TECHNICAL PAPER

Pairdriver® *1
Haptics-based Collaborative Steering Control Framework
for Automated Driving
T. NAKADE R. FUCHS

H. BLEULER

J. SCHIFFMANN

Over-reliance on automation in transportation systems is known to be the cause of new types of accidents. JTEKT
proposes a driver-oriented automation strategy to achieve collaborative steering that relies on three functionalities:
interaction, arbitration, and inclusion. This article is a short version of a paper published January, 2023 in thejournal Communications Engineering which is part of the Nature Portfolio1).
Key Words: physical human-robot interaction, collaborative shared control, electric power steering, Advanced
Driver Assistance System (ADAS), Automated Driving (AD)

1. Introduction

2. Human in the Loop

As a consequence of the fast adoption of driving
automation systems, most vehicles available on themarket are the result of a robot-centered development
approach. A few decades ago, the major challenges facedby engineers were to implement sensors and control
capability enabling the vehicle to follow and remain
within a lane. For safety and to ensure compliance withthe evolving regulations, driver monitoring systems(hands-on detection, head and gaze cameras) and override
or takeover strategies completed the necessary equipment.
In this manner, the human driver has been consideredafter the development of the robot-like vehicle. Focusing
on lateral control, the majority of vehicles use an override
strategy, which segregates manual from automatedsteering operation. The required torque threshold tooverride is set based on the design philosophy of eachOEM. Whether the torque threshold to override is set toa low or a high value, the concept of “driver assistance”
as the notion of working together is biased as it gives
the impression that the driver can be replaced by theautomation 2) . Consequently, level-2 ADAS do not
fulfill their objective of reducing the workload without
disengagement of the driver. The discontinuous steering
operation is assumed to be one of the causes of driver
misuse and disengagement.

The concept of “human in the loop” comes from theaviation industry as a safety mechanism that shaped legal
accountability in human-robot interaction. “Human in theloop” is an evocative metaphor, which suggests that thedriver in an automated driving car is keeping a certain
degree of situational awareness, and is consequently
able to react appropriately in the event of an automation
failure. However, this concept does not evoke how thedriver can affect the course of the automated vehicle,
rather it is generally interpreted in the industry as ahandover, which makes a clear separation between
automated and manual operation.

*1 Pairdriver® is a trademark of JTEKT Corporation.

76

3. From Looping to Teaming
In vehicles which are on the market today, drivermonitoring systems are becoming standard equipment toensure that drivers remain attentive to the road. However,appropriate safe reaction in the case of a failure cannot
be guaranteed. One of the ironies of automation is that as
the level of robot autonomy grows, the risk of accidents
will increase. Unlike an override strategy, “teaming”
is another approach based on the observation that “twois better than one.” It emphasizes that autonomy is notabout separation or isolation, but is a matter of consistentconnection and relations of mutual influence 3). This
report introduces haptic shared steering control which has
been developed motivated by the benefits on safety andintuitive operation of automated vehicles. This concept is

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−Pairdriver® Haptics-based Collaborative Steering Control Framework for Automated Driving−

often compared to horseback riding. The rider can convey
his or her intention to the horse “where does he wantto go” through the reins. On the other hand, the horse
understands the rider's order but also still has the abilityto look at the situation by itself and independently secure
the ride in case of danger. The steering system plays therole of rein, and the automated driving system must be
open for negotiation with the driver. It is essential that thedriver monitor the automation, and vice versa. Therefore,
it would be safer that the driver and the automation could
team up as if they were close trustworthy partners who
can watch out for each other.

cooperation is the education role arbitration, which is
critical for gaining new capability by ensuring a certain
degree of engagement.
・Co-activity occurs when the two interacting agents,
without knowledge of each other’s actions, incidentally
succeed in a common task.
・Collaboration features no fixed roles distribution. But
rather adapts the distribution to accommodate the otherwhile still considering its own perspective.
・Competition, similarly to collaboration, is a symmetricarbitration where the role distribution opposes the otherwhile considering its own perspective.
Arbitration (robot control) of the selected type ofinteraction is performed by estimating the humansensorimotor control (goal and impedance) and adapting
accordingly the robot response. When the haptic
communication is settled, each agent adjusts their motion
accordingly. For the automated driving application, it isnecessary to decide the type of interaction based on road,traffic and driver conditions. When the deviation resulting
from the interaction is large, assimilation of the driverintent into the AD trajectory, or re-routing, is computed tocomplete the collaborative steering experience.

