mlsanityData to 7 Oct 2026

A Comprehensive Review of 3D Object Detection in Autonomous Driving: Technological Advances and Future Directions

arXiv 2408.16530 ↗First posted 28 Aug 2024cs.CVcited by 2

On the map: End-to-end autonomous driving → VLMs for autonomous driving

Abstract (arXiv)

In recent years, 3D object perception has become a crucial component in the development of autonomous driving systems, providing essential environmental awareness. However, as perception tasks in autonomous driving evolve, their variants have increased, leading to diverse insights from industry and academia. Currently, there is a lack of comprehensive surveys that collect and summarize these perception tasks and their developments from a broader perspective. This review extensively summarizes traditional 3D object detection methods, focusing on camera-based, LiDAR-based, and fusion detection techniques. We provide a comprehensive analysis of the strengths and limitations of each approach, highlighting advancements in accuracy and robustness. Furthermore, we discuss future directions, including methods to improve accuracy such as temporal perception, occupancy grids, and end-to-end learning frameworks. We also explore cooperative perception methods that extend the perception range through collaborative communication. By providing a holistic view of the current state and future developments in 3D object perception, we aim to offer a more comprehensive understanding of perception tasks for autonomous driving. Additionally, we have established an active repository to provide continuous updates on the latest advancements in this field, accessible at: https://github.com/Fishsoup0/Autonomous-Driving-Perception.

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Placed in VLMs for autonomous driving, one of 9 directions in End-to-end autonomous driving. Builds on 25 of its 140 references that are in the map. First posted on arXiv 28 Aug 2024.

Builds on

25 of 140 references.

Closest ancestors · 2

Same direction, newest first

  1. 2024Embodied Understanding of Driving Scenarios7 Mar
  2. 2023HiLM-D: Enhancing MLLMs with Multi-Scale High-Resolution Details for Autonomous Driving11 Sep

Other references · 23

  1. 2017Proximal Policy Optimization Algorithmscited by 14,441
  2. 2015Continuous control with deep reinforcement learningcited by 3,279
  3. 2018YOLOv3: An Incremental Improvementcited by 2,652
  4. 2018An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modelingcited by 1,447
  5. 2016End to End Learning for Self-Driving Carscited by 1,000
  6. 2024GenAD: Generative End-to-End Autonomous Drivingcited by 225
  7. 2023Rethinking the Open-Loop Evaluation of End-to-End Autonomous Driving in nuScenescited by 191
  8. 2024GaussianFormer: Scene as Gaussians for Vision-Based 3D Semantic Occupancy Predictioncited by 155
  9. 2022Multi-modal Sensor Fusion for Auto Driving Perception: A Surveycited by 73
  10. 2024V2X-Real: a Large-Scale Dataset for Vehicle-to-Everything Cooperative Perceptioncited by 65
  11. 2023PointOcc: Cylindrical Tri-Perspective View for Point-based 3D Semantic Occupancy Predictioncited by 51
  12. 2024ViewFormer: Exploring Spatiotemporal Modeling for Multi-View 3D Occupancy Perception via View-Guided Transformerscited by 36
11 more
  1. 2024OccFusion: Multi-Sensor Fusion Framework for 3D Semantic Occupancy Predictioncited by 32
  2. 2023Can you text what is happening? Integrating pre-trained language encoders into trajectory prediction models for autonomous drivingcited by 30
  3. 2021Keyframe-Focused Visual Imitation Learningcited by 25
  4. 2023Vehicle-Infrastructure Cooperative 3D Object Detection via Feature Flow Predictioncited by 25
  5. 2023LiDAR-based 4D Occupancy Completion and Forecastingcited by 23
  6. 2024LiCROcc: Teach Radar for Accurate Semantic Occupancy Prediction using LiDAR and Cameracited by 18
  7. 2024OPEN: Object-wise Position Embedding for Multi-view 3D Object Detectioncited by 12
  8. 2024V2X-PC: Vehicle-to-everything Collaborative Perception via Point Clustercited by 9
  9. 2024Exploring the Causality of End-to-End Autonomous Drivingcited by 7
  10. 2022StreamYOLO: Real-time Object Detection for Streaming Perceptioncited by 6
  11. 2024Cross-Cluster Shifting for Efficient and Effective 3D Object Detection in Autonomous Drivingcited by 1

Built on by

Cited by year

Citing papers per year: 2024: 1; 2025: 1.

2 papers.

  1. 2024Street Gaussians without 3D Object Trackercited by 4
  2. 2025RadarNeXt: Real-Time and Reliable 3D Object Detector Based On 4D mmWave Imaging Radarcited by 1

Similar, not cited

Close in wording, no citation either way

  1. 2025All You Need for Object Detection: From Pixels, Points, and Prompts to Next-Gen Fusion and Multimodal LLMs/VLMs in Autonomous Vehicles
  2. 2025Learning-based 3D Reconstruction in Autonomous Driving: A Comprehensive Survey
  3. 2021LiDAR Cluster First and Camera Inference Later: A New Perspective Towards Autonomous Driving
  4. 2026Safety-Aligned 3D Object Detection: Single-Vehicle, Cooperative, and End-to-End Perspectives
  5. 2024OpenAD: Open-World Autonomous Driving Benchmark for 3D Object Detection
  6. 2021OPV2V: An Open Benchmark Dataset and Fusion Pipeline for Perception with Vehicle-to-Vehicle Communication
  7. 2023End-to-end Autonomous Driving: Challenges and Frontiers
  8. 2025Progressive Bird's Eye View Perception for Safety-Critical Autonomous Driving: A Comprehensive Survey