Social interactions for autonomous driving: A review and perspectives
No human drives a car in a vacuum; she/he must negotiate with other road users to achieve
their goals in social traffic scenes. A rational human driver can interact with other road users …
their goals in social traffic scenes. A rational human driver can interact with other road users …
Integrating scientific knowledge with machine learning for engineering and environmental systems
There is a growing consensus that solutions to complex science and engineering problems
require novel methodologies that are able to integrate traditional physics-based modeling …
require novel methodologies that are able to integrate traditional physics-based modeling …
Social nce: Contrastive learning of socially-aware motion representations
Learning socially-aware motion representations is at the core of recent advances in multi-
agent problems, such as human motion forecasting and robot navigation in crowds. Despite …
agent problems, such as human motion forecasting and robot navigation in crowds. Despite …
Review of pedestrian trajectory prediction methods: Comparing deep learning and knowledge-based approaches
R Korbmacher, A Tordeux - IEEE Transactions on Intelligent …, 2022 - ieeexplore.ieee.org
In crowd scenarios, predicting trajectories of pedestrians is a complex and challenging task
depending on many external factors. The topology of the scene and the interactions …
depending on many external factors. The topology of the scene and the interactions …
I-24 MOTION: An instrument for freeway traffic science
Abstract The Interstate-24 MObility Technology Interstate Observation Network (I-24
MOTION) is a new instrument for traffic science located near Nashville, Tennessee. I-24 …
MOTION) is a new instrument for traffic science located near Nashville, Tennessee. I-24 …
Vehicle trajectory prediction works, but not everywhere
Vehicle trajectory prediction is nowadays a fundamental pillar of self-driving cars. Both the
industry and research communities have acknowledged the need for such a pillar by …
industry and research communities have acknowledged the need for such a pillar by …
Explainable multimodal trajectory prediction using attention models
K Zhang, L Li - Transportation Research Part C: Emerging …, 2022 - Elsevier
Automated vehicles are expected to navigate complex urban environments safely along with
several non-cooperating agents. Therefore, accurate trajectory prediction is crucial for safe …
several non-cooperating agents. Therefore, accurate trajectory prediction is crucial for safe …
[HTML][HTML] Are socially-aware trajectory prediction models really socially-aware?
Our transportation field has recently witnessed an arms race of neural network-based
trajectory predictors. While these predictors are at the core of many applications such as …
trajectory predictors. While these predictors are at the core of many applications such as …
Trajectory prediction for heterogeneous traffic-agents using knowledge correction data-driven model
There is a dilemma regarding the accuracy and reality of vehicle trajectory prediction.
Balancing and predicting the effective trajectory is a topic of debate in autonomous driving …
Balancing and predicting the effective trajectory is a topic of debate in autonomous driving …
Incorporating driving knowledge in deep learning based vehicle trajectory prediction: A survey
Z Ding, H Zhao - IEEE Transactions on Intelligent Vehicles, 2023 - ieeexplore.ieee.org
Vehicle Trajectory Prediction (VTP) is one of the key issues in the field of autonomous
driving. In recent years, more researchers have tried applying Deep Learning methods and …
driving. In recent years, more researchers have tried applying Deep Learning methods and …