Efficient End-to-End Deep Learning for Autonomous Racing: TinyLidarNet and Low-Power Computing Platforms

Chapter 1 introduces TinyML, emphasizing the shift from large-scale machine learning to embedded, resource-constrained devices. It explores DeepPiCar, a project demonstrating the application of TinyML principles to autonomous driving on low-cost platforms. The chapter details the motivation, setup, and fine-tuning process used to improve DeepPiCar's adaptability in changing environments using a Raspberry Pi Zero 2 W. Key findings highlight the feasibility and efficiency of on-device fine-tuning.

Chapter 2 delves into prior research that has demonstrated the effectiveness of end-to-end deep learning for robotic navigation, where the control signals are directly derived from raw sensory data. However, the majority of existing end-to-end navigation solutions are predominantly camera-based. In this chapter, we introduce TinyLidarNet, a lightweight 2D LiDAR-based end-to-end deep learning model for autonomous racing. We systematically analyze its performance on untrained tracks and computing requirements for real-time processing. We find that TinyLidarNet's 1D Convolutional Neural Network (CNN) based architecture significantly outperforms widely used Multi-Layer Perceptron (MLP) based architecture. In addition, we show that it can be processed in real-time on low-end micro-controller units (MCUs).

Chapter 3 comprehensively analyzes the variation of TinyLidarNet, an enhanced deep learning model optimized for F1TENTH autonomous racing using the ESP32S3 microcontroller and RPLiDAR A1 sensor. It is aimed at addressing challenges like size, weight, power efficiency, and environmental adaptability in the F1TENTH autonomous racing competition. Through a combination of expert driving data and the Dataset Aggregation (DAGger) technique, the model was trained to improve navigation accuracy and processing efficiency. Testing demonstrated that the vehicle could complete multiple laps on both familiar and new tracks without collisions while meeting real-time processing requirements.