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Found 17 papers, 9 papers with code
Complementary Learning for Real-World Model Failure Detection
In real-world autonomous driving, deep learning models can experience performance degradation due to distributional shifts between the training data and the driving conditions encountered.
UMAD: Unsupervised Mask-Level Anomaly Detection for Autonomous Driving
Dealing with atypical traffic scenarios remains a challenging task in autonomous driving.
Hybrid Video Anomaly Detection for Anomalous Scenarios in Autonomous Driving
In autonomous driving, the most challenging scenarios are the ones that can only be detected within their temporal context.
AnoVox: A Benchmark for Multimodal Anomaly Detection in Autonomous Driving
In this work, we propose AnoVox, the largest benchmark for ANOmaly detection in autonomous driving to date.
Informed Reinforcement Learning for Situation-Aware Traffic Rule Exceptions
Reinforcement Learning is a highly active research field with promising advancements.
MUVO: A Multimodal World Model with Spatial Representations for Autonomous Driving
Learning unsupervised world models for autonomous driving has the potential to improve the reasoning capabilities of today's systems dramatically.
Conditioning Latent-Space Clusters for Real-World Anomaly Classification
Anomalies in the domain of autonomous driving are a major hindrance to the large-scale deployment of autonomous vehicles.
Exploring the Potential of World Models for Anomaly Detection in Autonomous Driving
We provide a characterization of world models and relate individual components to previous works in anomaly detection to facilitate further research in the field.
From Model-Based to Data-Driven Simulation: Challenges and Trends in Autonomous Driving
Simulation is an integral part in the process of developing autonomous vehicles and advantageous for training, validation, and verification of driving functions.
Perception Datasets for Anomaly Detection in Autonomous Driving: A Survey
Deep neural networks (DNN) which are employed in perception systems for autonomous driving require a huge amount of data to train on, as they must reliably achieve high performance in all kinds of situations.
Experiments on Anomaly Detection in Autonomous Driving by Forward-Backward Style Transfers
As a safety-critical problem, however, anomaly detection is a huge hurdle towards a large-scale deployment of autonomous vehicles in the real world.
Knowledge Augmented Machine Learning with Applications in Autonomous Driving: A Survey
The availability of representative datasets is an essential prerequisite for many successful artificial intelligence and machine learning models.
Multimodal Detection of Unknown Objects on Roads for Autonomous Driving
Tremendous progress in deep learning over the last years has led towards a future with autonomous vehicles on our roads.
Quantification of Actual Road User Behavior on the Basis of Given Traffic Rules
Driving on roads is restricted by various traffic rules, aiming to ensure safety for all traffic participants.
Ad-datasets: a meta-collection of data sets for autonomous driving
In this paper, we present ad-datasets, an online tool that provides such an overview for more than 150 data sets.
Compressing Sensor Data for Remote Assistance of Autonomous Vehicles using Deep Generative Models
Due to the massive volume of sensor data that must be sent in real-time, highly efficient data compression is elementary to prevent an overload of network infrastructure.
Description of Corner Cases in Automated Driving: Goals and Challenges
Scaling the distribution of automated vehicles requires handling various unexpected and possibly dangerous situations, termed corner cases (CC).
