1,163 research outputs found
PiP: Planning-informed Trajectory Prediction for Autonomous Driving
It is critical to predict the motion of surrounding vehicles for self-driving
planning, especially in a socially compliant and flexible way. However, future
prediction is challenging due to the interaction and uncertainty in driving
behaviors. We propose planning-informed trajectory prediction (PiP) to tackle
the prediction problem in the multi-agent setting. Our approach is
differentiated from the traditional manner of prediction, which is only based
on historical information and decoupled with planning. By informing the
prediction process with the planning of ego vehicle, our method achieves the
state-of-the-art performance of multi-agent forecasting on highway datasets.
Moreover, our approach enables a novel pipeline which couples the prediction
and planning, by conditioning PiP on multiple candidate trajectories of the ego
vehicle, which is highly beneficial for autonomous driving in interactive
scenarios.Comment: European Conference on Computer Vision (ECCV) 2020; Project page at
http://haoran-song.github.io/planning-informed-predictio
Decision-Making for Automated Vehicles Using a Hierarchical Behavior-Based Arbitration Scheme
Behavior planning and decision-making are some of the biggest challenges for
highly automated systems. A fully automated vehicle (AV) is confronted with
numerous tactical and strategical choices. Most state-of-the-art AV platforms
implement tactical and strategical behavior generation using finite state
machines. However, these usually result in poor explainability, maintainability
and scalability. Research in robotics has raised many architectures to mitigate
these problems, most interestingly behavior-based systems and hybrid
derivatives. Inspired by these approaches, we propose a hierarchical
behavior-based architecture for tactical and strategical behavior generation in
automated driving. It is a generalizing and scalable decision-making framework,
utilizing modular behavior blocks to compose more complex behaviors in a
bottom-up approach. The system is capable of combining a variety of scenario-
and methodology-specific solutions, like POMDPs, RRT* or learning-based
behavior, into one understandable and traceable architecture. We extend the
hierarchical behavior-based arbitration concept to address scenarios where
multiple behavior options are applicable but have no clear priority against
each other. Then, we formulate the behavior generation stack for automated
driving in urban and highway environments, incorporating parking and emergency
behaviors as well. Finally, we illustrate our design in an explanatory
evaluation
Game theoretic decision making for autonomous vehicles’ merge manoeuvre in high traffic scenarios
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