Category Maps Describe Driving Episodes Recorded with Event Data Recorders

Hirokazu Madokoro,Kazuhito Sato,Nobuhiro Shimoi

doi:10.3390/make1010003

Hirokazu Madokoro, Kazuhito Sato + Show 1 more

Open Access

https://doi.org/10.3390/make1010003

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Abstract

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.

Highlights

Drivers adjust their focus and their behavior according to traffic conditions to maintain safety
This paper presents a novel episodic memory model for driving safety according to traffic scenes using machine-learning-based algorithms of three types: adaptive resonance theory (ART) networks [16], which learn time-series features incrementally with the maintenance of stability and plasticity for time-series data, self-organizing maps (SOMs) [17], which represent input data as a map with topological relations using self-mapping characteristics, and counter propagation networks (CPNs) [18], which label category maps using input features and counter signals
Face orientations were varied because the driver moved his neck to check the non-visible intersection and the bicycle that ran from the right to the left in Case I

Summary

Introduction

Drivers adjust their focus and their behavior according to traffic conditions to maintain safety. Drivers carefully devote attention to pedestrians or bicycles when they drive near a school or a park. Drivers devote attention to surrounding cars running at high speed. Drivers will take extra care to avoid sleepiness when driving scenes do not often change. Prediction models for ensuring safety must adjust flexibly according to traffic changes, road conditions, environments, and situations. Danger prediction, and situational judgment are obtained from personal knowledge based on experiences and memory, and on collective intelligence in terms of experience-based stories from their family and friends, news from TV, radios, and newspapers, and lessons learned at driving schools [1]. Existing prediction models are hindered by limitations of event-based prediction using statistical information and probability models from sensor data and its histories

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