AI Shows the Way: Seoul Robotics Helps Cars Move, Park on Their Own

Summary

This article discusses how Seoul Robotics is using NVIDIA technology to turn non-autonomous cars into self-driving vehicles. The company's Level 5 Control Tower is a mesh network of sensors and computers placed on infrastructure around a facility, like buildings or light poles, to capture an unobstructed view of the environment. The system enables cars to move autonomously by directing their vehicle-to-everything, or so-called V2X, communication systems. These systems pass information from a vehicle to infrastructure, other vehicles, any surrounding entities - and vice versa. V2X technology, which comes standard in many modern cars, is used to improve road safety, traffic efficiency and energy savings. Seoul Robotics' platform, dubbed LV5 CTRL TWR, collects 3D data from the environment using cameras and lidar. Computer vision and deep learning-based AI analyze the data, determining the most efficient and safest paths for vehicles within the covered area. Then, through V2X, the platform can manage a car's existing features, such as adaptive-cruise-control, lane-keeping and brake-assist functions, to safely get it from place to place. LV5 CTRL TWR is built using NVIDIA CUDA libraries for creating GPU-accelerated applications, as well as the Jetson AGX Orin module for high-performance AI at the edge. NVIDIA GPUs are used in the cloud for global fleet path planning. Seoul Robotics is a member of NVIDIA Metropolis - a partner program centered on an application framework and set of developer tools that supercharge vision AI applications - and NVIDIA Inception, a free, global program that nurtures cutting-edge startups.

Q&As

AI Comments

👍 The article discusses how a new software company, Seoul Robotics, is using NVIDIA technology to turn non-autonomous cars into self-driving vehicles. The company's initial focus is on improving first- and last-mile logistics such as parking. The article discusses how the company's platform, LV5 CTRL TWR, collects 3D data from the environment using cameras and lidar, and how computer vision and deep learning-based AI analyze the data to determine the most efficient and safest paths for vehicles within the covered area. The article discusses how the platform is built using NVIDIA CUDA libraries for creating GPU-accelerated applications, as well as the Jetson AGX Orin module for high-performance AI at the edge. The article discusses how the company's technology is in early commercial deployment at a BMW manufacturing facility in Munich, and how the technology can be used across industries.

👎 The article does not mention any potential drawbacks of the technology.

AI Discussion

Me: It's about a company called Seoul Robotics that is using NVIDIA technology to turn non-autonomous cars into self-driving vehicles.

Friend: That's interesting. What are the implications of this?

Me: The implications of this are that the company is able to use existing features in cars to make them autonomous, without the need for expensive sensors or hardware. This could potentially revolutionize the automotive industry, making cars much cheaper and easier to produce.

Action items

• Join NVIDIA Inception to get access to early software development kits.
• Use the LV5 CTRL TWR to automate car transfers at manufacturing facilities.
• Deploy the LV5 CTRL TWR across industries to save time and money.

Technical terms

Autonomous

able to operate without human input

Self-driving

able to drive without human input

Vehicle-to-everything (V2X)

communication system that passes information from a vehicle to infrastructure, other vehicles, and any surrounding entities

Adaptive cruise control

a feature that automatically adjusts a vehicle's speed to maintain a safe distance from the vehicle in front of it

Lane keeping

a feature that uses sensors to detect lane markings and keep the vehicle within its lane

Brake assist

a feature that uses sensors to detect when a vehicle is stopping and applies the brakes automatically if the driver does not brake hard enough

Computer vision

the ability of a computer to interpret and understand digital images

Deep learning

a subset of machine learning that uses algorithms to model high-level abstractions in data

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