# Lidar for Autonomous Driving: The principles, challenges, and trends for automotive lidar and perception systems

arXiv: https://arxiv.org/abs/2004.08467 · HTML: https://mlsanity.com/paper/2004.08467/

## Key facts

- Cited by 100 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Apr 2020.

## Built on by (most cited first, 30 of 100)

- [T"oRF: Time-of-Flight Radiance Fields for Dynamic Scene View Synthesis](https://mlsanity.com/paper/2109.15271/) (2021)
- [A Survey on Autonomous Driving Datasets: Statistics, Annotation Quality, and a Future Outlook](https://mlsanity.com/paper/2401.01454/) (2024)
- [A Survey on Deep Domain Adaptation for LiDAR Perception](https://mlsanity.com/paper/2106.02377/) (2021)
- [ConDA: Unsupervised Domain Adaptation for LiDAR Segmentation via Regularized Domain Concatenation](https://mlsanity.com/paper/2111.15242/) (2021)
- [Among Us: Adversarially Robust Collaborative Perception by Consensus](https://mlsanity.com/paper/2303.09495/) (2023)
- [Hidden Gems: 4D Radar Scene Flow Learning Using Cross-Modal Supervision](https://mlsanity.com/paper/2303.00462/) (2023)
- [Complementarity in Human-AI Collaboration: Concept, Sources, and Evidence](https://mlsanity.com/paper/2404.00029/) (2024)
- [Towards Robust and Secure Embodied AI: A Survey on Vulnerabilities and Attacks](https://mlsanity.com/paper/2502.13175/) (2025)
- [Towards Knowledge-driven Autonomous Driving](https://mlsanity.com/paper/2312.04316/) (2023)
- [SELMA: SEmantic Large-scale Multimodal Acquisitions in Variable Weather, Daytime and Viewpoints](https://mlsanity.com/paper/2204.09788/) (2022)
- [SuperPC: A Single Diffusion Model for Point Cloud Completion, Upsampling, Denoising, and Colorization](https://mlsanity.com/paper/2503.14558/) (2025)
- [Compositional Semantic Mix for Domain Adaptation in Point Cloud Segmentation](https://mlsanity.com/paper/2308.14619/) (2023)
- [Emergent Visual Sensors for Autonomous Vehicles](https://mlsanity.com/paper/2205.09383/) (2022)
- [How Do We Fail? Stress Testing Perception in Autonomous Vehicles](https://mlsanity.com/paper/2203.14155/) (2022)
- [GRASP-Net: Geometric Residual Analysis and Synthesis for Point Cloud Compression](https://mlsanity.com/paper/2209.04401/) (2022)
- [Full-Velocity Radar Returns by Radar-Camera Fusion](https://mlsanity.com/paper/2108.10637/) (2021)
- [Instant Domain Augmentation for LiDAR Semantic Segmentation](https://mlsanity.com/paper/2303.14378/) (2023)
- [A Survey on Approximate Edge AI for Energy Efficient Autonomous Driving Services](https://mlsanity.com/paper/2304.14271/) (2023)
- [You Only Look at Once for Real-time and Generic Multi-Task](https://mlsanity.com/paper/2310.01641/) (2023)
- [GroundGrid:LiDAR Point Cloud Ground Segmentation and Terrain Estimation](https://mlsanity.com/paper/2405.15664/) (2024)
- [MiliPoint: A Point Cloud Dataset for mmWave Radar](https://mlsanity.com/paper/2309.13425/) (2023)
- [Prompting Multi-Modal Tokens to Enhance End-to-End Autonomous Driving Imitation Learning with LLMs](https://mlsanity.com/paper/2404.04869/) (2024)
- [Cross-Modal and Uncertainty-Aware Agglomeration for Open-Vocabulary 3D Scene Understanding](https://mlsanity.com/paper/2503.16707/) (2025)
- [On Data Fabrication in Collaborative Vehicular Perception: Attacks and Countermeasures](https://mlsanity.com/paper/2309.12955/) (2023)
- [Deep Transfer Learning for Intelligent Vehicle Perception: a Survey](https://mlsanity.com/paper/2306.15110/) (2023)
- [Collaborative Perception Datasets for Autonomous Driving: A Review](https://mlsanity.com/paper/2504.12696/) (2025)
- [High-speed object detection with a single-photon time-of-flight image sensor](https://mlsanity.com/paper/2107.13407/) (2021)
- [Adaptive Gating for Single-Photon 3D Imaging](https://mlsanity.com/paper/2111.15047/) (2021)
- [Hyperdimensional Uncertainty Quantification for Multimodal Uncertainty Fusion in Autonomous Vehicles Perception](https://mlsanity.com/paper/2503.20011/) (2025)
- [Anytime-Lidar: Deadline-aware 3D Object Detection](https://mlsanity.com/paper/2208.12181/) (2022)

## Abstract (arXiv)

Autonomous vehicles rely on their perception systems to acquire information about their immediate surroundings. It is necessary to detect the presence of other vehicles, pedestrians and other relevant entities. Safety concerns and the need for accurate estimations have led to the introduction of Light Detection and Ranging (LiDAR) systems in complement to the camera or radar-based perception systems. This article presents a review of state-of-the-art automotive LiDAR technologies and the perception algorithms used with those technologies. LiDAR systems are introduced first by analyzing the main components, from laser transmitter to its beam scanning mechanism. Advantages/disadvantages and the current status of various solutions are introduced and compared. Then, the specific perception pipeline for LiDAR data processing, from an autonomous vehicle perspective is detailed. The model-driven approaches and the emerging deep learning solutions are reviewed. Finally, we provide an overview of the limitations, challenges and trends for automotive LiDARs and perception systems.