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Shared Control Framework and Application for European Research Projects



Current commercial Advanced Driver Assistance Systems (ADAS) assist the driver indirectly through warning signals. However, a new generation of ADAS and Automated Driving applications, known as Shared Control, where driver and automation control the vehicle together, have the potential to influence upcoming functionalities, improving the driving performance and reducing the driver’s physical and mental workload. The development of such a system has the attention of the European Commission, and different Research Innovation Actions (RIA) are developing new technologies for the human-centered design of partially and highly-automated vehicles. In particular, the PRYSTINE and HADRIAN projects are facing the challenge of sharing the authority of the dynamic driving task between driver and automation. In this sense, a common approach is shared between these projects to combine the necessary systems for a complete collaborative driver-automation framework. The integration of a Driver Monitoring System, a cooperative HMI, and a Shared Control System is part of their goals. In particular, the control system in charge of changing the control authority will be presented in this article for a collaborative overtaking scenario, analyzing two modalities: a collision-avoidance system, and a control transition system. Results, discussion, and future challenges are presented.
Shared Control Framework
and Application for European
Research Projects
Mauricio Marcano1,2(B
), Sergio Diaz1, Myriam Vaca1,2, Joshu´eP´erez1,
and Eloy Irigoyen2
1TECNALIA, Basque Research and Technology Alliance (BRTA),
48160 Derio, Spain
2University of the Basque Country, 48013 Bilbao, Spain
Abstract. Current commercial Advanced Driver Assistance Systems
(ADAS) assist the driver indirectly through warning signals. How-
ever, a new generation of ADAS and Automated Driving applications,
known as Shared Control, where driver and automation control the vehi-
cle together, have the potential to influence upcoming functionalities,
improving the driving performance and reducing the driver’s physical and
mental workload. The development of such a system has the attention
of the European Commission, and different Research Innovation Actions
(RIA) are developing new technologies for the human-centered design of
partially and highly-automated vehicles. In particular, the PRYSTINE
and HADRIAN projects are facing the challenge of sharing the author-
ity of the dynamic driving task between driver and automation. In this
sense, a common approach is shared between these projects to combine
the necessary systems for a complete collaborative driver-automation
framework. The integration of a Driver Monitoring System, a cooperative
HMI, and a Shared Control System is part of their goals. In particular,
the control system in charge of changing the control authority will be
presented in this article for a collaborative overtaking scenario, analyz-
ing two modalities: a collision-avoidance system, and a control transition
system. Results, discussion, and future challenges are presented.
Keywords: Shared control ·Autonomous vehicles ·Driver-automation
cooperation ·Arbitration ·Partially automated vehicles
1 Introduction
Automated Driving (AD) applications have increased in impact and maturity in
the last years, due to the technological advances in perception sensors, processing
hardware capabilities, artificial intelligent techniques, and new legal concessions
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to test automated vehicles on public roads [1]. However, despite the impressive
demonstrators of automated driving functionalities, including commercial vehi-
cles with partially automated driving features, the realization of such technology
at a greater scale in our society is still a challenge [2], which could take decades
to be achieved, while facing the technological, legal, and social barriers.
In parallel, the relevant advances achieved up to know can contribute to the
development of human-centered vehicles that offer continuous control support
during the driving task, reducing mental and physical workload, and ensuring a
safer, more comfortable, and less demanding experience [3]. This collaborative
driving strategy is suitable for inclusion as an especial mode of operation in
partially automated vehicles (SAE Level 2 (L2) [4]). In these vehicles, automa-
tion has control over steering and pedals, but the driver has to monitor the
environment and be ready to take full control in critical scenarios.
Nonetheless, current L2 vehicles, work under the on/off standard, with almost
any cooperative control interaction with the driver. Furthermore, when the driver
is out of the control loop, it leads to over-trust in automation, and consequently,
increases the chance of a late take-over maneuver [5]. In this sense, ADAS with
control cooperative components (or shared control ADAS) is a topic of interest
in the AD research community. In these systems, the driver and the automation
are guiding the vehicle together, with the proper authority that corresponds to
the situation (e.g., driver distraction increases the authority of automation).