4. Tactical and Operational Steering
Control
The proposed control aimed to “unify driver andautomation” by utilizing prior knowledge of physicalhuman-robot interaction to enhance the shared controlof steering systems in automated driving, while trying toreproduce the “horse and rider” metaphor. By utilizing
the knowledge of previous research on physical humanrobot interaction, we aim to develop a “automateddriving system that can be operated by the driver moreintuitively”. To achieve this objective, we propose themulti-level haptic shared control named Pairdriver ®
shown in Fig. 1. It is inspired by human-humancollaboration. For example, when two people work
together to carry a desk, first, they communicate theirintention via interactive force to allocate roles (e.g. leaderand follower) and to negotiate the direction to go. Whena robot is interacting with a human, the role allocation isachieved by changing between one of the four types ofinteraction4):
・Cooperation takes place when the two agents work
towards a common end and need each other to reachthe goal. Part of cooperation is the education role
arbitration, which is critical for gaining new capability
by ensuring a certain degree of engagement. Part ofHaptic loop

5. Interaction, Arbitration, and Inclusion
Three functions have been developed to realize theconcept proposed (Fig. 1). They enable the driver tointervene with the automation both temporarily as a local
deviation and permanently as a manually triggered rerouting.
・Interaction, which consists of providing the capabilityof haptic shared control to the steering system.
Admittance control5) is applied to enable the driver
to manually deviate the vehicle from the planned
trajectory without deactivation of the automation andwithout impairing the tracking performance.
・Arbitration refers to the role allocation among thedriver and the automation when attempting to share

Interaction

Driver torque:

Manualdeviationn Automation
torque: aTrajectory
planning

Interaction
type

Arbitration

Inclusion

Trajectory
tracking++dAngle
Ancontrolto acceleration
and brake controls

EPS
Steering state
Vehicle state

Vision loop

Fig. 1 Schematic structure of proposed control

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−Pairdriver® Haptics-based Collaborative Steering Control Framework for Automated Driving−High precision GNSS

Required maneuver
100 m
100 m
100 m

Nominal AD trajectory

100 m
100 m

3.5 m

3.5 m

Fig. 2 Overview of prototype vehicle test

The proposed control framework has been implemented
in an actual test vehicle (see Fig. 2). The vehicle tracks apredefined trajectory (nominal AD trajectory) at 60 km/
h using cruise control and position feedback from a highprecision GNSS system (Global Navigation SatelliteSystem). The driving scenario is a double lane change
on a three-lane 1.5 km straight course. The nominal
trajectory of the automation lies on the center of themiddle lane and the vehicle is controlled to track thisnominal trajectory using the Stanley trajectory tracking
model6). The driver is free to intervene and to deviate thevehicle away from the nominal AD trajectory.
The practical verification of the proposed multi-level
haptic control demonstrates consistent response from thetest vehicle (Fig. 3 and Fig. 4). First, the driver interacts
with the automation under the control allocation set by
the arbitration rule. Because the driver and automation

Resistance torque at actual trajectoryPlanned trajectory
Adapted trajectory
Position error between adapted and actual trajectory

Resistance torque at actual trajectoryPlanned trajectory
Adapted trajectory
Position error between adapted and actual trajectory

Fig. 3 Experimental result of Collaboration mode78

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ion, mLatera

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ioal
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200

5po4000n,600

2m6ngaldinng

05l posit
ion, mLatera8itu

200

5po4000sit

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600

6. Prototype Vehicle Verification

Lo4,m6framework features the following advantages:
・Compatible with all levels of automation 0–4, where
the human can still take part in the driving.
・Integration of the ADAS functions and continuous
operation in shared control mode (override-freeADAS).
・ADAS functions that satisfy multi-objective
requirements related to vehicle motion and driver intent
to effectively contribute to better traffic safety.

Resistance torque, N・m

8Lo

Resistance torque, N・mlateral control of the vehicle. More specifically, thecontrol is designed so that the automation interacts withthe driver based on a type of arbitration. For example,
if both driver and automation work towards centering
the vehicle in the lane, then the type of interaction iscollaboration. However, arbitration may change theinteraction type to competition when the vehicle isgetting close to the lane marking for preventing lane
departure. This shows that the type of interaction is
likely to vary dynamically as a function of the vehicle
position, the traffic conditions, the driver state, etc.
・Inclusion consists in adapting the trajectory of theautomation following a driver intervention. If themanually induced deviation of the vehicle fromthe planned trajectory is sufficiently large, then theautomation has to assimilate this correction by replanning the path accordingly.
These three functions constitute a multi-level control
framework. Interaction and arbitration functions arelocated at the operational level as they render the steering
reaction torque of the ADAS, while the inclusion
function takes place at the tactical level of trajectory
planning. This enables driver initiated rerouting andconsistent coordination of all vehicle actuators. In thisway, automated vehicles, which rely on sight only, areaugmented with the incorporation of the driver intent.
The driver is neither replaced by nor excluded from theautomation, rather their role remains active to the benefitof trust building and driving safety. The proposed control

