Scenario understanding and motion prediction for autonomous vehicles—review and comparison
Scenario understanding and motion prediction are essential components for completely
replacing human drivers and for enabling highly and fully automated driving (SAE-Level …
replacing human drivers and for enabling highly and fully automated driving (SAE-Level …
Spatio-temporal trajectory similarity learning in road networks
Deep learning based trajectory similarity computation holds the potential for improved
efficiency and adaptability over traditional similarity computation. However, existing learning …
efficiency and adaptability over traditional similarity computation. However, existing learning …
openDD: A large-scale roundabout drone dataset
A Breuer, JA Termöhlen, S Homoceanu… - 2020 IEEE 23rd …, 2020 - ieeexplore.ieee.org
Analyzing and predicting the traffic scene around the ego vehicle has been one of the key
challenges in autonomous driving. Datasets including the trajectories of all road users …
challenges in autonomous driving. Datasets including the trajectories of all road users …
Scenario-transferable semantic graph reasoning for interaction-aware probabilistic prediction
Accurately predicting the possible behaviors of traffic participants is an essential capability
for autonomous vehicles. Since autonomous vehicles need to navigate in dynamically …
for autonomous vehicles. Since autonomous vehicles need to navigate in dynamically …
Integrated graphical representation of highway scenarios to improve trajectory prediction of surrounding vehicles
Varying numbers and types of vehicles, various road structures and traffic rules bring
difficulties to an autonomous vehicle driving in highway traffic scenarios. It is important to …
difficulties to an autonomous vehicle driving in highway traffic scenarios. It is important to …
Predicting future position from natural walking and eye movements with machine learning
G Bremer, N Stein, M Lappe - 2021 IEEE international …, 2021 - ieeexplore.ieee.org
The prediction of human locomotion behavior is a complex task based on data from the
given environment and the user. In this study, we trained multiple machine learning models …
given environment and the user. In this study, we trained multiple machine learning models …
CARLA Real Traffic Scenarios--novel training ground and benchmark for autonomous driving
This work introduces interactive traffic scenarios in the CARLA simulator, which are based
on real-world traffic. We concentrate on tactical tasks lasting several seconds, which are …
on real-world traffic. We concentrate on tactical tasks lasting several seconds, which are …
Reducing Error Rate for Eye-Tracking System by Applying SVM
N Ishtiaque Ahmed, F Nasrin - Machine Intelligence and Data Science …, 2022 - Springer
Electrooculography (EOG) is widely considered the most effective signal-processing
technique for identifying distinct eye movements. The EOG signal was used to extract …
technique for identifying distinct eye movements. The EOG signal was used to extract …
Multimodal Data Trajectory Prediction: A Review
X Wang, H Yue, Q Yang - 2023 IEEE 10th International …, 2023 - ieeexplore.ieee.org
Trajectory prediction refers to predicting the future movement of an object, person, or vehicle
based on past motion trajectory information and other relevant environmental information …
based on past motion trajectory information and other relevant environmental information …
Trajectory prediction dimensionality reduction for low-cost connected automated vehicle systems
To facilitate low-cost connected automated vehicle (CAV) system development, this study
proposes two interpretable dimensionality reduction techniques in vehicle trajectory …
proposes two interpretable dimensionality reduction techniques in vehicle trajectory …