Description of comm. vehicle demonstration system

INFORMATION SOCIETY TECHNOLOGIES (IST) PROGRAMME

FP6 - IST - 2004 - 4 - 027006

D9: Description of commercial vehicle demonstration system Dissemination

PU = Public

Work package

WP6 – System Integration

Authors

Stefan Nord, VTEC, VTEC_SN

Due date

01.10.2008

Delivery date

03.11.2008

Status

Final

This document

FRICTION_Deliverable_D9.doc

Short description

This document contains a description of the commercial vehicle demonstration system.

Deliverable D9 Dissemination PU

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PART 0 - Preliminaries

Authors Editor Mr. Stefan Nord

VTEC

VTEC_SN

Contributors Mr. Stefan Nord Mr. Johan Casselgren Ms. Anna Heyden Dr. Matti Kutila Mr. Michael Koehler Mr. Ari Tuononen

VTEC VTEC VTEC VTT IBEO Helsinki University of Technology

VTEC_SN VTEC_JC VTEC_AH VTT_MK IBEO_MIK TKK_AT

Consortium participants: Technical Research Centre of Finland, VTT (FIN) Centro Ricerche FIAT S.C.p.A, CRF (ITA) IBEO Automobile Sensor GmbH, IBEO (D) Rheinisch-Westfaelische Technische Hochschule Aachen, ika (D) Magneti Marelli Sistemi Elettronici S.p.A, MM (ITA) Nokian tyres plc, NR (FIN) Pirelli Tyre S.p.A, PI (ITA) Siemens AG, SI (D) Helsinki University of Technology, TKK (FIN) Volvo Technology Corporation, VTEC (SWE)


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Revision history Version 0.0

Date 2008-07-24

0.1

2008-07-30

0.2

2008-08-14

0.3 0.4

2008-08-21 2008-09-08

0.5

2008-09-16

0.6 0.7 0.8 0.9 0.10 0.11

2008-09-17 2008-09-22 2008-09-25 2008-09-25 2008-09-26 2008-09-29

0.12

2008-10-24

0.13 0.14 0.15 0.16

2008-10-29 2008-10-30 2008-10-31 2008-11-03

1.0

2008-11-03

Description Added headings and some initial information Added general information about the selected commercial vehicle. Added information about the HMI. Added information and pictures about sensors Added details about COMPOSE CMbB Added details about Road eye and the encapsulation tubes Added more info to chapter 2 and added headers for VTT Cameras and IBEO Laser Scanner. Some minor changes in chapter 6 and 7. Added info about IMU The IcOR system description added Corrected some figures and figure texts. The IcOR system description updated Compression of figures and correction of Roadeye to Road eye Added description of the IBEO laserscanner (taken from D8) Spell check of document Minor corrections Completed Laserscanner desription Updated tyre sensor description with input from TKK_AT Version finalized

Author VTEC_SN VTEC_SN

VTEC_SN VTEC_SN VTEC_JC VTEC_SN

VTEC_SN VTEC_SN VTT_MK VTEC_SN VTT_MK VTEC_SN VTEC_SN VTEC_SN VTEC_SN IBEO_MIK VTEC_SN VTEC_SN


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Document structure The document is divided as follows: PART 0 – Preliminaries Contains meta information about the document and its contents. PART 1 – General Commercial Vehicle Description Contains a description of the Volvo FH12 460 Tractor with an overview of the truck, its performance and physical properties. PART 2 – Demonstration System Description Describes the commercial vehicle demonstration system. PART 3 – Safety Application Description Describes the selected safety application from the PReVENT project COMPOSE. PART 4 – Appendices Contains references.


