Scenario understanding and motion prediction for autonomous vehicles—review and comparison

P Karle, M Geisslinger, J Betz… - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
Scenario understanding and motion prediction are essential components for completely
replacing human drivers and for enabling highly and fully automated driving (SAE-Level …

Spatio-temporal trajectory similarity learning in road networks

Z Fang, Y Du, X Zhu, D Hu, L Chen, Y Gao… - Proceedings of the 28th …, 2022 - dl.acm.org
Deep learning based trajectory similarity computation holds the potential for improved
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 …

Scenario-transferable semantic graph reasoning for interaction-aware probabilistic prediction

Y Hu, W Zhan, M Tomizuka - IEEE Transactions on Intelligent …, 2022 - ieeexplore.ieee.org
Accurately predicting the possible behaviors of traffic participants is an essential capability
for autonomous vehicles. Since autonomous vehicles need to navigate in dynamically …

Integrated graphical representation of highway scenarios to improve trajectory prediction of surrounding vehicles

L Hou, SE Li, B Yang, Z Wang… - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
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 …

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 …

CARLA Real Traffic Scenarios--novel training ground and benchmark for autonomous driving

B Osiński, P Miłoś, A Jakubowski, P Zięcina… - arXiv preprint arXiv …, 2020 - arxiv.org
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 …

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 …

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 …

Trajectory prediction dimensionality reduction for low-cost connected automated vehicle systems

H Yao, Q Li, X Li - Transportation Research Part D: Transport and …, 2022 - Elsevier
To facilitate low-cost connected automated vehicle (CAV) system development, this study
proposes two interpretable dimensionality reduction techniques in vehicle trajectory …