Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Real-Time Predictive Modeling and Robust Avoidance of Pedestrians with
  Uncertain, Changing Intentions   To plan safe trajectories in urban environments, autonomous vehicles must be
able to quickly assess the future intentions of dynamic agents. Pedestrians are
particularly challenging to model, as their motion patterns are often uncertain
and/or unknown a priori. This paper presents a novel changepoint detection and
clustering algorithm that, when coupled with offline unsupervised learning of a
Gaussian process mixture model (DPGP), enables quick detection of changes in
intent and online learning of motion patterns not seen in prior training data.
The resulting long-term movement predictions demonstrate improved accuracy
relative to offline learning alone, in terms of both intent and trajectory
prediction. By embedding these predictions within a chance-constrained motion
planner, trajectories which are probabilistically safe to pedestrian motions
can be identified in real-time. Hardware experiments demonstrate that this
approach can accurately predict pedestrian motion patterns from onboard
sensor/perception data and facilitate robust navigation within a dynamic
environment.

7
The categories of this paper are: hep-th
34
The categories of this paper are: chem-ph
37
The categories of this paper are: supr-con
25
The categories of this paper are: patt-sol
15
The categories of this paper are: nucl-ex
13
The categories of this paper are: quant-ph
27
The categories of this paper are: comp-gas
31
The categories of this paper are: ao-sci
10
The categories of this paper are: q-bio
28
The categories of this paper are: alg-geom
21
The categories of this paper are: adap-org
11
The categories of this paper are: hep-lat
5
The categories of this paper are: gr-qc
9
The categories of this paper are: nlin
2
The categories of this paper are: math
29
The categories of this paper are: funct-an