A context identification layer to the reasoning subsystem of context-aware driver assistance systems based on proximity to intersections

Abstract

This study proposes an addition to the architecture of the context-aware Driver Assistance Systems (DASs) by introducing a context identification layer to the reasoning subsystem. The proposed layer contains two algorithms that work in sequence: the infrastructure detection algorithm and the driver classification algorithm, respectively. The infrastructure detection algorithm aims to identify intersection-related driving when the driver adjusts his or her behavior due to the presence of an intersection ahead. Then, the driver classification algorithm categorizes drivers into cautious, normal, and aggressive at both locations. Data from 64 drivers in a Naturalistic Driving Study was used to prove the concept of the proposed layer. Several behavioral measures were extracted, including following distance, relative speed, headway, acceleration, time-to-collision, and jerk. These behavioral measures were then used to train the algorithms in the context identification layer. The results of both algorithms supported the concept of the proposed layer. These results have implications related to driver behaviors including i) the intersection-related driving behavior can be detected, ii) the drivers tend to be relatively aggressive at intersections when compared to segments, and iii) the driver classification, which ignores the driver's relative location to intersections, were more likely to misclassify drivers as aggressive when they were in high intersection density areas such as downtown cores. The findings of this study emphasized the importance of context-aware DAS architecture that acknowledges and integrates the variation in driver behavior due to both a change in the surrounding environment and drivers’ individual needs.