Session 27 Mathias Lidberg

Heavy Vehicle Dynamics Model &
Path Control Algorithms
Morteza Hassanzadeh, Mathias Lidberg

Vehicle Dynamics Group
Chalmers University of Technology

Outline

• Introduction
• Use cases suitable for path control
• Heavy vehicle system dynamics in planar motion
• Path control algorithms
• Simulation results; rear-end collision avoidance
• Model verification; comparison with test data
• Summary

2013-01-29 Transportforum 2013 2

Introduction
interactIVe project overview

• Website: www.interactIVe-ip.eu

• Budget: EUR 30 Million
• EC funding: EUR 17 Million

• Duration: 42 months (January 2010 – June 2013)

• Coordinator: Aria Etemad,
Ford Research & Advanced Engineering Europe
• 10 Countries: Czech Republic, Finland, France, Germany,
Greece, Italy, Spain, Sweden, The Netherlands,
The UK


Introduction
Partners and project structure


Introduction
SP5: INCA

• Development of integrated collision avoidance and vehicle path control for
passenger cars and commercial vehicles.
• “Vehicle path control” module dynamically evaluates a collision free
trajectory in rapidly changing driving scenarios.
• 3 demonstrator vehicles:
• Ford Focus
• Volvo S60
• Volvo FH13

• INCA coordinator:

• INCA cooperators:


Introduction
Current presentation

• Development of integrated collision avoidance and vehicle path control for
commercial vehicles.
• 3 use cases are prioritized and the problem is narrowed down to path
planning, actuators configuration, and control algorithm design.
• A robust path and speed controller should be developed to fulfil the
requirements of all use cases.
• A simulation tool, that includes a heavy vehicle model, is needed to
investigate performance and robustness of various actuator configurations,
and the control algorithms.


Use cases suitable for path control
Definition and prioritization

• Use case: a description of specific sequence of interactions between the
driver and truck to achieve a specific goal.
• Use cases are defined by name, accident type, and descriptive narrative.
• Use case prioritization is based on:
• Accident statistics
• Use case complexity
• Prioritized use cases are:
• Rear-end collision avoidance (RECA)
• Two lane road, single lane change
• Run-off road prevention (RORP)
• On a straight road
• In a curve


Rear-end collision avoidance (RECA)

• Use case name: Rear-end collision avoidance (RECA)
• Use case ID: UC_01_504_v2

• Use case description:
Prevents rear-end collisions
by informing or warning the driver or
by intervening by automatic braking
and/or steering.

• Reference
• Level 1
TS_SP5_1 [Accident in queue (rear end)]
• Level 2
TS_SP5_1.1 [Rear end collision due to slowing vehicle in front]


Run-off-road prevention on a straight road (RORP)

• Use case name: Run-off-road prevention on a straight road (RORP)

Informs/warns the driver of an
impending lane departure and,
if needed, steers automatically
to avoid road departure.

• Reference:
• Level 1
TS_SP5_2 [Single truck accident (run-off road)]
• Level 2
TS_SP5_2.1 [Running-off on a straight road]


Run-off-road prevention in a curve (RORP)

• Use case name: Run-off-road prevention in a curve (RORP)

Informs/warns the driver of an
impending lane departure and,
if needed, steers automatically
to avoid road departure.

• Reference:
• Level 1
TS_SP5_2 [Single truck accident (run-off road)]
• Level 2
TS_SP5_2.2 [Running-off in a curve]


Heavy vehicle system dynamics in planar motion
The model

• A 4 DOF two-track model:
• Longitudinal ( X )
• Lateral ( Y )
• Yaw ( )
• Roll (  )

• A nonlinear tyre model
(Magic Tyre Formula):
• Transient force build-up
(relaxation length is
considered)
• Drop in adhesion
coefficient for increased
vertical load


Path control algorithms
Overview

• Path planning block initiates the reference path that satisfies some criteria.
• Feedforward input is calculated based on the reference path.
• Feedback controller provides corrections to compensate for errors due to
simplifications and uncertainties.
• General overview of the whole process:

Heavy vehicle
model
Feedforward
Use case Path planning +
controller
Feedback
controller


Critical path

Heavy vehicle
model
Feedforward
controller
Feedback
controller

• Critical path: the shortest
feasible escape path, that can Required longitudinal distance, d (for RECA)
3
be determined by iterative
5
procedure:
2 Y ( X )   ci X i
• Start with initial guess. i 0

• Checking criteria. Y [m] 1 Vehicle path with no intervention
Intervention point
• Increasing longitudinal Rejected path
Critical path
distance if needed. 0

• Increasing longitudinal
distance of the critical path is -1
Required longitudinal
distance, d (for RORP)
equivalent to adding safety 0 20 40 60

margin to the manoeuvre. X [m]


Feedforward control

Heavy vehicle
model
Feedforward
controller
Feedback
controller

• Feedforward control: steady state bicycle model is used to calculate
steering inputs:
le ay
 FF   K us
r g
where:
2 C
le  l  (1  r )
l Cf


Feedback control

Heavy vehicle
model
Feedforward
controller
Feedback
controller

• Lateral position (Y) PID control:

 FB  K p (Yref  Y )  Ki  (Yref  Y )d  K d (Yref  Y )
 

• Yaw angle PD control:

 FB  K p ( ref  )  K d ( ref  )
 
dYref d Y 
 ref  tan 1 ( )  ref  V
 V
dX dX 1  Y 2


Simulation results

Rear-end collision avoidance by steering: a single lane change manoeuvre.

