Deep Interactive Motion Prediction and Planning: Playing Games with
Motion Prediction Models
- URL: http://arxiv.org/abs/2204.02392v1
- Date: Tue, 5 Apr 2022 17:58:18 GMT
- Title: Deep Interactive Motion Prediction and Planning: Playing Games with
Motion Prediction Models
- Authors: Jose L. Vazquez, Alexander Liniger, Wilko Schwarting, Daniela Rus, Luc
Van Gool
- Abstract summary: This work presents a game-theoretic Model Predictive Controller (MPC) that uses a novel interactive multi-agent neural network policy as part of its predictive model.
Fundamental to the success of our method is the design of a novel multi-agent policy network that can steer a vehicle given the state of the surrounding agents and the map information.
- Score: 162.21629604674388
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: In most classical Autonomous Vehicle (AV) stacks, the prediction and planning
layers are separated, limiting the planner to react to predictions that are not
informed by the planned trajectory of the AV. This work presents a module that
tightly couples these layers via a game-theoretic Model Predictive Controller
(MPC) that uses a novel interactive multi-agent neural network policy as part
of its predictive model. In our setting, the MPC planner considers all the
surrounding agents by informing the multi-agent policy with the planned state
sequence. Fundamental to the success of our method is the design of a novel
multi-agent policy network that can steer a vehicle given the state of the
surrounding agents and the map information. The policy network is trained
implicitly with ground-truth observation data using backpropagation through
time and a differentiable dynamics model to roll out the trajectory forward in
time. Finally, we show that our multi-agent policy network learns to drive
while interacting with the environment, and, when combined with the
game-theoretic MPC planner, can successfully generate interactive behaviors.
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