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Found 17 papers, 9 papers with code
Prediction and Interpretation of Vehicle Trajectories in the Graph Spectral Domain
This work provides a comprehensive analysis and interpretation of the graph spectral representation of traffic scenarios.
Optimization and Interpretability of Graph Attention Networks for Small Sparse Graph Structures in Automotive Applications
For automotive applications, the Graph Attention Network (GAT) is a prominently used architecture to include relational information of a traffic scenario during feature embedding.
A Multidimensional Graph Fourier Transformation Neural Network for Vehicle Trajectory Prediction
This work introduces the multidimensional Graph Fourier Transformation Neural Network (GFTNN) for long-term trajectory predictions on highways.
Gradient Derivation for Learnable Parameters in Graph Attention Networks
This work provides a comprehensive derivation of the parameter gradients for GATv2 [4], a widely used implementation of Graph Attention Networks (GATs).
Expert-LaSTS: Expert-Knowledge Guided Latent Space for Traffic Scenarios
The latent space so formed is used for successful clustering and novel scenario type detection.
ExAgt: Expert-guided Augmentation for Representation Learning of Traffic Scenarios
The input data is augmented into two distorted views and an encoder learns the representations that are invariant to distortions -- cross-view prediction.
Traffic Scenario Clustering by Iterative Optimisation of Self-Supervised Networks Using a Random Forest Activation Pattern Similarity
In this work, a method is proposed to address this challenge by introducing a clustering technique based on a novel data-adaptive similarity measure, called Random Forest Activation Pattern (RFAP) similarity.
Open-set Recognition based on the Combination of Deep Learning and Ensemble Method for Detecting Unknown Traffic Scenarios
Machine learning models are useful for scenario classification but most of them assume that data received during the testing are from one of the classes used in the training.
Novelty Detection and Analysis of Traffic Scenario Infrastructures in the Latent Space of a Vision Transformer-Based Triplet Autoencoder
An autoencoder triplet network provides latent representations for infrastructure images which are used for outlier detection.
Variational Autoencoder-Based Vehicle Trajectory Prediction with an Interpretable Latent Space
This paper introduces the Descriptive Variational Autoencoder (DVAE), an unsupervised and end-to-end trainable neural network for predicting vehicle trajectories that provides partial interpretability.
An Entropy Based Outlier Score and its Application to Novelty Detection for Road Infrastructure Images
In order to generate the infrastructure images, an openDRIVE parsing and plotting tool for Matlab is developed as part of this work.
High precision indoor positioning by means of LiDAR
In this work, a method for high precision indoor positioning using a LiDAR is presented.
High Precision Indoor Navigation for Autonomous Vehicles
Autonomous driving is an important trend of the automotive industry.
Accuracy Characterization of the Vehicle State Estimation from Aerial Imagery
With these error reductions, camera-equipped UAVs are very attractive tools for traffic data acquisition.
Signal Processing
Vehicle Position Estimation with Aerial Imagery from Unmanned Aerial Vehicles
A robust object detection is crucial for reliable results, hence the state-of-the-art deep neural network Mask-RCNN is applied for that purpose.
Unsupervised and Supervised Learning with the Random Forest Algorithm for Traffic Scenario Clustering and Classification
In the third part, a Random Forest classifier is trained using the defined clusters for the operational phase.
An Unsupervised Random Forest Clustering Technique for Automatic Traffic Scenario Categorization
A modification of the Random Forest algorithm for the categorization of traffic situations is introduced in this paper.
