Build Your Own AI-Powered Self-Driving JetBot in 4 Simple Steps
Learn how to assemble the NVIDIA JetBot, install its Docker‑based AI software, train obstacle‑avoidance and path‑tracking models, and create a functional self‑driving miniature vehicle, even if you’re a beginner with basic Python skills.
Autonomous driving has become a hot topic, and even a Bilibili creator recently built a self‑driving bicycle that went viral.
The NVIDIA JetBot is an entry‑level AI car that packs a Jetson Nano computer, enabling millions of AI processes, multiple sensors, and neural networks for object detection and obstacle avoidance.
In essence, JetBot is an assemble‑able AI robot platform.
Hardware Assembly
All you need is some hands‑on ability and basic Python knowledge. NVIDIA provides a parts list and 3D‑printed designs, which can be sourced online.
Software Installation
JetBot runs inside Docker containers that bundle all required deep‑learning libraries, simplifying setup.
Training Obstacle Avoidance
The process consists of three steps:
Collect environment‑related data.
Train a model using the collected data.
Deploy the trained model to the JetBot.
Path Tracking
Path tracking follows the same three‑step workflow:
Collect road‑related data.
Train a model with the data.
Deploy the trained model to the JetBot.
After completing these steps you have a working self‑driving mini‑car, and you can further extend it with features like face recognition or object detection.
Project page: https://www.nvidia.cn/jetbot
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