Interactive Joint Planning for Autonomous Vehicles
- URL: http://arxiv.org/abs/2310.18301v4
- Date: Wed, 22 Nov 2023 22:25:54 GMT
- Title: Interactive Joint Planning for Autonomous Vehicles
- Authors: Yuxiao Chen, Sushant Veer, Peter Karkus, and Marco Pavone
- Abstract summary: In interactive driving scenarios, the actions of one agent greatly influences those of its neighbors.
We present Interactive Joint Planning (IJP) that bridges MPC with learned prediction models.
IJP significantly outperforms the baselines that are either without joint optimization or running sampling-based planning.
- Score: 19.479300967537675
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: In highly interactive driving scenarios, the actions of one agent greatly
influences those of its neighbors. Planning safe motions for autonomous
vehicles in such interactive environments, therefore, requires reasoning about
the impact of the ego's intended motion plan on nearby agents' behavior.
Deep-learning-based models have recently achieved great success in trajectory
prediction and many models in the literature allow for ego-conditioned
prediction. However, leveraging ego-conditioned prediction remains challenging
in downstream planning due to the complex nature of neural networks, limiting
the planner structure to simple ones, e.g., sampling-based planner. Despite
their ability to generate fine-grained high-quality motion plans, it is
difficult for gradient-based planning algorithms, such as model predictive
control (MPC), to leverage ego-conditioned prediction due to their iterative
nature and need for gradient. We present Interactive Joint Planning (IJP) that
bridges MPC with learned prediction models in a computationally scalable manner
to provide us the best of both the worlds. In particular, IJP jointly optimizes
over the behavior of the ego and the surrounding agents and leverages
deep-learned prediction models as prediction priors that the join trajectory
optimization tries to stay close to. Furthermore, by leveraging homotopy
classes, our joint optimizer searches over diverse motion plans to avoid
getting stuck at local minima. Closed-loop simulation result shows that IJP
significantly outperforms the baselines that are either without joint
optimization or running sampling-based planning.
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