Parameter settings:
Parameters Values
Friction, µ 0.65
Lateral distance 3m
HV Initial Velocity, v 80 km/h
LV Initial Velocity, vl 0 km/h
Longitudinal distance 56 m


Simulation results

Heavy vehicle position

Intervention
4
Reference
Lateral position control
2
Y [m]

Yaw control

0 Obstacle

-2
0 10 20 30 40 50 60 70 80 90 100
X [m]
Heading angle
10
Reference
Intervention

 [deg]

5 Yaw control

0

-5
0 10 20 30 40 50 60 70 80 90 100
X [m]
Heading angle rate
20
d /dt [deg/s]

Reference
Intervention

Yaw control
0

-20
0 10 20 30 40 50 60 70 80 90 100
X [m]


Simulation results

Lateral acceleration Steering wheel angle
3 200
Reference Reference
2 150 Lateral position control
Yaw control
Yaw control
1 100
ay [m/s2]

 [deg]
0 50

-1 0

-2 -50

-3 -100
0 20 40 60 80 100 0 20 40 60 80 100
X [m] X [m]
Lateral jerk Steering wheel angle rate
20
Reference 1500 Reference
15 Lateral position control Lateral position control
Yaw control 1000 Yaw control
10 d/dt [deg/s]
i [m/s3]

5 500

0 0
-5
-500
-10
0 20 40 60 80 100 0 20 40 60 80 100
X [m] X [m]


Simulation results

Torque on the steering actuator Torque rate on the steering actuator

15 Lateral position control Lateral position control
200
Yaw control Yaw control
10 150

dT/dx [Nm/s]
T [Nm]

5 100
50
0
0
-5
-50
-10
0 20 40 60 80 100 0 20 40 60 80 100
X [m] X [m]
Percentage of utilized adhesion; solid line style corresponds to
lateral position control, and dashed line style corresponds to yaw control.
100
First axle, Left
First axle, Right
80 Second axle, Left
Second axle, Right
Third axle, Left
60 Third axle, Right
[%]

40

20

0
0 10 20 30 40 50 60 70 80 90 100
X [m]


Simulation results

Path error
0.5
eY [m]

Yaw control
0

X=d
-0.5
10 20 30 40 50 60 70 80 90 100
X [m]
Yaw angle error
2
e [deg]

Yaw control
0

X=d
-2
10 20 30 40 50 60 70 80 90 100
X [m]
Yaw rate error
5
ed /dt [deg/s]

0 Yaw control

-5
X=d
-10
10 20 30 40 50 60 70 80 90 100
X [m]


Simulation results

It is also tested with discrete input with update rate of 10 Hz; the controller
demands new steering wheel angle every 0.1 second.
Path error
0.2
Continuous data
eY [m]
Discrete data
0

X=d
-0.2
10 20 30 40 50 60 70 80 90 100
X [m]
Yaw angle error
2
Continuous data
e [deg]

0 Discrete data

-2
X=d
-4
10 20 30 40 50 60 70 80 90 100
X [m]
Yaw rate error
10
ed /dt [deg/s]

Continuous data
Discrete data
0

X=d
-10
10 20 30 40 50 60 70 80 90 100
X [m]


Model verification
Comparison with test data

• A series of tests were performed in the handling area of the Hällered Proving
Ground.
• The steering input from test data is also used as input to the simulation in
order to validate the simulation model.
• The results presented here also show a sample performance of the controller.


Model verification

A single lane change
manoeuvre was performed with
50% safety margin for
longitudinal distance.

Actuator's steering angle demand
60
Parameter settings:
40
Parameters Values
20
Lateral distance 3m

 [deg]
0
HV Initial Velocity, v 80 km/h
LV Initial Velocity, vl 0 km/h -20
Feedforward
Longitudinal distance 73.5 m -40 Feedback
Total controller demand
Actuator response
-60
0 1 2 3 4 5 6 7
t [s]


Model verification

Truck position
5

4

3
Y [m]

2

1 5th order polynomial; reference
5th order polynomial; target
0 Simulation
Test data
-1
0 20 40 60 80 100 120 140
X [m]
Yaw rate Lateral acceleration
8 3

6
2
4
1
d /dt [deg/s]

2

ay [m/s ]
0 2 0

-2
-1
-4
Reference -2 Reference
-6 Simulation Simulation
Test data Test data
-8 -3
0 1 2 3 4 5 6 7 0 1 2 3 4 5 6 7
t [s] t [s]


Session 27 Mathias Lidberg

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