Shared control in the context of automated driving, is defined using the ter-
minology presented by Abbink [6] as: “driver and automation interacting con-
gruently in a perception-action cycle to perform a dynamic driving task that
either the driver or the system could execute individually under ideal circum-
stances”. Also, a joint effort with Flemisch [7] has included shared control in a
cooperative framework at different task support levels: 1) operational, related to
the control task, 2) tactical, for the maneuvers and decisions, and 3) strategical,
which refers to the planning strategy of going from A to B.
The study of shared control systems has particular interest in steering appli-
cations, which is the most critical control interface in the driving task. There-
fore, many European projects, as part of the mobility needs for a more safe
and comfortable driving, have faced the challenge of human-machine coopera-
tion in automated vehicles, aiming for a collaborative system that: 1) increase
safety in dangerous maneuvers, such as lane change with a blind spot, 2) assist
driver in authority transitions to ensure a smooth, progressive, fluid and safe
control resuming, and 3) make the driving task comfortable and less demanding.
These ADAS for partially automated vehicles have been studied in different EU
research projects such as HAVEit [8], DESERVE [9], and the ABV Project [3].
Recently, two European projects continue this research line, looking for
the implementation of collaborative human-centered vehicles using the shared
control concept. First, PRYSTINE (Programmable Systems for Intelligence in
Automobiles) project [2,10], studies shared control under the framework of fail-
operational systems. Secondly, HADRIAN (Holistic Approach for Driver Role
Integration and Automation Allocation for European Mobility Needs), makes
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Shared Control Framework and Application for European Research Projects 3
emphasis on the dynamic adjustment of (fluid) human-machine interfaces (HMI)
that take environmental, vehicle and driver conditions into account to provide
adaptive signals and information, transfer control authority, and lead to safe
transition between automated driving levels. These two projects have similarities
and differences that will be highlighted in this article. Additionally, a common
design framework will be presented, with an emphasis on the shared control
system design that will be part of both approaches.
This article is structured as follows: Sect. 2presents the description and
objectives of the projects, together with the common framework, Sect. 3describes
the design of the shared control system. Section 4analyzes the results of the
system in an overtaking maneuver. Lastly, Sect. 5closes with conclusions and
future works.
2 Driver-Automation Framework for PRYSTINE
The European Commission has granted funding for the development of Research
Innovation Actions (RIA) in the context of automated driving. In this sense,
PRYSTINE and HADRIAN are part of the ongoing projects that evaluate,
design, and implement the human-centered concept in vehicles SAE Level 2,
3, and 4. PRYSTINE focuses the attention in fail-operational systems with an
emphasis on the perception of the external environment using cameras, radar,
and LiDAR, but also, considering in-cabin sensor fusion to detect the driver
state. On the other hand, HADRIAN evaluates the human-centered design imple-
menting fluid interfaces to improve driver automation-interaction not only at the
operational level, but also from the human-acceptance perspective.
On the one hand, PRYSTINE intends to increase the Technology Readiness
Level (TRL) to TRL 3–4, with validation in a Hardware and Driver in the Loop
(HDiL) simulator. Conversely, HADRIAN push to take this technology to imple-
mentation in real vehicles and achieve demonstrations in relevant environments,
Tabl e 1. Comparison of scopes of PRYSTINE and HADRIAN
Period 2018–2021 2020–2023
Objective Fail-operational system Fluid interfaces
Test platform HWiL/DiL simulator Experimental vehicle
DMS Fusion of audio and vision-based
sensor for driver distraction and
Multisensor platform with driver
model and RT-learning process
HMI Visual HMI Multi-sensory HMI Haptic,
auditory, and visual
Scenario Distraction in urban environment
Authority transition in overtaking
Elderly driver assistance system
Acceptance One cycle testing Two iteration cycles
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increasing the TRL index to 5–6, with more emphasis on driver acceptance tests.
A more detailed comparison between these two projects is given in Table 1.
