Automatic intersection management in mixed traffic using reinforcement learning and graph neural networks
M Klimke, B Völz, M Buchholz - 2023 IEEE Intelligent Vehicles …, 2023 - ieeexplore.ieee.org
Connected automated driving has the potential to significantly improve urban traffic
efficiency, eg, by alleviating issues due to occlusion. Cooperative behavior planning can be …
efficiency, eg, by alleviating issues due to occlusion. Cooperative behavior planning can be …
Social force embedded mixed graph convolutional network for multi-class trajectory prediction
Q Du, X Wang, S Yin, L Li, H Ning - IEEE Transactions on …, 2024 - ieeexplore.ieee.org
Accurate prediction of agent motion trajectories is crucial for autonomous driving,
contributing to the reduction of collision risks in human-vehicle interactions and ensuring …
contributing to the reduction of collision risks in human-vehicle interactions and ensuring …
A multi-task learning network with a collision-aware graph transformer for traffic-agents trajectory prediction
It is critical for autonomous vehicles to accurately forecast the future trajectories of
surrounding agents to avoid collisions. However, capturing the complex interactions …
surrounding agents to avoid collisions. However, capturing the complex interactions …
Hierarchical adaptable and transferable networks (hatn) for driving behavior prediction
When autonomous vehicles still struggle to solve challenging situations during on-road
driving, humans have long mastered the essence of driving with efficient transferable and …
driving, humans have long mastered the essence of driving with efficient transferable and …
Learning to play trajectory games against opponents with unknown objectives
Many autonomous agents, such as intelligent vehicles, are inherently required to interact
with one another. Game theory provides a natural mathematical tool for robot motion …
with one another. Game theory provides a natural mathematical tool for robot motion …
[HTML][HTML] Injecting knowledge in data-driven vehicle trajectory predictors
Vehicle trajectory prediction tasks have been commonly tackled from two distinct
perspectives: either with knowledge-driven methods or more recently with data-driven ones …
perspectives: either with knowledge-driven methods or more recently with data-driven ones …
Predicting vehicles trajectories in urban scenarios with transformer networks and augmented information
A Quintanar, D Fernández-Llorca… - 2021 IEEE Intelligent …, 2021 - ieeexplore.ieee.org
Understanding the behavior of road users is of vital importance for the development of
trajectory prediction systems. In this context, the latest advances have focused on recurrent …
trajectory prediction systems. In this context, the latest advances have focused on recurrent …
Multi-modal motion prediction with transformer-based neural network for autonomous driving
Predicting the behaviors of other agents on the road is critical for autonomous driving to
ensure safety and efficiency. However, the challenging part is how to represent the social …
ensure safety and efficiency. However, the challenging part is how to represent the social …
Lane-attention: Predicting vehicles' moving trajectories by learning their attention over lanes
Accurately forecasting the future movements of surrounding vehicles is essential for safe
and efficient operations of autonomous driving cars. This task is difficult because a vehicle's …
and efficient operations of autonomous driving cars. This task is difficult because a vehicle's …
Convolutional social pooling for vehicle trajectory prediction
N Deo, MM Trivedi - Proceedings of the IEEE conference on …, 2018 - openaccess.thecvf.com
Forecasting the motion of surrounding vehicles is a critical ability for an autonomous vehicle
deployed in complex traffic. Motion of all vehicles in a scene is governed by the traffic …
deployed in complex traffic. Motion of all vehicles in a scene is governed by the traffic …