Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: 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.

24
The categories of this paper are: dg-ga
27
The categories of this paper are: comp-gas
23
The categories of this paper are: solv-int
7
The categories of this paper are: hep-th
11
The categories of this paper are: hep-lat
37
The categories of this paper are: supr-con
31
The categories of this paper are: ao-sci
16
The categories of this paper are: stat
30
The categories of this paper are: q-alg
26
The categories of this paper are: bayes-an
35
The categories of this paper are: mtrl-th
0
The categories of this paper are: hep-ph
14
The categories of this paper are: math-ph
9
The categories of this paper are: nlin
18
The categories of this paper are: econ
22
The categories of this paper are: chao-dyn