Interaction-aware trajectory prediction of connected vehicles using CNN-LSTM networks
Predicting the future trajectory of a surrounding vehicle in congested traffic is one of the
necessary abilities of an autonomous vehicle. In congestion, a vehicle's future movement is …
necessary abilities of an autonomous vehicle. In congestion, a vehicle's future movement is …
Scale-net: Scalable vehicle trajectory prediction network under random number of interacting vehicles via edge-enhanced graph convolutional neural network
Predicting the future trajectory of surrounding vehicles in a randomly varying traffic level is
one of the most challenging problems in developing an autonomous vehicle. Since there is …
one of the most challenging problems in developing an autonomous vehicle. Since there is …
AI-TP: Attention-based interaction-aware trajectory prediction for autonomous driving
K Zhang, L Zhao, C Dong, L Wu… - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
Despite the advancements in the technologies of autonomous driving, it is still challenging to
study the safety of a self-driving vehicle. Trajectory prediction is one core function of an …
study the safety of a self-driving vehicle. Trajectory prediction is one core function of an …
Gisnet: Graph-based information sharing network for vehicle trajectory prediction
The trajectory prediction is a critical and challenging problem in the design of an
autonomous driving system. Many AI-oriented companies, such as Google Waymo, Uber …
autonomous driving system. Many AI-oriented companies, such as Google Waymo, Uber …
Grip: Graph-based interaction-aware trajectory prediction
Nowadays, autonomous driving cars have become commercially available. However, the
safety of a self-driving car is still a challenging problem that has not been well studied …
safety of a self-driving car is still a challenging problem that has not been well studied …
Interactive trajectory prediction of surrounding road users for autonomous driving using structural-LSTM network
L Hou, L Xin, SE Li, B Cheng… - IEEE Transactions on …, 2019 - ieeexplore.ieee.org
Accurate trajectory prediction of surrounding road users is critical to autonomous driving
systems. In mixed traffic flows, road users with different kinds of behaviors and styles bring …
systems. In mixed traffic flows, road users with different kinds of behaviors and styles bring …
[HTML][HTML] A car-following model based on trajectory data for connected and automated vehicles to predict trajectory of human-driven vehicles
D Qu, S Wang, H Liu, Y Meng - Sustainability, 2022 - mdpi.com
Connected and Automated Vehicles (CAV) have been rapidly developed, which, inevitably,
renders that human-driven and autonomous vehicles share the road. Thus, trajectory …
renders that human-driven and autonomous vehicles share the road. Thus, trajectory …
Recog: A deep learning framework with heterogeneous graph for interaction-aware trajectory prediction
Predicting the future trajectory of surrounding vehicles is essential for the navigation of
autonomous vehicles in complex real-world driving scenarios. It is challenging as a vehicle's …
autonomous vehicles in complex real-world driving scenarios. It is challenging as a vehicle's …
Grip++: Enhanced graph-based interaction-aware trajectory prediction for autonomous driving
Despite the advancement in the technology of autonomous driving cars, the safety of a self-
driving car is still a challenging problem that has not been well studied. Motion prediction is …
driving car is still a challenging problem that has not been well studied. Motion prediction is …
Trajectory prediction for intelligent vehicles using spatial‐attention mechanism
It is of great interest for autonomous vehicles to predict the trajectory of other vehicles when
planning a safe trajectory. To accurately predict the trajectory of the target vehicle, the …
planning a safe trajectory. To accurately predict the trajectory of the target vehicle, the …