AI training data collection devices play a pivotal role in the advancement of autonomous vehicles. Specifically, they collect, store, and analyze vast amounts of data generated by the vehicle's engine, driving systems, and external perception sensors. This process provides the training data necessary for AI models to understand and predict real-world situations.
For instance, by enabling AI to learn from diverse traffic scenarios, weather conditions, and obstacle detection, autonomous driving algorithms can make more precise and reliable driving decisions.
These data collection devices are utilized not only in the automotive industry but also in diverse fields such as urban infrastructure management, robotics, and drone operations. Data collected in these fields enables the development of advanced features like efficient resource management, safe route planning, and obstacle avoidance.
The block diagram below provides a brief overview of the logging system currently deployed in the field.
[ TELROP Real-Time Data Logging System for Autonomous Vehicle AI Training ]
System Overview and Key Features
The autonomous driving and AI data collection device is a system designed to perceive the surrounding environment and process/collect data through various interfaces, sensors, and high-performance computing capabilities. At the heart of this system lies the NVIDIA Jetson platform. By supporting a wide range of models (NANO, TX2 NX, Xavier NX, AGX Xavier, AGX Orin), it offers a platform tailored to specific usage requirements.
Key Specifications Include:
Camera Support: Support for GMSL and GMSL2 interfaces.
Vehicle Data: CAN 2.0 and CAN FD support for collecting vehicle operation information.
Sensor Connectivity: Ethernet connectivity for Lidar and external IMU integration.
Power & Network: Wide range power input (DC 10~30V) and Multi-path network support via multiple LTE Cat.4 modems.
Storage & Navigation: NVMe and uSD storage options, along with GPS and IMU support.
Remote Management and Data Integrity
The system supports remote monitoring and management via LTE modems, container-based remote software updates, SSH access through the cloud, H.264/H.265 camera video compression, and time-stamp-based logging of sensor data.
Vehicle operation information is collected via the CAN interface, and all data is logged with precise timing information, making it ready for analysis and future training. This entire process can be monitored and managed remotely through LTE and cloud connectivity, allowing for remote execution of software updates when necessary.
In summary, the autonomous driving and AI data collection device integrates various sensor and network technologies based on the high-performance NVIDIA Jetson platform, enabling advanced data collection tailored to specific needs.
sales@telelian.com
AI training data collection devices play a pivotal role in the advancement of autonomous vehicles. Specifically, they collect, store, and analyze vast amounts of data generated by the vehicle's engine, driving systems, and external perception sensors. This process provides the training data necessary for AI models to understand and predict real-world situations.
For instance, by enabling AI to learn from diverse traffic scenarios, weather conditions, and obstacle detection, autonomous driving algorithms can make more precise and reliable driving decisions.
These data collection devices are utilized not only in the automotive industry but also in diverse fields such as urban infrastructure management, robotics, and drone operations. Data collected in these fields enables the development of advanced features like efficient resource management, safe route planning, and obstacle avoidance.
The block diagram below provides a brief overview of the logging system currently deployed in the field.
[ TELROP Real-Time Data Logging System for Autonomous Vehicle AI Training ]
System Overview and Key Features
The autonomous driving and AI data collection device is a system designed to perceive the surrounding environment and process/collect data through various interfaces, sensors, and high-performance computing capabilities. At the heart of this system lies the NVIDIA Jetson platform. By supporting a wide range of models (NANO, TX2 NX, Xavier NX, AGX Xavier, AGX Orin), it offers a platform tailored to specific usage requirements.
Key Specifications Include:
Camera Support: Support for GMSL and GMSL2 interfaces.
Vehicle Data: CAN 2.0 and CAN FD support for collecting vehicle operation information.
Sensor Connectivity: Ethernet connectivity for Lidar and external IMU integration.
Power & Network: Wide range power input (DC 10~30V) and Multi-path network support via multiple LTE Cat.4 modems.
Storage & Navigation: NVMe and uSD storage options, along with GPS and IMU support.
Remote Management and Data Integrity
The system supports remote monitoring and management via LTE modems, container-based remote software updates, SSH access through the cloud, H.264/H.265 camera video compression, and time-stamp-based logging of sensor data.
Vehicle operation information is collected via the CAN interface, and all data is logged with precise timing information, making it ready for analysis and future training. This entire process can be monitored and managed remotely through LTE and cloud connectivity, allowing for remote execution of software updates when necessary.
In summary, the autonomous driving and AI data collection device integrates various sensor and network technologies based on the high-performance NVIDIA Jetson platform, enabling advanced data collection tailored to specific needs.
sales@telelian.com