This document describes a study that developed a neuromuscular driver model to simulate human steering tasks. It presents a dynamic vehicle model including an electric power steering system. A two-level controller models the neuromuscular system and path-following behavior of drivers. Simulations were conducted using the integrated driver and vehicle models, including simulating an ISO double lane change maneuver. Future work is proposed to improve the driver model and optimize electric power steering controllers.

1. Study of Human Steering Tasks
using a Neuromuscular Driver Model
Naser Mehrabi
Mohammad Sharif Shourijeh
John McPhee
University of Waterloo
Department of Systems Design Engineering
1

2.  Table of Contents
 Introduction
 Dynamic Modeling & Controller Design
 Driver Model
 Results and Discussion
 Summary and Future Work
2

3.  Introduction  Vehicle Modeling  Driver Modeling  Simulations
 Electric Power Steering System
In a steering task, the following four systems interact:
1. Driver
Resistance Torque
2. Steering system
Driver Torque
Driver
• Mechanical system
• EPS controller
3. Vehicle Steering Mechanical
System
4. Environment
Steer Angle
Disturbance
Controller Output
Measurements
Vehicle Dynamics
EPS controller
3

4.  Introduction  Vehicle Modeling  Driver Modeling  Simulations
 Driver Modeling Approaches
1. Black box (controls) driver models
 Non-Predictive
 Predictive
2. Neuromuscular driver models
 Simple lag-delay
 Pick et al.(2006),
The muscles involved in the steering task are identified.

 A torque-driven driver model is developed.
 Hoult et al.(2008),
 Activation dynamics and metabolic energy consumption are
considered in a neuromuscular driver model, but EPS and vehicle
dynamics has not been considered.
4

5.  Introduction  Vehicle Modeling  Driver Modeling  Simulations
 Research Goals
1) Develop a symbolic vehicle model including a column assist-type
Electric Power Steering (EPS) system
2) Develop a neuromuscular driver model to study interaction between
a driver’s hand and steering wheel
3) Design an optimal EPS controller for general population to improve
steering feel
4) Utilize the information from the driver model to tune a subject-
specific EPS controller
5

6.  Introduction  Vehicle Modeling  Driver Modeling  Simulations
 Full Vehicle Model in MapleSim for High-fidelity Simulations
Full vehicle model subsystems:
 Rear Trailing arm
Suspension
 Front Double
Wishbone Suspension
 Column assist-type
EPS system
 Fiala Tire Model
6

7.  Introduction  Vehicle Modeling  Driver Modeling  Simulations
 Schematic View of EPS System
on
Tfricti
ular
c
Ang city
Includes following components: e ri n
g Wh
ee l
Velo
Ste
bc m Electric
Motor
 One degree freedom steering Torque Sensor (Kc)
mechanism bm
Km / r1
G
r2
 Torque sensor to measure driver
torque Gea
r
r2
r1
Box
 DC electric motor connected to the
steering shaft with a gear
 Rack and pinion
 Viscous damping and Coulomb
friction in the steering wheel and
Connected to
electric motor shaft vehicle
Rack and Pinion
7

8.  Introduction  Vehicle Modeling  Driver Modeling  Simulations
 Electric Power Steering Controller Design
 Control Objectives
Control objectives can be Vehicle
Self Alignment Torque
Steering Wheel Angle
categorized as: Disturbance Dynamics
 Assistance
 Sufficient assistant torque to the Driver Torque Steering
driver. + System
Steering Torque
+
Assist Torque
 Return-to-center control mode
Desired
 Returnability of steering wheel Current
after it is released. Electric The assist
PID
Motor - characteristic
 Damping control mode Measured
Current
 Improve the stability of straight-
line motion.
8

9.  Introduction  Vehicle Modeling  Driver Modeling  Simulations
 Driver Modeling Introduction
Driver model can be divided into two
sub-systems:
 Path-following controller
 Neuromuscular system
Driver Model
Desired Path Actual Path
Path Following Neuromuscular Vehicle
Controller System Dynamics
Desired Steering Angle Actual Steering Angle
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10.  Introduction  Vehicle Modeling  Driver Modeling  Simulations
 Path-Following Controller
Multi-point predictive driver model [1] :
 Based on a circular steady-state motion of a linear bicycle
vehicle model
 Considers multi-preview-point to improve the path-following
properties
10 [1] - Kiumars Jalali. Development of a Path-following and a Speed Control Driver Model
for an Electric Vehicle, SAE Technical paper, Paper Number: 2012-01-0250, 2012.