Abbreviations and acronyms ABS ACC ADAS AEB ALA ASIC BAS CAN CAS CMbB COR CW EFF EMC ESP FOV HMI HW IMU IP IVSS LSB MSB N/A OEM PSD REY RFE RPU RWIS SAE SRIS STREP SUV SW TCS TFF TMC TYR VSC WP WT

Anti-lock Braking System Adaptive Cruise Control Advanced Driver Assistance System Automatic Emergency Braking Alasca laserscanner Application Specific Integrated Circuit Brake Assist Controller Area Network Collision Avoidance System Collision Mitigation by Braking Correvit Collision Warning Environmental Feature Fusion Electromagnetic Compatibility Electronic Stability Program Field Of View Human Machine Interface Hardware Inertial Measurement Unit Integrated Projects Intelligent Vehicle Safety Systems Least Significant Bit Most Significant Bit Not Applicable Original Equipment Manufacturer Position Sensitive Diode Road eye Road Friction Estimation Rapid Prototyping Unit Road Weather Information System Society of Automotive Engineers Slippery Road Information System Specific Targeted Research Project Sport Utility Vehicle Software Traction Control System Tyre Feature Fusion Traffic Message Channel Tyre Sensor secondary RPU Vehicle Stability Control Work Package Work Task

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Table of contents

PART 0 - PRELIMINARIES .............................................................................................................................2 AUTHORS ............................................................................................................................................................2 REVISION HISTORY .............................................................................................................................................3 DOCUMENT STRUCTURE ....................................................................................................................................4 ABBREVIATIONS AND ACRONYMS .....................................................................................................................5 TABLE OF CONTENTS .........................................................................................................................................6 PART 1 – GENERAL COMMERCIAL VEHICLE DESCRIPTION ........................................................7 1

COMMERCIAL VEHICLE DESCRIPTION ....................................................................................................7 1.1 Overview ........................................................................................................................................7 1.2 TEA2 – Truck Electronic System Architecture............................................................................8 1.3 Vehicle Based Sensors ..................................................................................................................9

PART 2 – DEMONSTRATION VEHICLE SYSTEM DESCRIPTION ..................................................11 2 3

DEMONSTRATOR VEHICLE NETWORK ARCHITECTURE .........................................................................11 ADDITIONAL SENSORS ...........................................................................................................................13 3.1 Environmental Sensor – Road eye .............................................................................................13 3.2 Environmental Sensor - IcOR.....................................................................................................17 3.3 Environmental Sensor – IBEO Laser Scanner..........................................................................19 3.4 IMU ..............................................................................................................................................22 3.5 Tyre Sensor..................................................................................................................................24 4 HMI HARDWARE ...................................................................................................................................37 5 FRICTION ESTIMATION SYSTEM ..........................................................................................................38

PART 2 – SAFETY APPLICATION DESCRIPTION................................................................................39 6 7 8

PREVENT APPLICATIONS WITHIN THE FH12 TRUCK ..........................................................................39 TRUCK DEMONSTRATOR SAFETY APPLICATION ..................................................................................39 HMI APPLICATION .................................................................................................................................42 8.1 Previous studies...........................................................................................................................42 8.2 Problems concerning friction value and HMI presentation.....................................................44 8.3 HMI simulation............................................................................................................................46 8.4 Other areas of use .......................................................................................................................47

PART 4 – APPENDICES..................................................................................................................................48 REFERENCES.....................................................................................................................................................48


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PART 1 – General Commercial Vehicle Description 1

Commercial Vehicle Description

This chapter describes the chosen commercial vehicle within the FRICTION project. 1.1

Overview

A Volvo FH12 460 4x2 High Tractor was chosen as commercial vehicle demonstrator for the FRICTION project. It is equipped with a 12 litre 460 hp diesel engine. It is 2600 mm in width and the wheel base is 3600 mm. The truck can be equipped with a ballast in order to achieve a more distributed load profile between the front and rear axle (see Figure 1 below). Without the ballast the load is approximately 5.5 tons on the front axle and 2 tons on the rear axle respectively. With ballast the load is approximately 6.3 tons on the front and the rear axle respectively.

Figure 1 The Commercial Vehicle Demonstrator equipped with ballast

Furthermore, within the project the truck has been equipped with new 315/70R22.5 tyres supplied by Pirelli. This specific truck has also been used for the integrated subprojects within PReVENT like e.g. SASPENCE, APALACI and COMPOSE. It has also been used as demonstrator for the FP6 project AIDE. This means that systems developed within those projects are available to be used in conjunction with FRICTION applications if needed.

Deliverable D9 Dissemination PU 1.2

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TEA2 – Truck Electronic System Architecture

The communication in the truck is run on two different networks that follow two different standards. The control network follows the SAE J1939 standard and the different commands to other units are sent on this network. This network is segmented in totally 5 different CAN busses. Diagnostics and information is sent on the other network according to the SAE J1708 standard. This link also gives a system redundancy and works as a back up in case of failure on the J1939. All vital control units are connected to this network.

