Cron AI – Leveraging State-of-the-Art 3D Data Perception Platform for Autonomous Machines

Autonomous mobile platforms are going to be huge part of the future transportation and autonomous navigation is the critical part of autonomous platforms. An autonomous mobile platform navigates the vehicle by perceiving the environment through the sensors mount on the vehicle, and acting on the data it receives from these sensors by making sense of the environmental and surroundings. As a result, an autonomous mobile platform consists of localization aka positioning and path planning. Both of them require very accurate sensor measurements. Different aspects of autonomous driving require machine learning and computer vision technologies. An important part includes the processing of an immense amount of data from cameras, in combination with other sensors (e.g., Lidar), and the learning of driving situations and driver behavior. An in-car automation application of deep learning involves head pose estimation from 2D or 3D depth data through a Convolutional Neural Network (CNN). Common use cases include detecting driver inattention, one of the main causes of traffic accidents. Therefore, real-time driver monitoring systems help to improve driver safety with computer vision.

Cron AI is an adaptive 3D data perception platform company building senseEDGE™ an edge inference 3D sensor data perception processing platform. Architected from the ground up and specifically designed to address the acceleration requirements of 3D sensing perception processing at the edge, the platform supports next-generation applications, sensors, neural networks and algorithms across the mobility, transport infrastructure, smart spaces, automation and security markets. senseEDGE™ is a ground-breaking agnostic (sensor and output), contextually aware, artificially evolving, self-optimizing heterogeneous FPGA-based edge platform. Multiple cameras placed over the production line can be used for defect detection in real-time. The systems monitor the wheel coating intensity, anomalies such as a small decreasing amount of paint that would reveal a sudden problem in the painting process.

senseEDGE™ Advantages

Real-time Traffic Road Signs Detection: The recognition of traffic road signs uses computer vision algorithms to detect road signs and their shape (triangle, square, and rectangle). Traffic signs recognition is an important field of computer vision, especially relevant for autonomous vehicles and Advanced Driver Assistance Systems (ADAS). Cameras for traffic sign detection are utilized in many other applications, such as road safety, or highway asset maintenance and management, to check the condition of signs on major roads.

Real-time prediction of automotive collision risk: Multiple automotive applications, including Advanced Driver Assistance Systems for collision avoidance and warnings, require estimating the future risk of a driving scene. Visual AI systems process the video stream of conventional, dashboard-mounted cameras to predict the collision risk over an intermediate time horizon and support absolute speed estimation.

Ergonomic Risk Assessment: In automotive manufacturing, ergonomic risk assessments help to increase occupational health and safety by reducing the risk of work-related musculoskeletal disorders. Digital video analysis with computer vision and machine learning techniques is used to perform accurate ergonomic risk assessments. Therefore, camera systems with deep learning can perform unsupervised ergonomic assessment ergonomics of multiple workers simultaneously, even in sub-optimal conditions (poor illumination, occlusion). Neural networks detect the worker’s skeletons and body-joint positions and infer angles. The information can be used to perform Rapid Upper Limb Assessment (RULA) evaluation scores.

Track Tool Movement in Automotive Assembly Plants: In automobile assembly, technicians use torque tools to mount bolts to parts of vehicles. Different bolts require varying torque levels to be fastened correctly. Hence, intelligent systems deliver torque levels in a specific order that must be strictly followed by the technician. Because of the pre-specified order, it is critical to follow the correct order precisely. Computer vision applications in automotive have been tested to identify, correct, and document human errors in the bolt securing process. Therefore, the application helps to detect incorrect ordering on an automotive factory assembly line. Visual AI can select a desired order of bolts, detect visitation of a torque tool to each bolt location, and report errors made in the sequence of actions in the vehicle assembly line.

The Leader Upfront   

Tushar Chhabra is a co-founder and the Chief Executive Officer of Cron AI.

“We are building a Deep learning first, artificially evolving, contextually aware edge platform architected to accelerate deep learning 3D sensing data.”