A suite of deep neural networks for real-time object detection, classification, lane detection, and free space estimation from camera and sensor data.
A single neural network architecture that performs multiple perception tasks (like detecting vehicles, pedestrians, and lanes) simultaneously from the same input image.
The software is trained and validated to maintain reliable detection accuracy in challenging lighting (night, glare) and weather conditions (rain, snow, fog).
SVNet is designed to be portable across various automotive-grade System-on-Chips (SoCs) from different manufacturers like NVIDIA, Qualcomm, and Texas Instruments.
Stradvision provides proprietary tools and services to help clients annotate driving data and fine-tune the perception models for specific geographic regions or unique vehicle types.
Automakers integrate SVNet to power Level 2+ ADAS features like Automatic Emergency Braking (AEB), Adaptive Cruise Control (ACC), and Lane Keeping Assist (LKA). The software identifies vehicles, pedestrians, cyclists, and lane markings in real-time, providing the critical perception layer that enables these safety systems to function. This helps reduce accidents and enhances driver comfort on highways and in urban environments.
Companies developing Level 3+ autonomous driving systems use Stradvision's software as a core component of their perception stack. SVNet provides a reliable, automotive-grade understanding of the vehicle's 360-degree environment, which is foundational for path planning and decision-making algorithms. Its efficiency allows more computational budget to be allocated to other complex autonomous functions.
Truck, bus, and delivery van manufacturers implement SVNet to improve safety and operational efficiency. Features like blind-spot detection, pedestrian warning systems at low speeds, and lane departure warnings are crucial for large vehicles operating in complex environments like city centers and distribution hubs, helping to protect vulnerable road users and cargo.
SVNet's precise object and free-space detection capabilities are used to enable advanced parking features. This includes Surround View Monitoring (SVM) with object detection, automated parking assist, and valet parking systems. The software helps the vehicle navigate tight spaces by accurately identifying parking slot lines, curbs, and obstacles in all directions.
Providers of autonomous ride-hailing and people-mover services rely on robust, scalable perception software like SVNet. It allows these driverless vehicles to safely navigate dynamic public roads, interact with other traffic, and pick up/drop off passengers. The software's ability to run on efficient hardware helps keep the overall vehicle platform cost-effective.
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