In:
Advanced Optical Technologies, Frontiers Media SA, Vol. 9, No. 6 ( 2020-12-16), p. 357-364
Abstract:
In everyday traffic, pedestrians rely on informal communication with other road users. In case of automated vehicles, this communication can be replaced by light signals, which need to be learned beforehand. Prior to an extensive introduction of automated vehicles, a learning phase for these light signals can be set up in manual driving with help of a driver intention prediction. Therefore, a three-staged algorithm consisting of a neural network, a random forest and a conditional stage, is implemented. Using this algorithm, a true-positive rate (TPR) of 94.0% for a 5.0% false-positive rate (FPR) can be achieved. To improve this process, a personalization procedure is implemented, using driver-specific behaviours, resulting in TPRs ranging from 91.5 to 96.6% for a FPR of 5.0%. Transfer learning of neural networks improves the prediction accuracy of almost all drivers. In order to introduce the implemented algorithm in today’s traffic, especially the FPR has to be improved considerably.
Type of Medium:
Online Resource
ISSN:
2192-8584
,
2192-8576
DOI:
10.1515/aot-2020-0035
Language:
English
Publisher:
Frontiers Media SA
Publication Date:
2020
detail.hit.zdb_id:
2663362-0
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