Additionally, in the context of these projects, a common control framework
is proposed to integrate the driver and the automation in the collaborative and
dynamic driving task. This integration requires interactions between different
systems related to automated driving functionalities. Previously, a general archi-
tecture has been proposed for fully automated vehicles by Gonzalez et al. [9],
with six high-level modules: acquisition, perception, communication, decision,
control, and actuation. However, there are additional necessary modules to be
included if the driver is sharing the authority of the vehicle with the automation:
1) a Driver Monitoring System (DMS), 2) a set of Human-Machine Interfaces
(HMI), and 3) a Shared Control System (SCS). These systems are integrated
into the original framework, and highlighted in green in Fig. 1, to indicate an
addition to the original architecture.
Driver Monitoring System: It evaluates the driver’s capability to execute
the driving task by his/her own, and indicates the degree of assistance needed.
According to the World Health Organization, most of the road traffic deaths
are due to driver recognition errors, such as distraction or inattention [11]. That
is the reason for the importance of taking into account the driver state as a
variable of primary importance for decision making. The factors that can affect
the driver’s behavior can be assigned to a specific group taking into account
outer (i.e. surrounding cars and vehicle density) and inner factors (i.e. distrac-
tion, drowsiness(fatigue), and medical conditions) [12]. Other measures indicat-
ing physical and mental workload are relevant in this module.
Human-Machine-Interfaces: They help the driver to understand the automa-
tion intention, state, and actions, increasing situation awareness and trust in the
automated vehicle. In this sense, the system can communicate information to
the driver by 1) a visual screen, through text or images, for example showing
the representation of the environment with nearby vehicles, 2) haptic interfaces,
using vibration in the pilot seat, at the steering wheel, or any other surface in
contact with the driver, and 3) audio warnings, either by sound alerts or tutor-
ing voice. The design of such strategies should follow the principles of comfort,
usability, and avoid excessive information to not overwhelm the driver.
Shared Control System: It is the critical module of a human-centered vehi-
cle framework where the decision and control actions are implemented. In the
decision block, an arbitration sub-module is in charge of distributing the author-
ity between the driver and the automation. This module calculates two relevant
values: 1) the Level of Haptic Authority (LoHA), that represents the strength
of intervention of the system when safety is compromised, (it is the stiffness of
the controller around the optimal command [13]), and 2) the Level of Shared
Authority (LoSA), a continuous value which indicates the mode of automation,
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Shared Control Framework and Application for European Research Projects 5
Haptic devices
Shared control
Steering Wheel System
sensors Obstacle detection
& classification
Driver monitoring
Vehicle to
infraestructure Vehicle to vehicle
Vehicle to X
Local planning
Global planning
Reacti ve
Fig. 1. Driver-automation framework
either fully automated or manual, to allow smooth, progressive, and comfortable
transitions. This shared control system is explained in detail in the next section.
3 Shared Control System
The shared control system architecture is comprised of two subsystems. First, the
lateral shared controller that assists the driver in the lane keeping task. Secondly,
an arbitration system calculates the two levels of authority. On the one hand, the
Level of Haptic Authority (LoHA) increases the default authority of the steering
controller to avoid hazardous situations. On the other hand, the Level of Shared
Authority (LoSA) manages the transitions of control from manual-to-automated
and automated-to-manual, as a continuous value.
3.1 Lane-Keeping Controller
The lane-keeping system for shared control applications makes use of the opti-
mization framework of Model Predictive Control (MPC) as in previous works
[14,15], considering the torque at the steering wheel as the control input, to
facilitate the driver-automation cooperation. The design of the controller has
two considerations: 1) the representation of the system model through differen-
tial equations, and 2) the configuration of the optimization problem.
The System Model: It comprehends three sub-systems: the vehicle, the lane-
keeping model, and the steering mechanism. This combination represents the
road-vehicle model. The vehicle model uses dynamic bicycle system equations for
a front steered vehicle. The lane-keeping model includes two differential equation
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respective to the lateral error (ey) and angular error (eΨ). The steering model
uses the inertia (J) and damping (B) model, which relates the steering wheel
angle with the steering torque. It also considers an approximation of the self-
aligning torque proportional to the lateral force of the front tire and includes
the torque of control (T) as part of the model. For more information on the
complete road-vehicle model, refer to [15].
