Book Description

Advanced driver assistance systems (ADAS) are a subject of increasing interest as they are being implemented on production vehicles and also continue to be developed and researched. These systems need to work cooperatively with the human driver to increase vehicle driving safety and performance. Such a cooperation requires the ADAS to work with the specific driver with some knowledge of the human driver’s driving behavior. To aid such cooperation between human drivers and ADAS, driver models are necessary to replicate and predict human driving behaviors and distinguish among different drivers. This dissertation presents several lateral and longitudinal driver models developed based on human subject driving simulator experiments that are able to identify different driver behaviors through driver model parameter identification. The lateral driver model consists of a compensatory transfer function and an anticipatory component and is integrated with the design of the individual driver’s desired path. The longitudinal driver model works with the lateral driver model by using the same desired path parameters to model the driver’s velocity control based on the relative velocity and relative distance to the preceding vehicle. A feedforward component is added to the feedback longitudinal driver model by considering the driver’s ability to regulate his/her velocity based on the curvature of his/her desired path. This interconnection between the longitudinal and lateral driver models allows for fewer driver model parameters and an increased modeling accuracy. It has been shown that the proposed driver model can replicate individual driver’s steering wheel angle and velocity for a variety of highway maneuvers. The lateral driver model is capable of predicting the infrequent collision avoidance behavior of the driver from only the driver’s daily driving habits. This is important due to the fact that these collision avoidance maneuvers require high control skills from the driver and the ADAS intervention offers the most benefits, but they happen very infrequently so previous knowledge of driver behavior during these incidents cannot be assumed to be known. The contributions of this dissertation include 1) an anticipatory and compensatory lateral driver steering model capable of modeling a wide range of in-city and highway maneuvers at a variety of speeds, 2) the combination of the lateral driver model with the addition of defining an individual driver’s desired path which allows for increased modeling accuracy, 3) a predictive lateral driver model that can predict a driver’s collision avoidance steering wheel angle signal with no prior knowledge of the driver’s collision avoidance behavior, only data from every day, standard driving, 4) the addition of a longitudinal driver model that works with the existing lateral driver model by using the same desired path and is capable of replicating an individual driver’s standard highway and collision avoidance behavior, and 5) A feedforward longitudinal driver model based on regulating the driver’s velocity along his/her desired path is added to the existing feedback longitudinal driver model that together are capable of modeling an individual driver’s velocity for lane-changing and collision-avoidance maneuvers with less than 0.45 m/s (1 mph) average error.