Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Pedestrian Behavior Prediction for Automated Driving: Requirements,
  Metrics, and Relevant Features   Automated vehicles require a comprehensive understanding of traffic
situations to ensure safe and anticipatory driving. In this context, the
prediction of pedestrians is particularly challenging as pedestrian behavior
can be influenced by multiple factors. In this paper, we thoroughly analyze the
requirements on pedestrian behavior prediction for automated driving via a
system-level approach. To this end we investigate real-world pedestrian-vehicle
interactions with human drivers. Based on human driving behavior we then derive
appropriate reaction patterns of an automated vehicle and determine
requirements for the prediction of pedestrians. This includes a novel metric
tailored to measure prediction performance from a system-level perspective. The
proposed metric is evaluated on a large-scale dataset comprising thousands of
real-world pedestrian-vehicle interactions. We furthermore conduct an ablation
study to evaluate the importance of different contextual cues and compare these
results to ones obtained using established performance metrics for pedestrian
prediction. Our results highlight the importance of a system-level approach to
pedestrian behavior prediction.

37
The categories of this paper are: supr-con
13
The categories of this paper are: quant-ph
25
The categories of this paper are: patt-sol
31
The categories of this paper are: ao-sci
21
The categories of this paper are: adap-org
20
The categories of this paper are: acc-phys
23
The categories of this paper are: solv-int
22
The categories of this paper are: chao-dyn
7
The categories of this paper are: hep-th
2
The categories of this paper are: math
28
The categories of this paper are: alg-geom
8
The categories of this paper are: hep-ex
3
The categories of this paper are: physics
12
The categories of this paper are: nucl-th
35
The categories of this paper are: mtrl-th
26
The categories of this paper are: bayes-an