Figure 2 The Volvo Truck Electronic System Architecture

Module TECU GECU ABS-B ABS-F EBS ESP VECU Tachograph EMS BBM IMMO

Description Transmission Electronic Control Unit Gearlever Electronic Control Unit Anti-lock Braking System Anti-lock Braking System Electronic Brake System Electronic Stabilizing Program Vehicle Electronic Control Unit Tachograph Engine Management System Body Builder Module Immobilizer

Deliverable D9 Dissemination PU Module SRS ECC MCC Dynafleet VCADS ECS Cluster Audio SWM Buttons in wheel Phone LCM RECU RAS

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Description Supplemental Restrain System Electronic Climate Control Manual Climate Control Dynafleet N/A Electronic Controlled Suspension Instrument Cluster Audio Steering Wheel Module Buttons in wheel Phone Light Control Module Retarder Electronic Control Unit Rear Axle Steerable

Table 1 Description of ECU:s in the Volvo Truck Electronic System Architecture

1.3

Vehicle Based Sensors

The truck is of course equipped with a big number of vehicle based sensors that could be of interest for the FRICTION algorithm. The most important vehicle sensor signals and their CAN format are outlined in Table 2 below.

initial min max offset transmitter value value value factor value value unit c-type ABS 0 0 251 0.00391 0 km/h uint16 RelSpdFrontLeft ABS 0 7.81257.8125 0.0625 7.8125km/h uint8 RelSpdFrontRight ABS 0 7.81257.8125 0.0625 7.8125km/h uint8 RelSpdRear1Left ABS 0 7.81257.8125 0.0625 7.8125km/h uint8 RelSpdRear1Right ABS 0 7.81257.8125 0.0625 7.8125km/h uint8 RelSpdRear2Left ABS 0 7.81257.8125 0.0625 7.8125km/h uint8 RelSpdRear2Right ABS 0 7.81257.8125 0.0625 7.8125km/h uint8 YawRate ESP 0 -3.92 3.92 0.00012207 -3.92 rad/s uint16 Yaw rate YawRate ABS 0 -3.92 3.92 0.00012207 -3.92 rad/s uint16 Lateral ESP 0 15.68716.31240.00048828 15.687m/s2 uint16 acceleration LateralAcc LateralAcc ABS 0 15.68715.687 0.00048828 15.687m/s2 uint16 sensor Wheel Speed

signal name FrontAxleSpd


sensor signal name Inclination Inclination Engine EngineTorqueMode torque

initial min max offset transmitter value value value factor value value unit c-type TECU 0 -25 25 0.2 -25 % uint8 Instrument Cluster 0 0 15 1 0 uint4

EngineTorqueMode EECU Steering angle

Brake Pressure

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SteeringWheelAngle ESP SteeringWheelAngle ABS BrkPressFrontLeft ABS BrkPressFrontRight ABS BrkPressRear1Left ABS BrkPressRear1Right ABS BrkPressRear2Left ABS BrkPressRear2Right ABS BrkPressRear3Left ABS BrkPressRear3Right ABS

0

0 15 1 0 0 31.374 31.374 0.000976563 31.374rad 0 31.374 31.374 0.000976563 31.374rad 0 0 12.5 0.05 0 bar 0 0 12.5 0.05 0 bar 0 0 12.5 0.05 0 bar 0 0 12.5 0.05 0 bar 0 0 12.5 0.05 0 bar 0 0 12.5 0.05 0 bar 0 0 12.5 0.05 0 bar 0 0 12.5 0.05 0 bar

Table 2 Truck Vehicle sensors – CAN format

uint4 uint16 uint16 uint8 uint8 uint8 uint8 uint8 uint8 uint8 uint8


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PART 2 – Demonstration Vehicle System Description 2

Demonstrator vehicle network architecture

The demonstrator vehicle network architecture is the network architecture used for experimental purposes (for projects other than FRICTION). Usually they are equipped with dedicated CAN buses in order to avoid interferences with normal vehicle functions. Also in this case messages acting on the added experimental CAN buses might be needed for the FRICTION algorithm operation. The truck demonstrator is a vehicle used for several experimental projects and therefore contains several dedicated experimental CAN buses and rapid prototype units. Figure 3 below outlines the already existing CAN buses and RPU:s. In addition to this a FRICTION Gateway RPU (xPC) was added as the processing platform for the VFF, EFF and TFF. Connected to the FRICTION Gateway RPU there is a dedicated CAN bus that contains the relevant sensors. The tyre sensor receiver ECU is connected to the secondary RPU (PDU), which calculates the tyre forces in real time.

