Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Transferable Pedestrian Motion Prediction Models at Intersections   One desirable capability of autonomous cars is to accurately predict the
pedestrian motion near intersections for safe and efficient trajectory
planning. We are interested in developing transfer learning algorithms that can
be trained on the pedestrian trajectories collected at one intersection and yet
still provide accurate predictions of the trajectories at another, previously
unseen intersection. We first discussed the feature selection for transferable
pedestrian motion models in general. Following this discussion, we developed
one transferable pedestrian motion prediction algorithm based on Inverse
Reinforcement Learning (IRL) that infers pedestrian intentions and predicts
future trajectories based on observed trajectory. We evaluated our algorithm on
a dataset collected at two intersections, trained at one intersection and
tested at the other intersection. We used the accuracy of augmented
semi-nonnegative sparse coding (ASNSC), trained and tested at the same
intersection as a baseline. The result shows that the proposed algorithm
improves the baseline accuracy by 40% in the non-transfer task, and 16% in the
transfer task.

26
The categories of this paper are: bayes-an
14
The categories of this paper are: math-ph
33
The categories of this paper are: plasm-ph
28
The categories of this paper are: alg-geom
30
The categories of this paper are: q-alg
8
The categories of this paper are: hep-ex
34
The categories of this paper are: chem-ph
36
The categories of this paper are: cmp-lg
21
The categories of this paper are: adap-org
12
The categories of this paper are: nucl-th
11
The categories of this paper are: hep-lat
35
The categories of this paper are: mtrl-th
29
The categories of this paper are: funct-an
6
The categories of this paper are: astro-ph
0
The categories of this paper are: hep-ph
2
The categories of this paper are: math