Category Maps Describe Driving Episodes Recorded with Event Data Recorders†
AbstractThis 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
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Madokoro, H.; Sato, K.; Shimoi, N. Category Maps Describe Driving Episodes Recorded with Event Data Recorders. Mach. Learn. Knowl. Extr. 2018, 1, 3.
Madokoro H, Sato K, Shimoi N. Category Maps Describe Driving Episodes Recorded with Event Data Recorders. Machine Learning and Knowledge Extraction. 2018; 1(1):3.Chicago/Turabian Style
Madokoro, Hirokazu; Sato, Kazuhito; Shimoi, Nobuhiro. 2018. "Category Maps Describe Driving Episodes Recorded with Event Data Recorders." Mach. Learn. Knowl. Extr. 1, no. 1: 3.
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