Aeye provides an AI‑driven computer‑vision platform that fuses camera, LiDAR, and radar data to deliver object detection, multi‑object tracking, and semantic scene understanding with sub‑30 ms latency on edge hardware. The solution includes a ROS 2 and gRPC SDK for seamless integration into autonomous vehicle, robotics, and drone control loops, and supports continuous learning, safety monitoring, and functional‑safety certification.
Funding
Funding not disclosed
Founders
Product
Problem
Autonomous platforms often struggle with real‑time perception due to fragmented sensor pipelines and computational bottlenecks, which can compromise safety and reduce operational uptime. Existing vision stacks may require extensive tuning and lack the latency guarantees needed for high‑speed decision making.
Solution
Aeye delivers an AI‑driven computer‑vision platform that ingests synchronized camera, LiDAR, and radar streams and produces unified object detection, multi‑object tracking, and semantic scene understanding outputs within milliseconds. The solution runs on edge‑optimized deep‑learning models that are compiled for heterogeneous accelerators, ensuring deterministic latency on embedded hardware. A modular SDK exposes standardized ROS 2 and gRPC interfaces, allowing OEMs to embed perception directly into control loops without custom integration work. Continuous learning pipelines automatically incorporate field data to improve model accuracy while preserving data privacy. Built‑in safety monitors flag anomalous sensor conditions and trigger graceful degradation, supporting functional‑safety certifications for automotive and industrial robotics.
Target Audience
Primary customers are autonomous‑vehicle manufacturers, industrial robotics integrators, and advanced drone system developers that require reliable, low‑latency perception for safety‑critical operations.
Features
- Multi‑modal sensor fusion engine that aligns camera, LiDAR, and radar data in a common 3‑D space
- Edge‑optimized convolutional and transformer architectures delivering <30 ms end‑to‑end inference on automotive‑grade SoCs
- ROS 2‑compatible SDK with pre‑built perception nodes, gRPC APIs, and C++/Python bindings for rapid integration
- Continuous learning framework with automated data labeling, model retraining, and OTA deployment pipelines
- Built‑in safety runtime that monitors sensor health, detects out‑of‑distribution inputs, and initiates fail‑safe modes
- Support for functional safety standards (ISO 26262, IEC 61508) with deterministic execution and traceable model provenance
- Scalable licensing model that includes per‑device runtime and optional cloud‑based analytics for fleet monitoring