The Optimization Problem: It considers three different optimization func-
tions: 1) the tracking performance, to follow the reference trajectory (ztra =
Ψ]), 2) the driving comfort, minimizing speeds (zcom =[vy,w]), and 3)
the control conflicts, optimizing the driver control effort (ztor =[T, ΔT]). The
reference of the controller comes from an offline trajectory with information of
curvature and tracking errors. The solution is obtained with the use of ACADO
toolkit, an online optimization problem solver [16].
3.2 The LoHA Controller
Additionally to the lane-keeping controller, the shared control system needs a
sub-module to increase the intervention of the controller to override the driver’s
intention in situations when safety is compromised. In this sense, a LoHA con-
troller is added in cascade to the lane-keeping controller. The LoHA is the stiff-
ness around the optimal steering angle. The higher the LoHA, the harder for the
driver to override automation. This controller is defined as a proportional term to
the difference between driver and automation command TLoHA =KLoHA(θθd).
It changes the original stiffness of the system K, to a new equivalent value
Keq =K+KLoHA . Therefore, to keep the system stable, a new equivalent
damping is found using the damping ratio formula Beq =B(K+KLoHA)/K
resulting in the following LoHA controller:
TLoHA =KLoH A(θθd)+(Beq B)w(1)
3.3 The Arbitration System
The arbitration system is based in a Fuzzy Inference System, a powerful soft
computing technique that allows to include human knowledge into the design
of control and decision algorithms [17]. The fuzzy scheme comprehends four
inputs and two outputs. The representative inputs are: 1) the driver’s intention,
considering the lateral error and its derivative, 2) the driver effort, measured as
the equivalent torque at the steering wheel, and 3) the risk of collision, calculated
as the time-to-collision with the vehicle in the left lane. These three variables
allow us to calculate the following outputs: 1) the LoHA, which represents the
need for a greater intervention of the system to avoid collisions, and 2) the LoSA,
which is the variable authority for a progressive transition from automated-to-
manual and manual-to-automation. The design rules are shown in Tables 2and
3for the conditions of a low and high risk of collision respectively.
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Shared Control Framework and Application for European Research Projects 7
Tabl e 2. Rules for low collision risk
eyLow Med High
dey/dt ↑ ↓
=0 A A A A T M M M M
>0 T T T T M M M M M
Manual (M) - Transition (T) - Auto(A)
Tabl e 3. Rules for high collision risk
eyLow Med High
dey/dt ↑ ↓ ↑ ↓
=0 L L L L M H H H H
>0 L M M M M H H H H
Low (L) - Medium (M) - High (H)
4 Use Case and Results
This section presents the tests of the shared control system in a collaborative
overtaking maneuver, where the system assists the driver in three scenarios.
First, if the driver plans to do a lane change, but there is a high risk of collision
with the side vehicle, the system increases the LoHA to guarantee the safety
and guides the vehicle towards the main lane, as shown in the top of Fig.2.
Secondly, when there is low collision risk, the system decreases the LoSA con-
ceding the transition from automated-to-manual (see bottom of Fig. 2). Lastly,
once the driver has passed the front vehicle and returns to the main lane, the
system increases the LoSA to allow the transition from manual-to-automated
and continue with the lane-keeping assistance.
The implementation of this maneuver is performed by one real driver in
the HiL Automated Driving Simulator shown in Fig. 3. It is comprised of a
high-performance computer, running Matlab/Simulink, and communicates with
a steering wheel capable of a maximum torque of 15 N.m. The automated driving
software simulator is based on Dynacar [18], a vehicle dynamic software based
on a multi-body formulation.
Fig. 2. Collaborative overtaking Fig. 3. HDiL simulator platform
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Fig. 4. Shared control system results in three scenarios
The results of the three scenarios are shown in Fig. 4. First, the collision
avoidance system is tested with the vehicle starting in a fully automated mode.
Initially, the LoHA is very low as there is no risk of collision. Then, in the
second 16, the driver intends to make a lane change, but the system detects a
low time-to-collision with the left lane vehicle. The arbitration system maintains
the automated mode and increases the LoHA to strengthen the intervention
of the system ensuring safety. The system achieves an assistance torque of 10
N.m, and the driver releases the steering wheel. In this case, the system can
return to the lane without losing stability. On the one hand, safety was the pri-
ority, but also, the comfort was compromised with a lateral acceleration close to
2. The results also show that the MPC solver always found a feasible
solution calculated in less than 1.5 ms.
