Description The Waveshare PiRacer Pro AI Racing Robot Powered by Raspberry Pi 4 is a professional Raspberry Pi 4 AI racing robot for DonkeyCar, TensorFlow, Python deep learning and self-driving experiments. This PiRacer Pro AI Kit uses a high-speed racing chassis, 5MP wide-angle camera and Raspberry Pi control architecture for autonomous racing development. The AI self-driving robot kit supports manual driving, data collection, model training and autonomous road-following workflows. Compared with a basic mobile robot, PiRacer Pro focuses on high-speed AI racing, Ackerman steering, 4WD drive, front and rear differentials, wireless gamepad control and camera-based road perception. Three variants are available: With PI4B 3GB RAM, With PI4B 4GB RAM, and Without PI4B. Choose the included Raspberry Pi 4 version for a complete ready-to-build kit, or select Without PI4B if you already have a compatible Raspberry Pi 4. The 18650 batteries are not included. Features PiRacer Pro AI Kit supports DonkeyCar project workflows. Designed for TensorFlow, Python, deep learning and self-driving experiments. 5MP 160-degree FOV camera for road image capture. High-speed racing chassis with Ackerman steering, 4WD and front/rear differentials. Wireless gamepad included for manual driving and data collection. PiRacer Pro expansion board supports rechargeable battery use and battery voltage monitoring. Supports Raspberry Pi OS and Raspberry Pi 4 Model B based development. Available with PI4B 3GB, PI4B 4GB or Without PI4B. Specifications Product name Waveshare PiRacer Pro AI Racing Robot Powered by Raspberry Pi 4 Product type Raspberry Pi 4 AI racing robot kit Options With PI4B (3GB RAM), With PI4B (4GB RAM), Without PI4B AI framework DonkeyCar project, TensorFlow, Python Operating system Raspberry Pi OS / Raspbian Camera 5MP HD resolution, 160-degree FOV wide-angle camera Display OLED status display support Steering and drive Ackerman steering, high-speed motors 4WD, front and rear axle differentials Motor RC380 high-speed carbon brushed motor, idle speed 15000RPM Battery requirement 8.4V, 18650 battery x4, two in parallel and two in series, not included Battery note Battery length should be less than 67mm Package weight 2.649 kg Applications AI racing robot education: teach data collection, model training and autonomous driving. DonkeyCar projects: build a Raspberry Pi based self-driving car platform. Computer vision experiments: use the 5MP wide-angle camera for road and track perception. Python and TensorFlow learning: connect software training with real robot control. Maker and lab demos: demonstrate high-speed autonomous racing workflows. Product List With PI4B variants Raspberry Pi 4 Model B, RAM depends on selected variant; TF card 32GB Without PI4B variant Raspberry Pi 4 Model B and TF card are not included Common kit items On-road chassis; PiRacer Pro expansion board; RPi Camera (G); acrylic camera spacer; camera holder; 8.4V battery charger + EU head; wireless gamepad; 6Pin cable; spanner; screwdriver x2; TF card reader; screws pack; track map Not included 18650 batteries Other ContentBattery note: 18650 batteries are not included. The official page notes that battery length should be less than 67mm, and some batteries with protection plates are not supported.Variant note: the 3GB and 4GB variants include Raspberry Pi 4 Model B. The Without PI4B variant is intended for users who already own a compatible Raspberry Pi 4.Development note: use the official wiki for assembly, software setup, DonkeyCar configuration, data collection and model training steps.AI racing platform: The overview image shows the PiRacer Pro AI racing robot chassis, Raspberry Pi 4 mounting area, camera module and professional self-driving car layout.Raspberry Pi optional variants: The kit can be selected with Raspberry Pi 4B RAM options or without PI4B, so buyers can match the AI racing robot to their existing hardware.Pro chassis details: PiRacer Pro uses a high-speed racing chassis with Ackerman steering, 4WD, front and rear differentials and a camera-ready upper structure.DonkeyCar and TensorFlow: The official detail image highlights DonkeyCar project support, Python development and TensorFlow-based deep learning for autonomous racing.Software workflow: PiRacer Pro AI Kit is designed for data collection, model training and self-driving experiments on Raspberry Pi OS.Gamepad control: The kit includes a wireless gamepad for manual driving, data collection and model-validation runs before autonomous control.OLED and status feedback: OLED status display support helps show system state, battery information and debugging feedback during robotics experiments.Expansion board: The PiRacer Pro expansion board integrates battery monitoring, charging support and cleaner wiring for easier assembly.5MP wide-angle camera: The 5MP 160-degree FOV camera provides the vision input needed for DonkeyCar, road following and AI racing experiments.Battery safety: Use compatible 18650 batteries shorter than 67mm; batteries with some protection plates are not supported.Robot selection guide: The selection guide compares PiRacer and PiRacer Pro and explains why the Pro version is suited for higher-speed AI racing.Full kit package: The package image helps confirm the included racing chassis, expansion board, camera, gamepad, charger and accessories.PI4B 3GB kit package: The PI4B 3GB package image shows the Raspberry Pi 4B included configuration and its accessory set.Resources Waveshare PiRacer Pro AI Kit Wiki Waveshare GitHub FAQWhat is the difference between the PiRacer Pro variants?The difference is whether the kit includes Raspberry Pi 4B 3GB, Raspberry Pi 4B 4GB, or no Raspberry Pi board.Are 18650 batteries included?No. Four compatible 18650 batteries are required and are not included.Does the PiRacer Pro AI Kit support DonkeyCar?Yes. The official page lists DonkeyCar project support for AI autonomous racing experiments.What camera is included?The kit includes an RPi Camera (G), described by Waveshare as a 5MP camera with 160-degree FOV.Is this suitable for deep learning projects?Yes. It is designed for TensorFlow, Python, data collection, model training and self-driving robot workflows.
