Autonomous Driving Perception

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20 papers in the last four weeks, up 186% on the four weeks before. 0.2% of all new papers.

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Latest papers 236

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To View Transform or Not to View Transform: NeRF-based Pre-training Perspective
Mar 30, 2026Hyeonjun Jeong, Juyeb Shin, Dongsuk Kum3D Reconstruction Autonomous Driving Perception
KAIST, Daejeon, Korea
$M^2$ -Occ: Resilient 3D Semantic Occupancy Prediction for Autonomous Driving with Incomplete Camera Inputs
Mar 10, 2026Kaixin Lin, Kunyu Peng, Di Wen +3Multi-View Learning Autonomous Driving Perception
OccTrack360: 4D Panoptic Occupancy Tracking from Surround-View Fisheye Cameras
Mar 9, 2026Yongzhi Lin, Kai Luo, Yuanfan Zheng +4Autonomous Driving Perception Semantic Occupancy Mapping
School of Artificial Intelligence and Robotics and the National Engineering Research Center of Robot Visual Perception and Control Technology, Hunan University, Changsha, China · Ant Group, Hangzhou, China · State Key Laboratory of Extreme Photonics and Instrumentation, Zhejiang University, Hangzhou, China
HyperDet: 3D Object Detection with Hyper 4D Radar Point Clouds
Feb 12, 2026Yichun Xiao, Jin Jin, Runwei Guan +1Point Cloud Completion mmWave Radar Sensing
HKUST(GZ) · University of Edinburgh · University of Oxford
GaussianCaR: Gaussian Splatting for Efficient Camera-Radar Fusion
Feb 9, 2026Santiago Montiel-Marín, Miguel Antunes-García, Fabio Sánchez-García +3Camera-Radar Fusion Autonomous Driving Perception
Department of Electronics. University of Alcalá, Spain. · Department of Cognitive Robotics. Delft University of Technology, The Netherlands.
A Comparative Evaluation of Large Vision-Language Models for 2D Object Detection under SOTIF Conditions
Jan 30, 2026Ji Zhou, Yilin Ding, Yongqi Zhao +4VLM Evaluation Large Vision-Language Models
Institute of Automotive Engineering, Graz University of Technology, 8010 Graz, Austria · Independent Researcher, 200000 Shanghai, China · School of Intelligent Connected Vehicle, Hubei University of Automotive Technology, 442002 Shiyan, China +1
Zero-Shot Distracted Driver Detection via Vision Language Models with Double Decoupling
Jan 13, 2026Takamichi Miyata, Sumiko Miyata, Andrew MorrisVision-Language Models Autonomous Driving Perception
Chiba Institute of Technology · Chiba, Japan · Institute of Science Tokyo +3
Semantic Misalignment in Vision-Language Models under Perceptual Degradation
Jan 13, 2026Guo Cheng, Huang LiVLM Evaluation VLM Robustness
LiDAS: Lighting-driven Dynamic Active Sensing for Nighttime Perception
Dec 9, 2025Simon de Moreau, Andrei Bursuc, Hafid El-Idrissi +1Autonomous Driving Perception Active Sensing
Valeo - Mines Paris PSL · Valeo · Mines Paris PSL
Fast-BEV++: Fast by Algorithm, Deployable by Design
Dec 9, 2025Yuanpeng Chen, Hui Song, Sheng Yang +5Autonomous Driving Perception Bird's-Eye-View Representation
iMotion Automotive Technology (Suzhou) Co., Ltd · School of Data Science, Fudan University · Independent Researcher
UniFlow: Zero-Shot LiDAR Scene Flow for Autonomous Vehicles
Nov 23, 2025Siyi Li, Qingwen Zhang, Ishan Khatri +4Point Cloud Learning Autonomous Driving Perception
University of Pennsylvania · KTH Royal Institute of Technology · StackAV +1
RoadBench: Benchmarking MLLMs on Fine-Grained Spatial Understanding and Reasoning under Urban Road Scenarios
Nov 22, 2025Jun Zhang, Xin Zhang, Jie Feng +5VLM Evaluation Spatial Reasoning Benchmarks
Department of Electronic Engineering, BNRist, Tsinghua University, Beijing, China · Zhongguancun Academy · Amap, Alibaba Group, Beijing, China
Benchmarking Nighttime Traffic Sign Recognition with Illumination-Adaptive Detection and Semantic Attribute Reasoning
Nov 21, 2025Aditya Mishra, Akshay Agarwal, Haroon LoneIntelligent Transportation Systems Autonomous Driving Perception
IISER Bhopal
HENet++: Hybrid Encoding and Multi-task Learning for 3D Perception and End-to-end Autonomous Driving
Nov 10, 2025Zhongyu Xia, Zhiwei Lin, Yongtao Wang +1Autonomous Driving Perception End-to-End Autonomous Driving
Wangxuan Institute of Computer Technology, Peking University, Beijing, China. · University of California, Merced, USA.
DIAL-GS: Dynamic Instance Aware Reconstruction for Label-free Street Scenes with 4D Gaussian Splatting
Nov 10, 2025Chenpeng Su, Wenhua Wu, Chensheng Peng +33D Gaussian Splatting Self-Supervised Learning
Shanghai Jiao Tong University, Shanghai 200240, China. · University of California, Berkeley, Berkeley, CA 94720, USA.
MLPerf Automotive
Oct 31, 2025Radoyeh Shojaei, Predrag Djurdjevic, Mostafa El-Khamy +7End-to-End Autonomous Driving Benchmark Design
University of California, Davis · Arm · Samsung +5
AD-SAM: Adapting the Segment Anything Model for Semantic Segmentation in Autonomous Driving
Oct 30, 2025Het Patel, Mario Camarena, Fatemeh Nazari +3Vision Foundation Model Adaptation Autonomous Driving Perception
Department of Computer Science, The University of Texas Rio Grande Valley, Edinburg, TX 78539 USA · Department of Civil Engineering, The University of Texas Rio Grande Valley, Edinburg, TX 78539 USA · Department of Computer Science and Engineering, University of California Riverside, Riverside, CA 92521 USA
BikeScenes: LiDAR Semantic Segmentation for Bicycles
Oct 29, 2025Denniz Goren, Holger CaesarAutonomous Driving Perception 3D Semantic Segmentation
AURASeg: Attention-Guided Upsampling with Residual-Assisted Boundary Refinement for Drivable-Area Segmentation
Oct 24, 2025Narendhiran VijayakumarAutonomous Driving Perception Boundary-Aware Segmentation
CogAD: Cognitive-Hierarchy Guided End-to-End Autonomous Driving
May 27, 2025Zhennan Wang, Jianing Teng, Canqun Xiang +4End-to-End Autonomous Driving Autonomous Driving Perception
HAOMO.AI Technology Co., Ltd
Simulating Automotive Radar with Lidar and Camera Inputs
Mar 11, 2025Peili Song, Dezhen Song, Yifan Yang +2Synthetic Data Augmentation Millimeter-Wave Radar
Institute of Robotics and Automatic Information System, Nankai University, Tianjin 300350, China; Tianjin Key Laboratory of Intelligent Robotics, Tianjin 300350, China; and also with TBI center, Nankai University, Tianjin 300350, China · Department of Robotics, Mohamed Bin Zayed University of Artificial Intelligence (MBZUAI), Abu Dhabi, United Arab Emirates
Open-World Panoptic Segmentation
Dec 17, 2024Matteo Sodano, Federico Magistri, Jens Behley +1Image Segmentation Panoptic Segmentation
Center for Robotics, University of Bonn, Germany, and the Lamarr Institute for Machine Learning and Artificial Intelligence, Germany
ROAD-Waymo: A Large-Scale Action Awareness Dataset for Autonomous Driving
Nov 3, 2024Salman Khan, Izzeddin Teeti, Reza Javanmard Alitappeh +5Autonomous Driving Datasets Autonomous Driving Benchmarks
Oxford Brookes University · MAZUST · University of Oxford +2
EPMF: Efficient Perception-aware Multi-sensor Fusion for 3D Semantic Segmentation
Jun 21, 2021Mingkui Tan, Zhuangwei Zhuang, Sitao Chen +4Camera-LiDAR Fusion Autonomous Driving Perception
School of Software Engineering, South China University of Technology · Pazhou Laboratory, Guangzhou, China · School of Electronic and Information Engineering, South China University of Technology +2
4D-RaDiff: Latent Point Diffusion for 4D Radar Point Cloud Generation
Date pendingJimmie Kwok, Holger Caesar, Andras PalffySynthetic Data Augmentation Point Cloud Learning
Delft University of Technology, Netherlands · Perciv AI, Netherlands
HeteroPROMPT: A Real-time and Privacy-Preserving Heterogeneous Collaborative Perception Framework
Date pendingArmin Maleki, Hayder RadhaAutonomous Driving Perception Cooperative Perception
Electrical and Computer Engineering, Michigan State University

