Cover Image

MUVO: A Multimodal Generative World Model for Autonomous Driving with Geometric Representations

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Bogdoll, Daniel, Yang, Yitian, Joseph, Tim, Yazgan, Melih, Zöllner, J. Marius
Format:	Preprint
Published:	2023
Subjects:	Machine Learning Robotics
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Exploring the Potential of World Models for Anomaly Detection in Autonomous Driving
by: Bogdoll, Daniel, et al.
Published: (2023)

EffiComm: Bandwidth Efficient Multi Agent Communication
by: Yazgan, Melih, et al.
Published: (2025)

Beyond Scalar Rewards: Distributional Reinforcement Learning with Preordered Objectives for Safe and Reliable Autonomous Driving
by: Abouelazm, Ahmed, et al.
Published: (2026)

Anomaly Detection in Autonomous Driving: A Survey
by: Bogdoll, Daniel, et al.
Published: (2022)

Description of Corner Cases in Automated Driving: Goals and Challenges
by: Bogdoll, Daniel, et al.
Published: (2021)

Hybrid Video Anomaly Detection for Anomalous Scenarios in Autonomous Driving
by: Bogdoll, Daniel, et al.
Published: (2024)

UMAD: Unsupervised Mask-Level Anomaly Detection for Autonomous Driving
by: Bogdoll, Daniel, et al.
Published: (2024)

Informed Reinforcement Learning for Situation-Aware Traffic Rule Exceptions
by: Bogdoll, Daniel, et al.
Published: (2024)

Centralized Decision-Making for Platooning By Using SPaT-Driven Reference Speeds
by: Yazgan, Melih, et al.
Published: (2025)

Diverse and Adaptive Behavior Curriculum for Autonomous Driving: A Student-Teacher Framework with Multi-Agent RL
by: Abouelazm, Ahmed, et al.
Published: (2025)

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

On The Impact of Replacing Private Cars with Autonomous Shuttles: An Agent-Based Approach
by: Bogdoll, Daniel, et al.
Published: (2023)

AnoVox: A Benchmark for Multimodal Anomaly Detection in Autonomous Driving
by: Bogdoll, Daniel, et al.
Published: (2024)

Compressing Sensor Data for Remote Assistance of Autonomous Vehicles using Deep Generative Models
by: Bogdoll, Daniel, et al.
Published: (2021)

Planning with Adaptive World Models for Autonomous Driving
by: Vasudevan, Arun Balajee, et al.
Published: (2024)

Conditioning Latent-Space Clusters for Real-World Anomaly Classification
by: Bogdoll, Daniel, et al.
Published: (2023)

Fool the Stoplight: Realistic Adversarial Patch Attacks on Traffic Light Detectors
by: Pavlitska, Svetlana, et al.
Published: (2025)

A Review of Reward Functions for Reinforcement Learning in the context of Autonomous Driving
by: Abouelazm, Ahmed, et al.
Published: (2024)

World Models for Autonomous Driving: An Initial Survey
by: Guan, Yanchen, et al.
Published: (2024)

DreamerAD: Efficient Reinforcement Learning via Latent World Model for Autonomous Driving
by: Yang, Pengxuan, et al.
Published: (2026)

From Imitation to Exploration: End-to-end Autonomous Driving based on World Model
by: Li, Yueyuan, et al.
Published: (2024)

Label-Free Model Failure Detection for Lidar-based Point Cloud Segmentation
by: Bogdoll, Daniel, et al.
Published: (2024)

Exploring Contextual Representation and Multi-Modality for End-to-End Autonomous Driving
by: Azam, Shoaib, et al.
Published: (2022)

Learning Safe Autonomous Driving Policies Using Predictive Safety Representations
by: Keswani, Mahesh, et al.
Published: (2025)

Empowering Autonomous Shuttles with Next-Generation Infrastructure
by: Ochs, Sven, et al.
Published: (2024)

Balancing Progress and Safety: A Novel Risk-Aware Objective for RL in Autonomous Driving
by: Abouelazm, Ahmed, et al.
Published: (2025)

Predictive Planner for Autonomous Driving with Consistency Models
by: Li, Anjian, et al.
Published: (2025)

A Survey on Intermediate Fusion Methods for Collaborative Perception Categorized by Real World Challenges
by: Yazgan, Melih, et al.
Published: (2024)

Runtime Safety Monitoring of Deep Neural Networks for Perception: A Survey
by: Schotschneider, Albert, et al.
Published: (2025)

UniGen: Unified Modeling of Initial Agent States and Trajectories for Generating Autonomous Driving Scenarios
by: Mahjourian, Reza, et al.
Published: (2024)

SafeAuto: Knowledge-Enhanced Safe Autonomous Driving with Multimodal Foundation Models
by: Zhang, Jiawei, et al.
Published: (2025)

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

Chain-of-Thought for Autonomous Driving: A Comprehensive Survey and Future Prospects
by: Cui, Yixin, 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)

Automatic Curriculum Learning for Driving Scenarios: Towards Robust and Efficient Reinforcement Learning
by: Abouelazm, Ahmed, et al.
Published: (2025)

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

Diffusion Models for Robotic Manipulation: A Survey
by: Wolf, Rosa, et al.
Published: (2025)

Safety-aware Causal Representation for Trustworthy Offline Reinforcement Learning in Autonomous Driving
by: Lin, Haohong, et al.
Published: (2023)

Real-Time Generative Policy via Langevin-Guided Flow Matching for Autonomous Driving
by: Zhu, Tianze, et al.
Published: (2026)

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