An analysis of an elderly driver behaviour in urban intersections based on a risk potential model

Abstract

This paper proposes a driving safety evaluation method based on the parameter analysis of a driver model. Since the driving abilities, such as reaction, recognition and judgment time decrease with aging, it is difficult for elderly drivers to avoid the traffic accidents instantly. Therefore, the proactive safety driving is important for the elderly drivers to prevent the accidents. Then, the safety evaluation criteria of a driving behavior are useful to encourage the driver to keep proactive safety driving. And these criteria are useful to develop an advanced driver assistance system for elderly drivers. To evaluate the driving behavior, we use the parameters of a risk potential model which is the driver model to represent the speed and course control around the risk factors. Since the risk potential model represents the driving behavior by using a small number of the parameters, we make the assumption that the parameters are highly abstract the driving behavior and it is useful for analyzing the safety level of the driver. To evaluate the assumption experimentally, we collect the actual driving data of elderly, general and normative drivers for analyzing the parameters of the risk potential model and the safety level of the driver. Finally, the results show that the parameters of the risk potential model can be used to indicate the features of the driving behavior of the elderly drivers.

Deadlock-Free Planner for Occluded Intersections Using Estimated Visibility of Hidden Vehicles

“…Studies have shown that experienced and expert drivers tend to take proactive action to avoid collisions, e.g., by slowing down as they approach an intersection with poor visibility [19,33]. However, it is neither safe nor reasonable to assume that every driver approaching an intersection will behave in this manner.…”

Driving Stability Analysis Using Naturalistic Driving Data With Random Matrix Theory

“…Analyzing driving behaviors is a sophisticated task, since drivers' maneuvering pattern can be affected by many factors such as road environment (e.g., intersection or traffic congestion) and driver categories (e.g., age). For example, [29] applies kinematics analysis to extract features from driving data of several driving school instructors, general drivers, and elderly drivers when they drive passing urban intersections. It is shown that elderly drivers exhibit a common pattern that is different from young drivers.…”

“…The main drawback of using simulation data is that simulators cannot fully reflect the true driving condition. To collect driving data in practical traffic environments, carefully designed field-experiments are conducted in [23], [29], [40]. However, one potential issue of such methods is that drivers may be aware of the experiment purposes.…”