Data to 7 Oct 2026

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

arXiv 2004.08467 ↗First posted 17 Apr 2020cs.ROcited by 100

Not on the map. Why?

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.

Key facts. Cited by 100 papers in the map (data through 7 Oct 2026). First posted on arXiv 17 Apr 2020.

Builds on

No reference list yet.

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 8; 2022: 13; 2023: 18; 2024: 19; 2025: 30; 2026: 12.

100 papers.

Sort:

By area:

2021T"oRF: Time-of-Flight Radiance Fields for Dynamic Scene View Synthesiscited by 88
2024A Survey on Autonomous Driving Datasets: Statistics, Annotation Quality, and a Future Outlookcited by 65
2021A Survey on Deep Domain Adaptation for LiDAR Perceptioncited by 46
2021ConDA: Unsupervised Domain Adaptation for LiDAR Segmentation via Regularized Domain Concatenationcited by 41
2023Among Us: Adversarially Robust Collaborative Perception by Consensuscited by 37
2023Hidden Gems: 4D Radar Scene Flow Learning Using Cross-Modal Supervisioncited by 36
2024Complementarity in Human-AI Collaboration: Concept, Sources, and Evidencecited by 35
2025Towards Robust and Secure Embodied AI: A Survey on Vulnerabilities and Attackscited by 32
2023Towards Knowledge-driven Autonomous Drivingcited by 29
2022SELMA: SEmantic Large-scale Multimodal Acquisitions in Variable Weather, Daytime and Viewpointscited by 22
2023Compositional Semantic Mix for Domain Adaptation in Point Cloud Segmentationcited by 20
2025SuperPC: A Single Diffusion Model for Point Cloud Completion, Upsampling, Denoising, and Colorizationcited by 20

38 more

2022Emergent Visual Sensors for Autonomous Vehiclescited by 18
2022How Do We Fail? Stress Testing Perception in Autonomous Vehiclescited by 17
2022GRASP-Net: Geometric Residual Analysis and Synthesis for Point Cloud Compressioncited by 17
2021Full-Velocity Radar Returns by Radar-Camera Fusioncited by 16
2023Instant Domain Augmentation for LiDAR Semantic Segmentationcited by 16
2023A Survey on Approximate Edge AI for Energy Efficient Autonomous Driving Servicescited by 14
2023You Only Look at Once for Real-time and Generic Multi-Taskcited by 14
2024GroundGrid:LiDAR Point Cloud Ground Segmentation and Terrain Estimationcited by 14
2023MiliPoint: A Point Cloud Dataset for mmWave Radarcited by 13
2024Prompting Multi-Modal Tokens to Enhance End-to-End Autonomous Driving Imitation Learning with LLMscited by 13
2025Cross-Modal and Uncertainty-Aware Agglomeration for Open-Vocabulary 3D Scene Understandingcited by 13
2023On Data Fabrication in Collaborative Vehicular Perception: Attacks and Countermeasurescited by 11
2023Deep Transfer Learning for Intelligent Vehicle Perception: a Surveycited by 10
2025Collaborative Perception Datasets for Autonomous Driving: A Reviewcited by 8
2021High-speed object detection with a single-photon time-of-flight image sensorcited by 7
2021Adaptive Gating for Single-Photon 3D Imagingcited by 7
2025Hyperdimensional Uncertainty Quantification for Multimodal Uncertainty Fusion in Autonomous Vehicles Perceptioncited by 6
2022Anytime-Lidar: Deadline-aware 3D Object Detectioncited by 5
2022Risk-Driven Design of Perception Systemscited by 4
2023ReBound: An Open-Source 3D Bounding Box Annotation Tool for Active Learningcited by 4
2024RePLAy: Remove Projective LiDAR Depthmap Artifacts via Exploiting Epipolar Geometrycited by 4
2022Verifiable Obstacle Detectioncited by 3
2024MaskFuser: Masked Fusion of Joint Multi-Modal Tokenization for End-to-End Autonomous Drivingcited by 3
2024A Survey on Reinforcement Learning Applications in SLAMcited by 3
2024VALO: A Versatile Anytime Framework for LiDAR-based Object Detection Deep Neural Networkscited by 3
2024Optical Lens Attack on Deep Learning Based Monocular Depth Estimationcited by 3
2025CuRLA: Curriculum Learning Based Deep Reinforcement Learning for Autonomous Drivingcited by 3
2025Robust 3D Object Detection using Probabilistic Point Clouds from Single-Photon LiDARscited by 3
2025D2GS: Dense Depth Regularization for LiDAR-free Urban Scene Reconstructioncited by 3
2026The Era of End-to-End Autonomy: Transitioning from Rule-Based Driving to Large Driving Modelscited by 3
2021CAR -- Cityscapes Attributes Recognition A Multi-category Attributes Dataset for Autonomous Vehiclescited by 2
2022A Review of Mobile Mapping Systems: From Sensors to Applicationscited by 2
2022Efficient Perception, Planning, and Control Algorithm for Vision-Based Automated Vehiclescited by 2
2023DeepIPCv2: LiDAR-powered Robust Environmental Perception and Navigational Control for Autonomous Vehiclecited by 2
2023Autonomous Advanced Aerial Mobility -- An End-to-end Autonomy Framework for UAVs and Beyondcited by 2
2024A Benchmark Grocery Dataset of Realworld Point Clouds From Single Viewcited by 2
2025Knowledge-Informed Multi-Agent Trajectory Prediction at Signalized Intersections for Infrastructure-to-Everythingcited by 2
2025Point Cloud Based Scene Segmentation: A Surveycited by 2

50 most cited of 100 All 100 (JSON)