Autonomous driving cars
• Predicting future trajectories of surrounding obstacles
is a crucial task for autonomous driving cars to achieve
a high degree of road safety.
Challenges in trajectory prediction in
real-world traffic scenarios:
• Considering surrounding traffic
• Dealing with social interactions.
• Handling traffic of multi-class
• Predicting multi-modal
trajectories with probability.
• Probability awareness.
• Multi-modal predictions with considering traffic environments, dealing with social interactions,
and predicting multi-class movement patterns with probability values, simultaneously.
• Dynamic Graph Attention Network (DGAN), Dynamic Attention Zone and GAT are combined
to model the intention and habit of human driving in heterogeneous traffic scenarios.
• To capture complex social interactions among road agents, the authors combine a semantic
HD map, observed trajectories of road agents, and the current status of the traffic.
Dynamic Attention Zone and Graph Modelling
• A dynamic attention zone is designed to capture
the normal ability of people when interacting with
others in traffic.
• Human choose which surrounding moving agents:
distances, headings, velocities, and sizes.
an attention circle
• Semantic Map: semantic HD map contains valuable traffic rule
• The middle-layer output estimated by the CNN is extracted
as the visual feature:
• Observed Trajectory: An LSTM is used to extract joint features
from the observed trajectories of all involved agents.
• Traffic state: S is very important for capturing extra information
to predict the future trajectories:
• The final embedding feature:
Graph Attention Network
• GAT: multiple stacked graph attention layers.
• Hierarchical classification to calculate the probabilities belonging to class c and
Multi-modal Trajectory Prediction
• Predicting multiple possible future trajectories with corresponding probability
using pre-defined anchor trajectories.
• The final loss consists of anchor classification loss and trajectory offset loss:
• represents the single-mode loss L of the 𝑖𝑡ℎ agent’s 𝑘𝑐 anchor, where:
• Hierarchical classification loss:
Logistic Delivery Dataset
• Left: Logistic delivery dataset example,
consisting of three-dimensional cloud points
with manually labeled information, front
camera image, and semantic map.
• Middle: observed in dashed yellow and
future ground truth trajectories in red.
• Right: Prediction results using our proposed
DGAN method showing up the two most
likely future trajectories, and corresponding
probabilities encoded in a color map to the
handling complex situations at traffic
the predicted trajectory with the maximum
probability value is more likely to follow
center lines of lanes guiding by the semantic
ETH and UCY Datasets
• Datasets for pedestrian trajectory prediction only, include 5 scenes in total, including ETH, HOTEL,
ZARA1, ZARA2, and UNIV.
• A dynamic social interaction-aware model that predicts the future trajectories of agents in
real-world settings to solve several challenges.
• An encoded semantic map, the observed history trajectories, and the current status of
agents as the input of the GAT.
• To generate the graph at the current time step, we use the dynamic attention zone to
simulate the intuitive ability of people to navigate roads in real-world traffic.
• The experiment results demonstrate the potential ability of the proposed method for
trajectory prediction in a real-world setting and achieves good performance.