MulticoreWare

中文

中文

中文

中文

Autonomous Mobility Software - Engineering Autonomous Embodied Intelligence

Autonomous Mobility & Intelligent Vehicles
End-to-End Autonomous Mobility Platform Enablement

L2+ ADAS

In the realm of Advanced Driver Assistance Systems (ADAS), we possess the capability to seamlessly port your algorithms to a specific target hardware. We offer a comprehensive approach with Neural Network optimization and end-to-end real-time performance on diverse semiconductor platforms, ensuring optimal efficiency and responsiveness of the system. Our expertise extends to building algorithms with Birds Eye View projections tailored for micro-mobility, two-wheeled and four-wheeled vehicles.

Features of Advanced Driver Assistance System (ADAS)

ADAS-Features

Blind Spot Detection

We implement efficient and precise low-power 2D object detection using camera-based Neural Network (NN) algorithms. We have accomplished the delivery of a Blind Spot Detection (BSD) for a key automotive Tier 1 entity in the Asia Pacific.

Lane Tracking & Lane Centering

The lane centering feature in ADAS utilizes cutting-edge computer vision and sensor fusion technologies to provide real-time lane detection and tracking. By incorporating intelligent alerts and customizable parameters, we can fine-tune your algorithms for optimal efficiency.

Vulnerable Road Users (VRU)

In order to improve the safety and awareness of pedestrians, cyclists, and other vulnerable road users, our approach integrates a range of sensor technologies like cameras and radars to detect and monitor the movements of pedestrians and cyclists in the vicinity of a vehicle. Leveraging our profound knowledge of sensing technologies and computational hardware, we can optimize the algorithms for this purpose.

Forward Collision Warning (FCW)

FCW is a safety feature found in modern vehicles that utilizes sensors and algorithms to detect potential collisions and alert drivers, preventing rear-end accidents. Our state-of-the-art algorithm utilizes sophisticated sensor fusion and machine learning techniques to accurately detect potential collision risks, enabling timely and effective warnings to drivers.

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