Abstract
In this paper, a car-following model considering various driving styles is constructed to fulfill the personalized needs of different users of autonomous vehicles. First, according to a set of selection rules, car-following events are selected from the Next Generation Simulation (NGSIM) dataset, and then through an unsupervised machine learning method, the extracted data are divided into two styles, i.e., conservative and aggressive. Statistical analysis is then conducted to analyze the differences in vehicle speed, acceleration, desired time headway, and so on between both driving styles. Based on the analysis, a car-following model based on model predictive control is designed. Experimental results from testing data show that the proposed car-following models demonstrate different driving styles in terms of safety, comfort, and effectiveness. The conservative driving model is safer and more comfortable than the radical driving model, but the driving efficiency is low.
Highlights
A car-following model is one of the most important microscopic traffic flow models [1] and depicts the longitudinal behavior of a vehicle and its interaction with leading vehicle(s) in the same lane [2]
Controlling vehicle velocity to maintain a safe and comfortable following distance is the main goal of a car-following model [3]. e first car-following model was developed in the 1950s [4], and a large number of traditional car-following models have been proposed since [5], e.g., the intelligent driver model (IDM) [6], Gazis–Herman–Rothery (GHR) model [7], optimal velocity model [8], and Wiedemann model [9]
time to collision inverse (TTCi), time headway, and the absolute value of acceleration/deceleration were selected as the behavior characteristics to reflect the driving style
Summary
A car-following model is one of the most important microscopic traffic flow models [1] and depicts the longitudinal behavior of a vehicle and its interaction with leading vehicle(s) in the same lane [2]. Luo et al [10] proposed a car-following model based on a model-predictive-control (MPC) framework with multiple objectives. Zhu et al [3] proposed a model based on reinforcement learning that, through training, can effectively control the speed during car following. E APF values are used in the MPC model design process In this way, people’s driving habits and styles are added to the controller. A personalized car-following model based on an MPC algorithm, which can effectively solve the multiobjective optimization problem under constraints, is proposed . E multiple objectives include minimizing the following: (1) the error between actual inter-vehicle distance and desired inter-vehicle distance to reflect driving styles, (2) the relative speed to the leading vehicle to maintain following behavior, and (3) the acceleration and the jerk to ensure comfort.
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