Transportation


The Edge AI appliance is designed to revolutionize computer vision solutions for both vehicles and railways. This innovative platform combines the power of rugged design, AI acceleration, rich I/O interfaces, and wireless connectivity with real-time processing capabilities, paving the way for in-vehicle computer vision solutions with reduced latency and improved decision-making.

 

 

Urban mobility is increasingly strained by congestion, traffic violations, and inefficient parking—challenges that demand smarter, real-time traffic management. Lanner, together with DataFromSky, offers an advanced AI-powered solution that turns standard IP cameras into intelligent traffic sensors. By processing video at the edge, directly inside traffic controllers, this joint solution delivers immediate insights and automated control capabilities for modern cities.

The Solution

At the heart of this deployment is the Lanner EAI-I131, a rugged Edge AI computer powered by NVIDIA Jetson Orin, capable of delivering up to 100 TOPS of AI performance. Integrated with DataFromSky’s Flow platform, this solution collects real-time video data from multiple cameras and runs deep learning models right at the edge, removing the need for cloud-based processing.

This enables real-time traffic analysis across a variety of use cases:

Intersection Monitoring: Optimize traffic light timing based on real-time queue detection and flow analysis to increase throughput at busy intersections.

Tunnel Safety: Detect smoke, fire, fallen objects, pedestrian intrusions, or any abnormal events inside tunnels using AI-based object tracking.

Road Surveillance: Identify and report traffic violations like speeding or improper lane changes instantly, supporting law enforcement and safety initiatives.

Parking Management: Accurately detect free and occupied spaces to streamline parking operations and reduce idle driving time.

Edge AI Appliance EAI-I131

The EAI-I131 is purpose-built for rugged environments, with a fanless IP40-rated design and an extended temperature range of -40°C to 75°C. It seamlessly integrates with the NVIDIA JetPack SDK, providing access to Jetson Linux, CUDA-X libraries, and powerful AI tools. Rich I/O options, including PoE, COM, USB, and wireless support (LTE/5G/Wi-Fi), make it the ideal all-in-one platform for edge AI traffic deployments.

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Rail service providers are facing increasing cybersecurity risks within their wayside signaling and train control infrastructure. With thousands of rail-specific assets and applications distributed across a wide network, there is a pressing need for continuous threat detection and monitoring to ensure network integrity and passenger safety.

Rail Obstacle Detection refers to the process of alerting railway operators about obstacles or obstructions present on the railway tracks. These obstacles can range from debris and fallen branches to unauthorized vehicles or individuals trespassing on the tracks.

We will be reviewing a machine vision system for light rail collision avoidance. This solution allows a light rail to avoid unexpected obstacles down the rail and reduce the chances of a collision. It uses an onboard industrial-grade edge AI appliance EAI-I130 provided by Lanner, capable of running MV models onboard. This appliance brings machine vision capabilities as close as possible to the light rail, allowing data captured by sensors to be processed right on-site.

Continuous surging growth in urban populations creates a demand for urban mobility, a greater demand for faster traffic flow, improved public safety, and more innovative transportation. A good urban traffic management network will not only improve traffic safety and boost local economies, but it will also improve public health and protect the environment.

Countless mines worldwide are already deploying autonomous driving mining vehicles to improve productivity and safety. In this post, we will review an autonomous driving vehicle solution that relies on different components, including in-vehicle computing, devices, networks, and the cloud.

Boats and ships traveling offshore usually have limited access to fast Internet connections. Without fast internet, they can’t benefit from what cloud computing has to offer, including data analytics and AI/ML technologies. But now, thanks to edge computing and improvements in wireless communications, the maritime industry is starting to introduce words like “smart” and “autonomous” into their dictionaries— you’ll now hear: smart boats, autonomous ships, etc.

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