Next Article in Journal
A Machine Learning Approach to Determine Oyster Vessel Behavior
Previous Article in Journal
Learning to Teach Reinforcement Learning Agents
Open AccessArticle

Category Maps Describe Driving Episodes Recorded with Event Data Recorders

Faculty of Systems Science and Technology, Akita Prefectural University, Yurihonjo City, Akita 015-0055, Japan
*
Author to whom correspondence should be addressed.
This paper is partially presented on the 23rd IEEE International Symposium on Robot and Human Interactive Communication, Edinburgh, Scotland, 25–29 August 2014 and 2017 IEEE International Joint Conference on Neural Networks, Anchorage, AK, USA, 14–19 May 2017.
Mach. Learn. Knowl. Extr. 2019, 1(1), 43-63; https://doi.org/10.3390/make1010003
Received: 29 January 2018 / Revised: 7 March 2018 / Accepted: 8 March 2018 / Published: 12 March 2018
(This article belongs to the Section Learning)
This study was conducted to create driving episodes using machine-learning-based algorithms that address long-term memory (LTM) and topological mapping. This paper presents a novel episodic memory model for driving safety according to traffic scenes. The model incorporates three important features: adaptive resonance theory (ART), which learns time-series features incrementally while maintaining stability and plasticity; self-organizing maps (SOMs), which represent input data as a map with topological relations using self-mapping characteristics; and counter propagation networks (CPNs), which label category maps using input features and counter signals. Category maps represent driving episode information that includes driving contexts and facial expressions. The bursting states of respective maps produce LTM created on ART as episodic memory. For a preliminary experiment using a driving simulator (DS), we measure gazes and face orientations of drivers as their internal information to create driving episodes. Moreover, we measure cognitive distraction according to effects on facial features shown in reaction to simulated near-misses. Evaluation of the experimentally obtained results show the possibility of using recorded driving episodes with image datasets obtained using an event data recorder (EDR) with two cameras. Using category maps, we visualize driving features according to driving scenes on a public road and an expressway. View Full-Text
Keywords: episodic memory; context; facial expressions; category maps; event data recorder; unsupervised learning episodic memory; context; facial expressions; category maps; event data recorder; unsupervised learning
Show Figures

Figure 1

MDPI and ACS Style

Madokoro, H.; Sato, K.; Shimoi, N. Category Maps Describe Driving Episodes Recorded with Event Data Recorders. Mach. Learn. Knowl. Extr. 2019, 1, 43-63.

Show more citation formats Show less citations formats

Article Access Map by Country/Region

1
Back to TopTop