Vehicle CAN J1939

SAFELANE

INSAFES

AIDE

FRICTION

Gateway (xPC)

Gateway (xPC)

Gateway / ICA (xPC)

Gateway RPU (xPC)

Lane Tracker

Maps&Adas

Decision

ACC Radar

Side Sensors (Lateral Safe)

Road eye

APALACI Radars

IMU

APALACI Image Processing

2nd RPU

Blind Spot Sensor (right)

Active Steering

Figure 3 Truck Demonstrator Experimental Electronic Architecture

Tyre Sensor Receiver

IcOR

Laser scanner


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Figure 4 below outlines the detailed connection scheme for the basic and additional sensors that was integrated on the demonstrator truck. The xPC has 4 different CANports denoted CAN1, CAN2, CAN3 and CAN4. CAN1 is connected to the Road eye sensor, the PDU and a Camera PC from VTT. CAN2 is connected to the J1939 vehicle CAN bus. CAN3 is connected to the IMU and the CAN4 is connected to the IBEO laser scanner. A laptop is also connected to the FRICTION xPC via a UDP link in order to log data and download changes in the Simulink implementation of the VFF and EFF running on the xPC.

Cameras

CAN3

IMU J1939

CAN2

xPC

CAN4

CAN1

Road eye

UDP

IcOR PC

Laptop PDU 1 Mbit/s CAN From tyre sensor receiver

Figure 4 Integration of additional sensors

Laser scanner


3

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Additional sensors

3.1

Environmental Sensor – Road eye

Different surfaces scatter and absorb electromagnetic radiation differently due to for example differences in surface structure and the scattering medium. Illumination by the same electromagnetic radiation the reflected response will alter for different surfaces which make a classification of the surface possible. This technique is implemented in the Road eye sensor (see Figure 5) for road condition classification ahead of vehicles.

Figure 5 Classification boundaries depicted in a 2-dimensional plane with the two wavelengths as axis and a picture of the Road eye sensor

3.1.1

Road eye

The environmental sensor Road eye is constructed of two laser diodes and a photo diode combined with focusing optics. The two laser diodes, of wavelength 1320 and 1570 nm, are used for illumination of the road. Lenses in front of the diodes making the illuminated spot on the asphalt about 1 cm in diameter at a distance of 1 m. The photo diode measuring the reflected intensities, the lens ensures measuring within the illuminated spots. The Road eye sensor output is an intensity measurement for each wavelength, respectively (λ1 and λ2). By plotting the measurements in a plane with the two wavelengths as axis each measurement can be represented by a magnitude and an argument as:


Mag =

λ1 2 + λ 2 2 ,

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λ  Arg = arctan  1   λ2 

The difference in both magnitude and argument for different road condition makes classification possible. In Figure 5 a classification is depicted for a measurement done on a test track with the four road conditions dry asphalt, water, ice and snow, each measurement depicted by a marker, the coloured polygons representing the classification boundaries for each road condition. Note that the water and ice marker are gather close together, hence the classification of water and ice is difficult. The measurement depicted in Figure 5 was done with an older version of the Road eye sensor which were suggested some improvements, to get a more valid classification of the surfaces water and ice. The improvement was carried out and tested within the FRICTION project. 3.1.2

Laser diodes

Laser diodes are available in a wide range of wavelengths the ones used in the Road eye is infrared wavelengths, this is due to the fact that within the infrared bandwidth water, ice and snow have distinguished absorptions spectra, i.e. the three surfaces absorbs the light differently making a contribution for classification. Due to the construction of laser diodes the light emitted from the semi conductors is linearly polarized i.e. the electromagnetic waves only fluctuate in one direction perpendicular to the direction of propagation. When electromagnetic radiation (light) is reflected against a surface the polarization of the incident wave is effected due to the structure and medium. Resent investigations show that to accomplish low probability of wrong classification between water and ice the state of polarization should be perpendicular (S-) to the incident angle, i.e. S-polarized. The first version of the Road eye sensor had the laser diodes mounted with one diode Spolarized and one parallel (P-) polarized, the updated version was updated so both diodes were S-polarized to improve the classification.

