# TinyLidarNet: 2D LiDAR-based End-to-End Deep Learning Model for F1TENTH Autonomous Racing

arXiv: https://arxiv.org/abs/2410.07447 · HTML: https://mlsanity.com/paper/2410.07447/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 31 references that are in the map.
- First posted on arXiv 9 Oct 2024.

## Builds on (most cited first, 16 of 16)

- [End to End Learning for Self-Driving Cars](https://mlsanity.com/paper/1604.07316/) (2016)
- [End-to-End Training of Deep Visuomotor Policies](https://mlsanity.com/paper/1504.00702/) (2015)
- [End-to-end Autonomous Driving: Challenges and Frontiers](https://mlsanity.com/paper/2306.16927/) (2023)
- [End-to-end Driving via Conditional Imitation Learning](https://mlsanity.com/paper/1710.02410/) (2017)
- [HG-DAgger: Interactive Imitation Learning with Human Experts](https://mlsanity.com/paper/1810.02890/) (2018)
- [TensorFlow Lite Micro: Embedded Machine Learning on TinyML Systems](https://mlsanity.com/paper/2010.08678/) (2020)
- [Autonomous Vehicles on the Edge: A Survey on Autonomous Vehicle Racing](https://mlsanity.com/paper/2202.07008/) (2022)
- [How Simulation Helps Autonomous Driving:A Survey of Sim2real, Digital Twins, and Parallel Intelligence](https://mlsanity.com/paper/2305.01263/) (2023)
- [Efficient and Robust LiDAR-Based End-to-End Navigation](https://mlsanity.com/paper/2105.09932/) (2021)
- [The NVIDIA PilotNet Experiments](https://mlsanity.com/paper/2010.08776/) (2020)
- [A Benchmark Comparison of Imitation Learning-based Control Policies for Autonomous Racing](https://mlsanity.com/paper/2209.15073/) (2022)
- [DeepPicar: A Low-cost Deep Neural Network-based Autonomous Car](https://mlsanity.com/paper/1712.08644/) (2017)
- [Unifying F1TENTH Autonomous Racing: Survey, Methods and Benchmarks](https://mlsanity.com/paper/2402.18558/) (2024)
- [Towards End-to-End Deep Learning for Autonomous Racing: On Data Collection and a Unified Architecture for Steering and Throttle Prediction](https://mlsanity.com/paper/2105.01799/) (2021)
- [Stress Testing Autonomous Racing Overtake Maneuvers with RRT](https://mlsanity.com/paper/2110.01095/) (2021)
- [DeepPicarMicro: Applying TinyML to Autonomous Cyber Physical Systems](https://mlsanity.com/paper/2208.11212/) (2022)

## Built on by (most cited first, 6 of 6)

- [Efficient Real-World Autonomous Racing via Attenuated Residual Policy Optimization](https://mlsanity.com/paper/2603.12960/) (2026)
- [Physics-Informed Reinforcement Learning of Spatial Density Velocity Potentials for Map-Free Racing](https://mlsanity.com/paper/2604.09499/) (2026)
- [Depth Matters: Multimodal RGB-D Perception for Robust Autonomous Agents](https://mlsanity.com/paper/2503.16711/) (2025)
- [Accelerating Real-World Overtaking in F1TENTH Racing Employing Reinforcement Learning Methods](https://mlsanity.com/paper/2510.26040/) (2025)
- [Real-Time 2D LiDAR Object Detection Using Three-Frame RGB Scan Encoding](https://mlsanity.com/paper/2602.02167/) (2026)
- [Continual-RL for Generalization in Autonomous Racing on the RoboRacer Platform](https://mlsanity.com/paper/2607.24320/) (2026)

## Abstract (arXiv)

Prior research 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 paper, we introduce TinyLidarNet, a lightweight 2D LiDAR-based end-to-end deep learning model for autonomous racing. An F1TENTH vehicle using TinyLidarNet won 3rd place in the 12th F1TENTH Autonomous Grand Prix competition, demonstrating its competitive performance. 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).