Cover Image

Panoptic Perception for Autonomous Driving: A Survey

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Li, Yunge, Xu, Lanyu
Format:	Preprint
Published:	2024
Subjects:	Robotics Computer Vision and Pattern Recognition Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

A Survey for Foundation Models in Autonomous Driving
by: Gao, Haoxiang, et al.
Published: (2024)

ZOPP: A Framework of Zero-shot Offboard Panoptic Perception for Autonomous Driving
by: Ma, Tao, et al.
Published: (2024)

Dynamic Adversarial Attacks on Autonomous Driving Systems
by: Chahe, Amirhosein, et al.
Published: (2023)

Neighbor-Aware Token Reduction via Hilbert Curve for Vision Transformers
by: Li, Yunge, et al.
Published: (2025)

VisionPAD: A Vision-Centric Pre-training Paradigm for Autonomous Driving
by: Zhang, Haiming, et al.
Published: (2024)

RMT-PPAD: Real-time Multi-task Learning for Panoptic Perception in Autonomous Driving
by: Wang, Jiayuan, et al.
Published: (2025)

STELLAR: Scaling 3D Perception Large Models for Autonomous Driving
by: Li, Yingwei, et al.
Published: (2026)

DriveTransformer: Unified Transformer for Scalable End-to-End Autonomous Driving
by: Jia, Xiaosong, et al.
Published: (2025)

Hybrid-Prediction Integrated Planning for Autonomous Driving
by: Liu, Haochen, et al.
Published: (2024)

Collaborative Perception Datasets in Autonomous Driving: A Survey
by: Yazgan, Melih, et al.
Published: (2024)

Joint Perception and Prediction for Autonomous Driving: A Survey
by: Dal'Col, Lucas, et al.
Published: (2024)

ProIn: Learning to Predict Trajectory Based on Progressive Interactions for Autonomous Driving
by: Dong, Yinke, et al.
Published: (2024)

Multi-Modal Data-Efficient 3D Scene Understanding for Autonomous Driving
by: Kong, Lingdong, et al.
Published: (2024)

Data Scaling Laws for End-to-End Autonomous Driving
by: Naumann, Alexander, et al.
Published: (2025)

LargeAD: Large-Scale Cross-Sensor Data Pretraining for Autonomous Driving
by: Kong, Lingdong, et al.
Published: (2025)

AutoTrust: Benchmarking Trustworthiness in Large Vision Language Models for Autonomous Driving
by: Xing, Shuo, et al.
Published: (2024)

TransDiffuser: Diverse Trajectory Generation with Decorrelated Multi-modal Representation for End-to-end Autonomous Driving
by: Jiang, Xuefeng, et al.
Published: (2025)

CARIL: Confidence-Aware Regression in Imitation Learning for Autonomous Driving
by: Delavari, Elahe, et al.
Published: (2025)

Multi-Objective Reinforcement Learning for Adaptable Personalized Autonomous Driving
by: Surmann, Hendrik, et al.
Published: (2025)

Advancing Autonomous Driving: DepthSense with Radar and Spatial Attention
by: Hussain, Muhamamd Ishfaq, et al.
Published: (2021)

Hilbert-Guided Sparse Local Attention
by: Li, Yunge, et al.
Published: (2025)

Foundation Models for Autonomous Driving Perception: A Survey Through Core Capabilities
by: Sathyam, Rajendramayavan, et al.
Published: (2025)

EMPERROR: A Flexible Generative Perception Error Model for Probing Self-Driving Planners
by: Hanselmann, Niklas, et al.
Published: (2024)

ACT-Bench: Towards Action Controllable World Models for Autonomous Driving
by: Arai, Hidehisa, et al.
Published: (2024)

DINO Pre-training for Vision-based End-to-end Autonomous Driving
by: Juneja, Shubham, et al.
Published: (2024)

Language-Enhanced Latent Representations for Out-of-Distribution Detection in Autonomous Driving
by: Mao, Zhenjiang, et al.
Published: (2024)

Pseudo-Simulation for Autonomous Driving
by: Cao, Wei, et al.
Published: (2025)

See Less, Drive Better: Generalizable End-to-End Autonomous Driving via Foundation Models Stochastic Patch Selection
by: Mallak, Amir, et al.
Published: (2026)

Depth Matters: Multimodal RGB-D Perception for Robust Autonomous Agents
by: Clement, Mihaela-Larisa, et al.
Published: (2025)

A Survey on Deep Multi-Task Learning in Connected Autonomous Vehicles
by: Wang, Jiayuan, et al.
Published: (2025)

Transformer-Based Autonomous Driving Models and Deployment-Oriented Compression: A Survey
by: Zhong, Juan, et al.
Published: (2023)

NeurAll: Towards a Unified Visual Perception Model for Automated Driving
by: Sistu, Ganesh, et al.
Published: (2019)

Machine Learning-Based Vehicle Intention Trajectory Recognition and Prediction for Autonomous Driving
by: Yu, Hanyi, et al.
Published: (2024)

OpenEMMA: Open-Source Multimodal Model for End-to-End Autonomous Driving
by: Xing, Shuo, et al.
Published: (2024)

NavigScene: Bridging Local Perception and Global Navigation for Beyond-Visual-Range Autonomous Driving
by: Peng, Qucheng, et al.
Published: (2025)

Bench2Drive-R: Turning Real World Data into Reactive Closed-Loop Autonomous Driving Benchmark by Generative Model
by: You, Junqi, et al.
Published: (2024)

From Static to Dynamic: a Survey of Topology-Aware Perception in Autonomous Driving
by: Chen, Yixiao, et al.
Published: (2025)

End-to-end Autonomous Driving: Challenges and Frontiers
by: Chen, Li, et al.
Published: (2023)

CRITERIA: a New Benchmarking Paradigm for Evaluating Trajectory Prediction Models for Autonomous Driving
by: Chen, Changhe, et al.
Published: (2023)

STT: Stateful Tracking with Transformers for Autonomous Driving
by: Jing, Longlong, et al.
Published: (2024)