In the second scenario, the driver intends to do a lane change again, but in
this case, the system does not detect any collision risk and allows the transition
from automated-to-manual. It is shown in the second column of Fig.4, that the
LoSA is changed smoothly and progressively, making the transition comfortable
and understandable for the driver, with a maximum effort of 5 N.m in a short
period. The maximum lateral acceleration was kept close to 2 m/s2. Also, it is
observed that the variation of authorities, does not affect the calculation of a
feasible solution of the optimization problem.
Lastly, when the driver wants to return to the original lane after surpass-
ing the front vehicle, the system changes from manual-to-automated and keeps
assisting the driver in the lane-keeping task. In this case, the LoHA is low and
the LoSA changes progressively to 1 (fully automated mode). It is important
to mention that the behavior of the LoSA departing the lane and returning the
lane is different. In the first, an intermediate step is observed which is, in fact,
helpful for the driver to confirm the lane change intention. In the second one,
the transition is performed without medium steps, allowing activation of the
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Shared Control Framework and Application for European Research Projects 9
lane-keeping that is barely notable to the driver, as shown by the low lateral
acceleration and steering wheel angular velocity.
5 Conclusions and Future Works
This article presents a shared control framework for implementations in two
European RIA projects, PRYSTINE and HADRIAN, to improve the develop-
ment of advanced control techniques for human-centered vehicles SAE Level
2, 3 and 4. The shared control system is comprised of an arbitration function
that calculates the appropriate control authorities, based on fuzzy logic, a well-
known soft computing technique, and a cascade architecture controller including
an MPC and a PD controller for the lane-keeping task.
Results show the effectiveness of the system in a collaborative overtaking
maneuver. When the risk of lateral collision is high, the automation overrides
the driver’s intention increasing the level of intervention (LoHA). Conversely,
when there is no risk, and a lane change intention is recognized a transition of
authority takes place in a fluid, progressive, and comfortable manner by changing
the LoSA. The system is also able to reactivate the automated mode when
returning to the lane.
In future works, the integration of the shared control system with other com-
plementary modules such as the DMS and the cooperative HMI is necessary to
prove the feasibility of the complete collaborative framework. The implementa-
tion of this approach will be tested with different drivers for a complete driver
acceptance test and evaluates the utility of this cooperative control system in
passenger vehicles.
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... There are various cooperative control approaches catering to the specific needs of each domain, such as patient assistance [154], [155], autonomous driving [156], [157] unmanned aerial vehicle systems [158], [159], assembly [160], and remote operation [116], [149]. How the applications fit into the proposed PDE framework is shown in Table II. ...
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Advanced Driver Assistance Systems (ADAS) acting over throttle and brake are already available in level 2 automated vehicles. In order to increase the level of automation new systems need to be tested in an extensive set of complex scenarios, ensuring safety under all circumstances. Validation of these systems using real vehicles presents important drawbacks: the time needed to drive millions of kilometers, the risk associated with some situations, and the high cost involved. Simulation platforms emerge as a feasible solution. Therefore, robust and reliable virtual environments to test automated driving maneuvers and control techniques are needed. In that sense, this paper presents a use case where three longitudinal low speed control techniques are designed, tuned, and validated using an in-house simulation framework and later applied in a real vehicle. Control algorithms include a classical PID, an adaptive network fuzzy inference system (ANFIS), and a Model Predictive Control (MPC). The simulated dynamics are calculated using a multibody vehicle model. In addition, longitudinal actuators of a Renault Twizy are characterized through empirical tests. A comparative analysis of results between simulated and real platform shows the effectiveness of the proposed framework for designing and validating longitudinal controllers for real automated vehicles.