Mar 30, 2026, cs.CVJ/K move · Enter open · S save

To View Transform or Not to View Transform: NeRF-based Pre-training Perspective

Hyeonjun Jeong, Juyeb Shin, Dongsuk Kum

KAIST, Daejeon, Korea

3D Reconstruction Autonomous Driving Perception 3D Object Detection 3D Representation Learning Self-Supervised Visual Representation Learning Neural Radiance Fields

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Neural radiance fields (NeRFs) have emerged as a prominent pre-training paradigm for vision-centric autonomous driving, which enhances 3D geometry and appearance understanding in a fully self-supervised manner. To apply NeRF-based pretraining to 3D perception models, recent approaches have simply applied NeRFs to volumetric features obtained from view transformation. However, coupling NeRFs with view transformation inherits conflicting priors; view transformation imposes discrete and rigid representations, whereas radiance fields assume continuous and adaptive functions. When these opposing assumptions are forced into a single pipeline, the misalignment surfaces as blurry and ambiguous 3D representations that ultimately limit 3D scene understanding. Moreover, the NeRF network for pre-training is discarded during downstream tasks, resulting in inefficient utilization of enhanced 3D representations through NeRF. In this paper, we propose a novel NeRF-Resembled Point-based 3D detector that can learn continuous 3D representation and thus avoid the misaligned priors from view transformation. NeRP3D preserves the pre-trained NeRF network regardless of the tasks, inheriting the principle of continuous 3D representation learning and leading to greater potentials for both scene reconstruction and detection tasks. Experiments on nuScenes dataset demonstrate that our proposed approach significantly improves previous state-of-the-art methods, outperforming not only pretext scene reconstruction tasks but also downstream detection tasks.

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Latest papers 236