A Personalized Human Drivers’ Risk Sensitive Characteristics Depicting Stochastic Optimal Control Algorithm for Adaptive Cruise Control

Jiwan Jiang,Jiaming Wu,Fan Ding,Yang Zhou,Huachun Tan

doi:10.1109/access.2020.3015349

Abstract

This paper presents a personalized stochastic optimal adaptive cruise control (ACC) algorithm for automated vehicles (AVs) incorporating human drivers’ risk-sensitivity under system and measurement uncertainties. The proposed controller is designed as a linear exponential-of-quadratic Gaussian (LEQG) problem, which utilizes the stochastic optimal control mechanism to feedback the deviation from the design car-following target. With the risk-sensitive parameter embedded in LEQG, the proposed method has the capability to characterize risk preference heterogeneity of each AV against uncertainties according to each human drivers’ preference. Further, the established control theory can achieve both expensive control mode and non-expensive control mode via changing the weighting matrix of the cost function in LEQG to reveal different treatments on input. Simulation tests validate the proposed approach can characterize different driving behaviors and its effectiveness in terms of reducing the deviation from equilibrium state. The ability to produce different trajectories and generate smooth control of the proposed algorithm is also verified.

Highlights

Automated vehicles have drawn considerable attention widespread from the public recently since they have been expected to have a transformative impact on road transportation, for instance, to address critical traffic issues such as energy and capacity shortage
For the linear exponential-of-quadratic Gaussian (LEQG) problem defined by Eq (6), Eq (12), Eq (13) and Eq (26), the output feedback case optimal control can be obtained by using the separation principle [30], which states that control and estimation are two independent processes in designing
EXPERIMENTS AND RESULTS ANALYSIS In order to validate the performance of the proposed stochastic linear optimal control method, numerical simulation experiments have been conducted since field test is expensive and beyond scope

Summary

INTRODUCTION

Linear ACC controllers usually design the acceleration to be proportional to the spacing deviation and the relative speed with the predecessor, which are fast in computing and easier to apply in practice. The remainder of the paper is organized as follows: Section II presents the continuous and discretized system state-space formulation and introduces control design. STATE-SPACE FORMULATION AND CONTROL DESIGN This section starts by presenting the system state-space formulations in both continuous and discretized form, introduces the proposed LEQG stochastic optimal control problem. The above system can be formulated as a linear time invariant (LTI) system with system state x(t) and control input u(t) by assuming the leading vehicle follows a constant speed during each sampling interval, the state equation follows: x(t) = Ax(t) + Bu(t) + V (t). The system becomes more realistic and are available for digital-computer implementation

STATE FEEDBACK CONTROL STRATEGY

HETEROGENEOUS DRIVING BEHAVIOR CAUSED BY

CONCLUSION