DeepAI AI Chat
Log In Sign Up

Traffic Agent Trajectory Prediction Using Social Convolution and Attention Mechanism

by   Tao Yang, et al.
Xi'an Jiaotong University

The trajectory prediction is significant for the decision-making of autonomous driving vehicles. In this paper, we propose a model to predict the trajectories of target agents around an autonomous vehicle. The main idea of our method is considering the history trajectories of the target agent and the influence of surrounding agents on the target agent. To this end, we encode the target agent history trajectories as an attention mask and construct a social map to encode the interactive relationship between the target agent and its surrounding agents. Given a trajectory sequence, the LSTM networks are firstly utilized to extract the features for all agents, based on which the attention mask and social map are formed. Then, the attention mask and social map are fused to get the fusion feature map, which is processed by the social convolution to obtain a fusion feature representation. Finally, this fusion feature is taken as the input of a variable-length LSTM to predict the trajectory of the target agent. We note that the variable-length LSTM enables our model to handle the case that the number of agents in the sensing scope is highly dynamic in traffic scenes. To verify the effectiveness of our method, we widely compare with several methods on a public dataset, achieving a 20 decrease. In addition, the model satisfies the real-time requirement with the 32 fps.


page 1

page 3

page 5


Class-Aware Attention for Multimodal Trajectory Prediction

Predicting the possible future trajectories of the surrounding dynamic a...

Importance is in your attention: agent importance prediction for autonomous driving

Trajectory prediction is an important task in autonomous driving. State-...

Trajectory Prediction for Autonomous Driving based on Multi-Head Attention with Joint Agent-Map Representation

Predicting the trajectories of surrounding agents is an essential abilit...

BGM: Building a Dynamic Guidance Map without Visual Images for Trajectory Prediction

Visual images usually contain the informative context of the environment...

Robust Trajectory Forecasting for Multiple Intelligent Agents in Dynamic Scene

Trajectory forecasting, or trajectory prediction, of multiple interactin...

Heterogeneous Edge-Enhanced Graph Attention Network For Multi-Agent Trajectory Prediction

Simultaneous trajectory prediction for multiple heterogeneous traffic pa...

Social Attention for Autonomous Decision-Making in Dense Traffic

We study the design of learning architectures for behavioural planning i...