Cover Image

Exploring the Potential of World Models for Anomaly Detection in Autonomous Driving

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Bogdoll, Daniel, Bosch, Lukas, Joseph, Tim, Gremmelmaier, Helen, Yang, Yitian, Zöllner, J. Marius
Format:	Preprint
Published:	2023
Subjects:	Artificial Intelligence Robotics
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

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

MUVO: A Multimodal Generative World Model for Autonomous Driving with Geometric Representations
by: Bogdoll, Daniel, et al.
Published: (2023)

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

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

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)

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

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

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

Boundary-Guided Trajectory Prediction for Road Aware and Physically Feasible Autonomous Driving
by: Abouelazm, Ahmed, et al.
Published: (2025)

AnoVox: A Benchmark for Multimodal Anomaly Detection in Autonomous Driving
by: Bogdoll, Daniel, 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)

Vehicle Dynamics Embedded World Models for Autonomous Driving
by: Li, Huiqian, et al.
Published: (2025)

TPK: Trustworthy Trajectory Prediction Integrating Prior Knowledge For Interpretability and Kinematic Feasibility
by: Baden, Marius, et al.
Published: (2025)

Learning Multiple Probabilistic Decisions from Latent World Model in Autonomous Driving
by: Xiao, Lingyu, et al.
Published: (2024)

Exploring Adversarial Robustness of LiDAR-Camera Fusion Model in Autonomous Driving
by: Yang, Bo, et al.
Published: (2023)

Ego-centric Learning of Communicative World Models for Autonomous Driving
by: Wang, Hang, et al.
Published: (2025)

AdaWM: Adaptive World Model based Planning for Autonomous Driving
by: Wang, Hang, et al.
Published: (2025)

Kinematics-Aware Latent World Models for Data-Efficient Autonomous Driving
by: Li, Jiazhuo, et al.
Published: (2026)

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

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

Exploring the Interplay Between Video Generation and World Models in Autonomous Driving: A Survey
by: Fu, Ao, et al.
Published: (2024)

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

CarDreamer: Open-Source Learning Platform for World Model based Autonomous Driving
by: Gao, Dechen, et al.
Published: (2024)

Unleashing the Potential of Diffusion Models for End-to-End Autonomous Driving
by: Zheng, Yinan, et al.
Published: (2026)

World Models for Anomaly Detection during Model-Based Reinforcement Learning Inference
by: Domberg, Fabian, et al.
Published: (2025)

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

Imitation Learning for Autonomous Driving: Insights from Real-World Testing
by: Dursun, Hidayet Ersin, et al.
Published: (2025)

Diffusion Models for Safety Validation of Autonomous Driving Systems
by: Wang, Juanran, et al.
Published: (2025)

VLM-SAFE: Vision-Language Model-Guided Safety-Aware Reinforcement Learning with World Models for Autonomous Driving
by: Qu, Yansong, et al.
Published: (2025)

Conditional Flow Matching for Continuous Anomaly Detection in Autonomous Driving on a Manifold-Aware Spectral Space
by: Guillen-Perez, Antonio
Published: (2026)

DrivingGen: A Comprehensive Benchmark for Generative Video World Models in Autonomous Driving
by: Zhou, Yang, et al.
Published: (2026)

Integrating End-to-End and Modular Driving Approaches for Online Corner Case Detection in Autonomous Driving
by: Kaljavesi, Gemb, et al.
Published: (2024)

Enhancing Autonomous Driving Safety through World Model-Based Predictive Navigation and Adaptive Learning Algorithms for 5G Wireless Applications
by: Ding, Hong, et al.
Published: (2024)

SynAD: Enhancing Real-World End-to-End Autonomous Driving Models through Synthetic Data Integration
by: Kim, Jongsuk, et al.
Published: (2025)

Tokenize the World into Object-level Knowledge to Address Long-tail Events in Autonomous Driving
by: Tian, Ran, et al.
Published: (2024)

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

Correcting Autonomous Driving Object Detection Misclassifications with Automated Commonsense Reasoning
by: Kimbrell, Keegan, et al.
Published: (2026)

LSRE: Latent Semantic Rule Encoding for Real-Time Semantic Risk Detection in Autonomous Driving
by: Cheng, Qian, et al.
Published: (2025)

DriveMind: A Dual Visual Language Model-based Reinforcement Learning Framework for Autonomous Driving
by: Wasif, Dawood, et al.
Published: (2025)