A Review on DonkeyCar Autonomous Driving Platform Using Neural Network
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Abstract
DonkeyCar is a DIY autonomous vehicle platform for exploring AI-driven self-driving technology. Using the DonkeyCar Unity simulator, models can be trained and tested in a risk-free environment before real-world deployment. This study examines the impact of training data and track complexity on model performance. Results show that increasing the number of training images from 1,346 to 5,351 reduced the error rate from 36% to 3%, thereby improving driving stability. Models trained on complex tracks adapted better but required longer training. However, challenges such as sensor noise, environmental uncertainties, and limitations in obstacle avoidance suggest the need for data augmentation, reinforcement learning, and multi-sensor fusion to enhance model robustness. These findings contribute to optimizing low-cost, vision-based autonomous driving systems.
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