Deliverable D9 Dissemination PU 3.1.3

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Physical integration

Figure 6 Installation of the Road eye

The Road eye sensor has been tested in trucks in previous projects IVSS RFE, hence the mounting was easy. The sensor was mounted in the wheel house in front of the left wheel as seen in Figure 6 This placement ensured measuring 1 m in font of the left wheel. The mounting resulted in an illumination spot of around 0.01 m in diameter on the road for each laser diode, and the measuring spot of around 0.1 m. The length of the tube holding the sensor as depicted in Figure 6 was 0.35 m and the sensor was mounted about 0.6 m above the road. Investigations within the IVSS RFE project suggested two small alteration of the mounting of the Road eye to improve the classification, especially the classification of water and ice. These alterations were carried out within the Friction project. The first alteration was to change the position of the illumination source and the receiver in respect to each other, previously they hade been place parallel with each other but the results from the IVSS investigation showed that they should be placed with the illumination source above the receiver. Hence such a mounting would increase the optical path the lights propagate through the covering medium leading to the covering mediums scattering and absorption properties get more prominent on the reflected light, decreasing the probability of wrong classification. The second alteration was the angle of inclination which should be as small as possible according to IVSS investigations. Due to space limitation inside the truck see Figure 6 Installation of the Road eye and the risk of pollution of dirt on the sensor, which will be discussed in a following section, the inclination angle was changed from previous 60° to 45°. This was also done to increase the optical path as described above.


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Encapsulation of the sensor

The encapsulation of the sensor is a crucial factor, hence the sensor is a “seeing” sensor it is vulnerable for dirt. To get a good classification the sensor needs to be close to the road surface which exposes the sensor for a lot of water and other dirt splashing from the road on to the sensor.

Figure 7 The three different dummy tubes mounted on the tractors for the winter expedition to Arjeplog

As shown in Figure 6 the sensor was mounted in a straight cut tube, this mounting have work in previous projects and protected the sensor from water and dirt. For the FRICTION project an investigation of the tubes cutting was implemented. Three tubes two with the same straight cutting and one with a 45° slope of the cutting. One straight and the 45° tube where mounted at the opposite side on the tractor’s frame behind the compartment and the front tire, the third tube was placed behind the 45° cut tube. All tube where mounted with an angle of 45° to the ground. The mounting took place before the travel of 1500 km from Gothenburg to Arjeplog in order to investigate the pollution factor inside the tubes.


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Environmental Sensor - IcOR

The IcOR application is a camera system which includes two detectors, one for horizontal and the second for capturing the vertical polarisation reflection. The system also analyses graininess of the road surface in order to improve performance of distinguishing wet and icy road surfaces. The system provides as an output whether the road ahead is icy, wet, snowy or if the asphalt is dry.

ROAD SURFACE CLASSIFICATION 0,7 0,65

Graininess

0,6 Snow Wet/Slush Snow/Ice

0,55 0,5 0,45 0,4 0

2

4

6

8

10

12

Polarisation difference Figure 8 The classification results of the IcOR road surface monitoring application

The IcOR system bases on cameras and is therefore capable of analysing the road area as far as 50 - 100 m ahead of the vehicle. The system does not contain illumination equipment itself but it is rather optimised to operate close to the near infrared band which makes possible to utilise the vehicle’s head lamps while driving in dark conditions. 3.2.5

Equipment

The IcOR hardware consists of camera unit, polarization filters and CPU. There are two image detectors in the camera which has been bought from Videre Design LLC, U.S.A. and its model is STH-DGSG-6cm. The system is originally designed for low cost stereo vision applications but it is feasible solution for performing synchronized image capturing in automotive application since the design is compact and takes power directly via the Firewire cable. The camera cell is Micron MT9V022 which is particularly dedicated for automotive products.