Fig. 4 Experimental result of Competition mode

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−Pairdriver® Haptics-based Collaborative Steering Control Framework for Automated Driving−

impedances are complementary in “Collaboration” mode(Fig. 3), the torque peak that is observed is relatively
low. Second, the lateral deviation induced from theinteraction is propagated to the trajectory planning via
inclusion control. The reaction torque generated by theautomation is reduced. Consequently, the vehicle tracks
the left and right lanes without sustained manual torque.
In “Competition” mode (see Fig. 4), the automation
impedance varies together with that of the driver. Thedriver cannot apply a torque high enough to deviate
the vehicle from the AD trajectory without reaching
the maximum capacity of the steering system. No
assimilation of the manual intervention is verified, andthe vehicle tracks the trajectory developed without hapticcontribution.
In summary, the haptic cue communicated to the driveris two-fold. First, the driver feels the set role allocationand may be able to generate a lateral deviation of thevehicle. When this deviation is large enough, inclusion
occurs by means of haptic adaptation of the trajectory.
Second, the interaction torque remains bounded indicating
to the driver that his intervention has been assimilated.
These haptic cues contribute to the realization of an
intuitive collaborative steering.

Table 1 Control modes for quantitative study
Mode no.

01

23

Modes
(a)

4

1
0.9
0.8
0.7

12

3

1

Co-activity

Inactive

2

Collaboration

Inactive3

Co-activity

Active4

Collaboration

Active

4

10.5
01

2

34
Modes

Modes
(b)

53 4
1 2
Participants(c)Normalized StE , 

0.5

Normalized DrE , 

Normalized StE , 

Normalized DrE , This section presents an evaluation of several
individuals to verify the usefulness of the proposed multilevel haptic control framework. Five participants withan average age of 35 (from 29 to 44) years old took partin the driving assessment on the test vehicle shown in
Fig. 2. All participants were experienced drivers andreported an average annual travel distance of 5 800 km.
The participants were required to execute double lanechange maneuvers, as illustrated in Fig. 2 with the four
different control modes (see Table 1) set in random order.
All participants drove twice under each control mode andthe averages of these trials are used for this study.
Two criteria are proposed for the assessment of thecollaborative steering: driver effort (DrE) and steering
entropy (StE). DrE corresponds to the driver torquesteering effort throughout the test duration, while StE is
1.1

Inclusiona criterion representing the smoothness of the evolutionof the steering angle that is commonly used to quantify
maneuverability7). Control modes with lower DrE andStE allow for a smoother operation with less effort forthe driver. Statistical differences between the control
modes were analyzed using one way analysis of variance(ANOVA), and multiple comparisons between specificcontrol modes were executed via paired samples t-tests.
The results obtained with each control mode aresummarized in Fig. 5. Figure. 5 (a) shows that theDrE significantly decreases in the order of modes 1-4(p<0.001) according to the ANOVA results. In particular,
there is a large gap in DrE under modes 3 and 4 comparedto modes 1 and 2. This is interpreted as the result of theimplementation of inclusion, where the AD trajectory wasadapted to match the driver intention so that sustained
manual torque was no longer required during the lanechange maneuver.
Further, Fig. 5 (b) suggests that the application ofarbitration reduces the average of StE both with andwithout inclusion, i.e. the maneuver was executed moresmoothly. StE shown in Fig. 5 (b) suggests that thevariability between participants was higher than thatof DrE (Fig. 5 (a)), especially under modes 1 and 2.
In addition, the participants can be classified into twogroups: group 1 includes participant 1 and 2, and group
2 includes the others. The StE is smaller and the DrE
larger in group 1 compared to group 2 (see Fig. 5 (c) and(d)). From these trends, it can be inferred that participantsof group 1 applied more effort to achieve a smoothmaneuver, while participants of group 2 operated with asmaller torque input at the expense of smoothness. Thesevariations in driver behavior suggest that control modes
1 and 2 may lead to a low rate of acceptance, whereas

7. Quantitative Study

1Arbitration

10.8
12Modes3453 41 2Participants(d)Fig. 5 Result of individual driver assessment

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−Pairdriver® Haptics-based Collaborative Steering Control Framework for Automated Driving−control modes 3 and 4, which yield a smaller variability,
are likely to be accepted by a wide range of drivers.

8. Conclusion
This report introduced a collaborative steering control
concept for driver-oriented automation. With the proposedcontrol, the driver receives steering reaction torque fromthe automation via the steering wheel according to apredefined interaction type. Further, the driver intentions
is reﬂected to the trajectory planning of the automation.
The driver input is propagated at both operational andtactical levels, enabling intuitive haptic communication
with the automation. By applying the proposed
control framework to individual ADAS functions, it
encourages the driver to be engaged in the driving taskin partial automation. At higher-level of automation, theindependency can be secured while reﬂecting the driverintention.

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T. NAKADE*

R. FUCHS*

H. BLEULER**

J. SCHIFFMANN****S y s t e m s I n n o v a t i o n R & D D e p t . , R e s e a rc h &Development Division** School of Engineering, Ecole Polytechnique Fédéralede Lausanne (EPFL)*** Laboratory for Applied Mechanical Design (LAMD),
Ecole Polytechnique Fédérale de Lausanne (EPFL)

JTEKT ENGINEERING JOURNAL English Edition No. 1020E (2024)