and respond.

Instead of treating all radar data equally, Arbe uses a “course-to-fine” AI processing approach. It first casts a wider net across the radar scene to identify where meaningful radar information is likely to be. Then, it applies more focused processing to the most relevant information. This helps manage dense radar data more efficiently while preserving the detail needed for perception.

Arbe’s high-resolution radar can generate extremely dense raw information, equivalent to up to 3 terabytes per second. Edge AI processing helps convert that data into a more efficient perception output, preserving the information needed for AI while reducing bandwidth and central compute load. This enables dense radar perception without overwhelming the vehicle architecture.

Arbe’s architecture supports a split AI flow: processing radar information at the edge, as well as running on central compute. Data from both layers can feed the sensor fusion system, helping the vehicle build a more complete understanding of the driving environment.