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In addition to the camera hardware, polarization filters have been installed front of the optics. The filters transmittance is approx. 30% in visible spectrum 400 – 700 nm and their polarization efficiently is 95 %. The visible spectrum is here utilized since the system is expected work also during night time when the vehicle’s head lamps are turned on. The camera unit is connected with a Firewire cable to the computing unit. In the demonstration vehicle CPU is a laptop with the MS Windows XP –operating system. The IcOR software runs in the laptop and via the implemented CAN interface the road state information is transmitted to the in-vehicle computing unit. 3.2.6

Physical Integration

Figure 9 Installation examples. On left side the truck and on right side the passenger car installation

The IcOR system was installed behind of the windscreen since it offers water proof place for the camera and the vehicles own wipers maintain the optical path of the camera clear. The installation when prepared to the truck test runs in Arjeplog, Sweden on March 2008 was made to the dashboard of the vehicle. The image processing PC was installed to the rack where the other vehicle-PCs exist as well. In the truck implementation the first generation camera setup was used which was physically quite big. To the IKA’s Audi demonstration the second generation camera interface was elaborated and it enabled opportunity to install the camera side of the internal mirror where for example the lane trackers normally locate in passenger cars.


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Environmental Sensor – IBEO Laser Scanner

The Ibeo LUX Laser scanner was developed under the requirement to simultaneously support multiple ADAS applications like e.g. ACC Stop & Go, Automatic Emergency Braking, Pre-Crash, Pedestrian Protection. These applications require accurate detection and tracking of moving and static objects in the environment of vehicle. The acquisition of information about weather conditions is not a major requirement for those applications; instead they demand robustness against adverse weather conditions like rain, snow and fog. Within the FRICTION project the sensor embedded algorithms have been enhanced so that it is also possible to detect and classify measurements on precipitation related targets like raindrops or snowflakes.

Figure 10 shows a visualization of a laser scanner measurement recorded during snow conditions. Visualized are the raw measurements. Each dot represents the distance of a backscattered laser pulse. The snow density, respectively the amount of precipitation can be related to the number and the distribution of measurements classified as snow in a defined area of the laser scanners field of view. The classification of the single measurements is based on the signal characteristic of the backscattered signal and their spatial distribution: •

Measurements on snow and rain are only detectable in a certain measurement range of ~0.5 .. 12m.

•

Measurements on snow and rain are characterized by a significantly lower energy in the backscattered signal.

Figure 10 detected snow in Laser scanner measurements


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The following figure shows the output of the precipitation estimation in three different snow conditions.

Figure 11 Output of Laser scanner precipitation estimation

The following figure shows the detection, and classification of spray during a motorway test drive with spray measurements marked blue, object measurements are marked red.

Figure 12 Laser scanner spray detection and classification


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The main benefit of the laser scanner in the context of the FRICTION project is the capability of detecting precipitation – relevant for friction determination, and objects relevant for ADAS applications - simultaneously, and to distinguish between both types of measurements. 3.3.7


Figure 13 Physical integration of the IBEO Lux laser scanner on the truck


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3.4 IMU The Inertial Measurement Unit (IMU) is developed by Continental AG (former Siemens VDO) to provide key data for vehicle dynamics control systems. The IMU applied in the EU-project contains three Yaw Rate Clusters (YRC). The three same YRCs are mutually perpendicularly mounted in the IMU housing (see Figure 14 below). The YRC is designed by using MEMS (Mirco Electromechanical sensors) technology to provide an angular velocity and two accelerations. It indicates that the combined IMU can provide the three angular velocities and three accelerations of vehicle body, i.e., yaw, roll and pitch rates, and longitudinal, lateral and vertical accelerations. By using the IMU, the 3dimensional movement and 3-dimensional attitude of vehicle body can be precisely estimated. The integrated IMU in a single unit has been applied in ECU in 2007.

Figure 14 The IMU applied in the EU-project contains three Yaw Rate Clusters (YRC).