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What are the main contributing factors to road accidents? Factors such as inexperience, lack of skill, and risk-taking behaviors have been associated with the collisions of young drivers. In contrast, visual, cognitive, and mobility impairment have been associated with the collisions of older drivers. We investigated the main causes of road accidents by drawing on multiple sources: expert views of police officers, lay views of the driving public, and official road accident records. In Studies 1 and 2, police officers and the public were asked about the typical causes of road traffic collisions using hypothetical accident scenarios. In Study 3, we investigated whether the views of police officers and the public about accident causation influence their recall accuracy for factors reported to contribute to hypothetical road accidents. The results show that both expert views of police officers and lay views of the driving public closely approximated the typical factors associated with the collisions of young and older drivers, as determined from official accident records. The results also reveal potential underreporting of factors in existing accident records, identifying possible inadequacies in law enforcement practices for investigating driver distraction, drug and alcohol impairment, and uncorrected or defective eyesight. Our investigation also highlights a need for accident report forms to be continuously reviewed and updated to ensure that contributing factor lists reflect the full range of factors that contribute to road accidents. Finally, the views held by police officers and the public on accident causation influenced their memory recall of factors involved in hypothetical scenarios. These findings indicate that delay in completing accident report forms should be minimised, possibly by use of mobile reporting devices at the accident scene.
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Although automation-initiated and driver-initiated control transfers from automated to manual driving may yield unstable steering activity even when the drivers are focused on the road environment ahead, there are few studies on the development of control transfer methods at an operational level after a request to intervene (RTI). Therefore, we propose a shared authority mode connecting the automated and manual driving modes and a method for transferring control authority using haptic shared control to achieve smooth transfer. The results of driving simulation experiments demonstrate that the instability in the steering angular velocity originally present during control transfer is significantly.
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Shared control is an increasingly popular approach to facilitate control and communication between humans and intelligent machines. However, there is little consensus in guidelines for design and evaluation of shared control, or even in a definition of what constitutes shared control. This lack of consensus complicates cross fertilization of shared control research between different application domains. This paper provides a definition for shared control in context with previous definitions, and a set of general axioms for design and evaluation of shared control solutions. The utility of the definition and axioms are demonstrated by applying them to four application domains: automotive, robot-assisted surgery, brain–machine interfaces, and learning. Literature is discussed for each of these four domains in light of the proposed definition and axioms. Finally, to facilitate design choices for other applications, we propose a hierarchical framework for shared control that links the shared control literature with traded control, co-operative control, and other human–automation interaction methods. Future work should reveal the generalizability and utility of the proposed shared control framework in designing useful, safe, and comfortable interaction between humans and intelligent machines.
Automated and Highly-automated Vehicles still need to interact with the driver at different cognitive levels. Those who are SAE Level 1 or 2 consider the human in the loop all the time and require strong participation of the driver at the control level. To increase safety, trust and comfort of the driver with this kind of automation, systems with a strong cooperative component are needed. This paper introduces the design of a vehicle controller based on shared control, together with an arbitration system, and the design of a Human-Machine Interface (HMI) to foster the mutual understanding between driver and automation in a lane-keeping task. The driver-automation cooperation is achieved through incremental support, in a continuum spectrum from manual to full automation. Additionally, the design of an HMI to support the driver in a takeover maneuver is presented. This functionality is a key component of vehicles SAE Level 3 and 4.
Prompted by emerging developments in connected and automated vehicles, parallel steering control, one aspect of parallel driving, has become highly important for intelligent vehicles for easing the burden and ensuring the safety of human drivers. This paper presents a parallel steering control framework for an intelligent vehicle using moving horizon optimization. The framework considers lateral stability, collision avoidance and actuator saturation and describes them as constraints, which can blend the operation of a human driver and a parallel steering controller effectively. Moreover, the road hazard and the steering operation error are employed to evaluate the operational hazardous of an intelligent vehicle. Under the hazard evaluation, the intelligent vehicle will be mainly operated by the human driver when the vehicle operates in a safe and stable manner. The automated steering driving objective will play an active role and regulate the steering operations of the intelligent vehicle based on the hazard evaluation. To verify the effectiveness of the proposed hazard-evaluation-oriented moving horizon parallel steering control approach, various validations are conducted, and the results are compared with a parallel steering scheme that does not consider automated driving situations. The results illustrate that the proposed parallel steering controller achieves acceptable performance under both conventional conditions and hazardous conditions., "Hazard-evaluation-oriented moving horizon parallel steering control for driver-automation collaboration during automated driving," IEEE/CAA