Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: TIP: Task-Informed Motion Prediction for Intelligent Systems   Motion prediction is important for intelligent driving systems, providing the
future distributions of road agent behaviors and supporting various decision
making tasks. Existing motion predictors are often optimized and evaluated via
task-agnostic measures based on prediction accuracy. Such measures fail to
account for the use of prediction in downstream tasks, and could result in
sub-optimal task performance. We propose a task-informed motion prediction
framework that jointly reasons about prediction accuracy and task utility, to
better support downstream tasks through its predictions. The task utility
function does not require the full task information, but rather a specification
of the utility of the task, resulting in predictors that serve a wide range of
downstream tasks. We demonstrate our framework on two use cases of task
utilities, in the context of autonomous driving and parallel autonomy, and show
the advantage of task-informed predictors over task-agnostic ones on the Waymo
Open Motion dataset.

25
The categories of this paper are: patt-sol
24
The categories of this paper are: dg-ga
27
The categories of this paper are: comp-gas
19
The categories of this paper are: eess
23
The categories of this paper are: solv-int
22
The categories of this paper are: chao-dyn
32
The categories of this paper are: atom-ph
2
The categories of this paper are: math
5
The categories of this paper are: gr-qc
0
The categories of this paper are: hep-ph
6
The categories of this paper are: astro-ph
36
The categories of this paper are: cmp-lg
13
The categories of this paper are: quant-ph
17
The categories of this paper are: q-fin
15
The categories of this paper are: nucl-ex
28
The categories of this paper are: alg-geom