Improving multi-agent trajectory prediction using traffic states on interactive driving scenarios
Predicting trajectories of multiple agents in interactive driving scenarios such as
intersections, and roundabouts are challenging due to the high density of agents, varying …
intersections, and roundabouts are challenging due to the high density of agents, varying …
Trafficpredict: Trajectory prediction for heterogeneous traffic-agents
To safely and efficiently navigate in complex urban traffic, autonomous vehicles must make
responsible predictions in relation to surrounding traffic-agents (vehicles, bicycles …
responsible predictions in relation to surrounding traffic-agents (vehicles, bicycles …
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 …
Multi-agent trajectory prediction with heterogeneous edge-enhanced graph attention network
Simultaneous trajectory prediction for multiple heterogeneous traffic participants is essential
for safe and efficient operation of connected automated vehicles under complex driving …
for safe and efficient operation of connected automated vehicles under complex driving …
Amenet: Attentive maps encoder network for trajectory prediction
Trajectory prediction is critical for applications of planning safe future movements and
remains challenging even for the next few seconds in urban mixed traffic. How an agent …
remains challenging even for the next few seconds in urban mixed traffic. How an agent …
Trafficbots: Towards world models for autonomous driving simulation and motion prediction
Data-driven simulation has become a favorable way to train and test autonomous driving
algorithms. The idea of replacing the actual environment with a learned simulator has also …
algorithms. The idea of replacing the actual environment with a learned simulator has also …
Predictionnet: Real-time joint probabilistic traffic prediction for planning, control, and simulation
Predicting the future motion of traffic agents is crucial for safe and efficient autonomous
driving. To this end, we present PredictionNet, a deep neural network (DNN) that predicts …
driving. To this end, we present PredictionNet, a deep neural network (DNN) that predicts …
Trajectory prediction for autonomous driving based on multi-head attention with joint agent-map representation
Predicting the trajectories of surrounding agents is an essential ability for autonomous
vehicles navigating through complex traffic scenes. The future trajectories of agents can be …
vehicles navigating through complex traffic scenes. The future trajectories of agents can be …
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 …
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 …