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

04/19/2022
by   Christopher Hazard, et al.
3

Trajectory prediction is an important task in autonomous driving. State-of-the-art trajectory prediction models often use attention mechanisms to model the interaction between agents. In this paper, we show that the attention information from such models can also be used to measure the importance of each agent with respect to the ego vehicle's future planned trajectory. Our experiment results on the nuPlans dataset show that our method can effectively find and rank surrounding agents by their impact on the ego's plan.

READ FULL TEXT
research
08/31/2022

Class-Aware Attention for Multimodal Trajectory Prediction

Predicting the possible future trajectories of the surrounding dynamic a...
research
03/25/2020

PiP: Planning-informed Trajectory Prediction for Autonomous Driving

It is critical to predict the motion of surrounding vehicles for self-dr...
research
09/07/2023

PBP: Path-based Trajectory Prediction for Autonomous Driving

Trajectory prediction plays a crucial role in the autonomous driving sta...
research
06/27/2023

What Truly Matters in Trajectory Prediction for Autonomous Driving?

In the autonomous driving system, trajectory prediction plays a vital ro...
research
11/30/2020

Trajformer: Trajectory Prediction with Local Self-Attentive Contexts for Autonomous Driving

Effective feature-extraction is critical to models' contextual understan...
research
05/31/2023

Smooth-Trajectron++: Augmenting the Trajectron++ behaviour prediction model with smooth attention

Understanding traffic participants' behaviour is crucial for predicting ...
research
10/18/2020

Multiple Future Prediction Leveraging Synthetic Trajectories

Trajectory prediction is an important task, especially in autonomous dri...

Please sign up or login with your details

Forgot password? Click here to reset