Pedestrian trajectory prediction in pedestrian-vehicle mixed environments: A systematic review
M Golchoubian, M Ghafurian… - IEEE Transactions …, 2023 - ieeexplore.ieee.org
Planning an autonomous vehicle's (AV) path in a space shared with pedestrians requires
reasoning about pedestrians' future trajectories. A practical pedestrian trajectory prediction …
reasoning about pedestrians' future trajectories. A practical pedestrian trajectory prediction …
A survey on graph neural networks in intelligent transportation systems
Intelligent Transportation System (ITS) is vital in improving traffic congestion, reducing traffic
accidents, optimizing urban planning, etc. However, due to the complexity of the traffic …
accidents, optimizing urban planning, etc. However, due to the complexity of the traffic …
Graph neural networks for intelligent transportation systems: A survey
Graph neural networks (GNNs) have been extensively used in a wide variety of domains in
recent years. Owing to their power in analyzing graph-structured data, they have become …
recent years. Owing to their power in analyzing graph-structured data, they have become …
Hierarchical planning through goal-conditioned offline reinforcement learning
Offline Reinforcement learning (RL) has shown potent in many safe-critical tasks in robotics
where exploration is risky and expensive. However, it still struggles to acquire skills in …
where exploration is risky and expensive. However, it still struggles to acquire skills in …
Multi-person extreme motion prediction
Human motion prediction aims to forecast future poses given a sequence of past 3D
skeletons. While this problem has recently received increasing attention, it has mostly been …
skeletons. While this problem has recently received increasing attention, it has mostly been …
Vehicle trajectory prediction in connected environments via heterogeneous context-aware graph convolutional networks
The accurate trajectory prediction of surrounding vehicles is crucial for the sustainability and
safety of connected and autonomous vehicles under mixed traffic streams in the real world …
safety of connected and autonomous vehicles under mixed traffic streams in the real world …
Rain: Reinforced hybrid attention inference network for motion forecasting
Motion forecasting plays a significant role in various domains (eg, autonomous driving,
human-robot interaction), which aims to predict future motion sequences given a set of …
human-robot interaction), which aims to predict future motion sequences given a set of …
MTP-GO: Graph-based probabilistic multi-agent trajectory prediction with neural ODEs
Enabling resilient autonomous motion planning requires robust predictions of surrounding
road users' future behavior. In response to this need and the associated challenges, we …
road users' future behavior. In response to this need and the associated challenges, we …
Interaction modeling with multiplex attention
Modeling multi-agent systems requires understanding how agents interact. Such systems
are often difficult to model because they can involve a variety of types of interactions that …
are often difficult to model because they can involve a variety of types of interactions that …
Multimodal vehicular trajectory prediction with inverse reinforcement learning and risk aversion at urban unsignalized intersections
Understanding human drivers' intentions and predicting their future motions are significant to
connected and autonomous vehicles and traffic safety and surveillance systems. Predicting …
connected and autonomous vehicles and traffic safety and surveillance systems. Predicting …