Article
Risk-Sensitive Rear-Wheel Steering Control Method Based on
the Risk Potential Field
Toshinori Kojima and Pongsathorn Raksincharoensak *
Citation: Kojima, T.;
Raksincharoensak, P. Risk-Sensitive
Rear-Wheel Steering Control Method
Based on the Risk Potential Field.
Appl. Sci. 2021, 11, 7296. https://
doi.org/10.3390/app11167296
Academic Editor: John McPhee
Received: 13 July 2021
Accepted: 6 August 2021
Published: 9 August 2021
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4.0/).
Department of Mechanical Systems Engineering, Tokyo University of Agriculture and Technology,
2-24-16 Naka-cho Koganei, Tokyo 184-0012, Japan; s200112w@st.go.tuat.ac.jp
* Correspondence: pong@cc.tuat.ac.jp; Tel.: +81-42-388-7397
Abstract:
Various driving assistance systems have been developed to reduce the number of automo-
bile accidents. However, the control laws of these assistance systems differ based on each situation,
and the discontinuous control command value may be input instantaneously. Therefore, a seamless
and unified control law for driving assistance systems that can be used in multiple situations is
necessary to realize more versatile autonomous driving. Although studies have been conducted on
four-wheel steering that steers the rear wheels, these studies considered the role of the rear wheels
only to improve vehicle dynamics and not to contribute to autonomous driving. Therefore, in this
study, we define the risk potential field as a uniform control law and propose a rear-wheel steering
control system that actively steers the rear wheels to contribute to autonomous driving, depending
on the level of the perceived risk in the driving situation. The effectiveness of the proposed method
is verified by a double lane change test, which is performed assuming emergency avoidance in
simulations, and subject experiments using a driving simulator. The results indicate that actively
steering the rear wheels ensures a safer and smoother drive while simultaneously improving the
emergency avoidance performance.
Keywords: vehicle dynamics; autonomous driving; four-wheel steering (4WS); risk potential field
1. Introduction
Autonomous driving systems have been practically used for normal driving as well
as emergency avoidance driving to realize a safe and comfortable traffic system. Several
autonomous driving systems, such as the lane keep assist system and automatic emergency
steering, already exist in practical use. However, the control laws of these driving assistance
systems differ in each situation. To achieve more versatile autonomous driving, it is
necessary to develop a driving assistance system that can be designed with a uniform
control law for both normal driving and emergency avoidance driving.
Therefore, the risk potential field is used as a uniform control law in this study.
There are many classic risks in vehicle dynamics control, such as sideslip, rollover, and
collision. In a previous study, a rollover prevention system using a steer-by-wire system
was proposed [
1
]. In this study, a risk potential field is used to reduce the collision risk
among possible risks in vehicle control. Several studies have investigated this control
method and applied it to various situations, such as highway merging scenes, emergency
avoidance scenes, and shared control [
2
–
6
]. As a previous study in our research group,
Sato et al. applied a dynamic risk potential to a highway merging scene, wherein the
risk potential changed based on the risk of road deviations. The system controls the
steering angle, acceleration, and deceleration to drive safely without colliding with other
vehicles or road boundaries. The simulation results showed that the dynamic risk potential
could reproduce merging behavior identical to that of an expert driver [
3
]. As an another
example of previous work, Inoue et al. applied the risk potential field to a shared control:
a driver assistance system with both the human and machine controlling the vehicle.
Herein, the driver and machine cooperate to ensure safe driving. They developed motion
Appl. Sci. 2021, 11, 7296. https://doi.org/10.3390/app11167296 https://www.mdpi.com/journal/applsci