11.  Introduction  Vehicle Modeling  Driver Modeling  Simulations
 Neuromuscular System
 Hill Muscle Model
 A muscle can be modeled using
the following three elements:
• Contractile Element (CE)
=
• Parallel Element (PE) CE
• Series Element (SE) SE
PE
 The force generated by the CE can
be approximated by the following
curves when activation (ai) is
assumed unity.
11

12.  Introduction  Vehicle Modeling  Driver Modeling  Simulations
 Neuromuscular System
 Hill muscle model
ce
f

0.5 1 1.5
ce
f
1.5
1
0 v ce
-10 0 10
Shortening Lengthening
12

13.  Introduction  Vehicle Modeling  Driver Modeling  Simulations
 Neuromuscular System
 Musculoskeletal Driver Model Assumptions and Features
 One degree of freedom system
Fle
(Elbow joint is assumed fixed)
orx
 An agonist and antagonist pair Extensor
muscle at the shoulder
 Constant moment arm for each
muscle Fixed Joint
Revolute Joint
 The contractile elements have a
dominant effect on the steering task Muscle 1
 Activation dynamics in the CE1
neuromuscular system Vehicle
Inertia
Model
 Indeterminate system (two muscles, CE2
one degree of freedom)
θ t , θ t 
13 
Muscle 2

14.  Introduction  Vehicle Modeling  Driver Modeling  Simulations
 Example1: Sinusoidal Steering Input
Inverse Dynamics IK Specified
Shoulder
Activations SW
Kinematics
Motion
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15.  Introduction  Vehicle Modeling  Driver Modeling  Simulations
 Closed-Loop Simulation
 Forward Dynamic optimization
 Dynamic optimization
 Optimization over the whole range of simulation
 Can be used for time-dependant objectives and unknowns
 Static optimization
 Optimization at each instance to find the unknown activation signals
 Quicker optimization time
Actual Path
Vehicle
Steering Wheel
Self Alignment
Disturbance Dynamics
Torque
Angle
Desired Path Steering System
- MPPC1 NMS2 +
+ Steering Torque
Driver +
Desired Torque
Assist Torque
Steering Angle
Desired
Current
Activation Signals
Electric The assist
PID
Motor - characteristic
1 Optimization
15 2
MPPC: Multi-point Predictive Controller Measured
NMS: Neuromuscular System Current

16.  Introduction  Vehicle Modeling  Driver Modeling  Simulations
 Closed-Loop Simulation
 Example2: ISO Double Lane Change
• Vehicle velocity : 20 m/s
• With MapleSim model of vehicle, EPS, and driver
• Dynamic optimization to resolve muscle activations
Model predictive driver model (with and without EPS) for
Driver torque with and without EPS
ISO double lane change path
4 4
Desired Path Without EPS
3.5 Model Predictive Driver Model 3 With EPS
3 2
Lateral Displacement (m)
Driver Torque (N.m)
2.5 1
2 0
-1
1.5
-2
1
-3
0.5
-4
0
-5
16 -0.5
0 5 10 15 20
0 5 10 15 20
Time (s)
Time(s)

17.  Summary and Future Work
 Summary:
• A high-fidelity vehicle model including a column-type electric power
steering system is developed.
• A two-level controller for the neuromuscular driver model is
developed.
• An environment for simulating driver and vehicle interactions is
developed.
• A method to find muscle activation signals in inverse and forward
dynamics is developed.
 Future Work:
• Incorporate the driver’s sense of steering torque into the driver
model
• Include metabolic energy consumption into optimization cost
function
• Include muscle fatigue reduction into the EPS controller’s objective
• Study age, gender, and physical ability on driver’s performance
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18. Questions ?
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