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Table 3 below outlines the IMU sensor signal definitions. Offset value

Unit

Size

Signal age

+31.984

Factor value (in units per digit) 0.015625

-32

m.s-²

-32

+31.984

0.015625

-32

m.s ²

0 0

-32 -128

+31.984 +127.938

0.015625 0.0625

-32 -128

m.s ² °.s-1

RollRate

0

-128

+127.938

0.0625

-128

°.s

YawRate

0

-128

+127.938

0.0625

-128

°.s

Sensor

Signal name

Initial value

Min value

Max value

longitudinal acceleration

ax

0

-32

lateral acceleration

ay

0

vertical acceleration pitch rate

az PitchRate

roll rate Yaw rate

12 bits

10 ms

-

12 bits

10 ms

-

12 bits 12 bits

10 ms 10 ms

-1

12 bits

10 ms

-1

12 bits

10 ms

Table 3 – IMU Sensor Signal Definitions

3.4.8


The IMU was pretty easy to install even though it could not be installed at the centre of gravity of the truck.

Figure 15 Installation of the IMU


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Tyre Sensor

Figure 16 below shows the truck tyre sensor system architecture with the antenna box mounted in close proximity of the front left wheel hub centre, the Tyre Sensor receiver ECU containing the receiver unit for the wireless link and the Secondary RPU (PDU) for real time calculations of tyre forces.

12V Tyre Sensor Receiver

Antenna box

1 Mb/s CAN

Secondary RPU (PDU)

12V

Figure 16 – Truck tyre sensor system architecture

3.5.1

Tyre sensor system

Tyre sensor system needs particular care due to the nature of data coming from it. The system is made up by a receiver unit for each wheel. The outputs are available on dedicated point-to-point high speed (1 Mbps) CAN Buses. Such buses are completely saturated and cannot be directly connected to the FRICTION Bus. For that reason a Secondary Rapid Prototyping Unit will be provided in order to pre-process signal and output synthetic tyre sensors data. The optical tyre sensor is well known from the APOLLO EU-project. An optical tyre deformation sensor was developed and tested in this EU founded project. This sensor measures the displacement of the tire contact patch relative to the rim. Set up and measuring principle is discussed shortly in the following. The Sensor consists of a PSD (Position Sensitive Device), a lens and a light source on the inner liner of the tyre. The diode is glued to the inner liner. The PSD chip and the lens are located in a housing directly on the rim.


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Z I3 UI

I2

I1

Y

PSD Linse

I4

Z IR-Diode

Y X ∆s

Figure 17 - Axial optical sensor on inner liner [source: APOLLO project]

As visible in Figure 17 the light beam, which is emitted by the IR-diode, is focused on the PSD chip. The centre of the light spot on the PSD lens is responsible for the 4 currents that can be measured at the edges of the PSD chip. Vertical deflection of the tyre causes a higher intensity of the light beam hitting the PSD. This results in an increasing overall current I1 + I2 + I3 + I4 with the same ratio at all corners of the chip. A movement of the light beam in lateral of longitudinal tyre direction changes the current at the edges with different ratios.

The relation between movement of the spot on the PSD chip and the current at the four borders can be described mathematically and can be used to get the final displacement. Appropriate tyre models can be used to calculate global tyre forces from the displacement signals. Each tyre sensor provides data on five channels: · Four currents (I1 to I4) from the PSD-chip (Position Sensitive Device) · Information on data transmission: defective data from PSD will be skipped As these signals represent pure raw data from the tyre sensor, a pre-processing is required In a first step the deflection (longitudinal, lateral and vertical) of the tyre belt in relation to the rim is calculated using the four currents I1 to I4 from the PSD-chip. Potentially it is possible to use the deflection signal of the tyre belt in order to gain information about the road surface state. In a second step the deflection data of the tyre belt is used to calculate the tyre forces acting in the tyre contact patch. The tyre force information can be used to very precisely observe the current state of vehicle motion. The exact knowledge of the tyre forces enables the possibility of a wheel individual friction used and in some cases friction available calculation.


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The data from the secondary RPU is read from the CAN-BUS (1Mbit/s) message 8 (length 8 bytes). The values are between 0 and 4095 (12 bits). The update rate is approximately 5200Hz (radio sends new message), but can be down sampled to ~3000 Hz if needed. Message Composition is shown in Table 4. All signals are unsigned integers.

Table 4 - Message Composition

3.5.2

ERROR-CORRECTION

If CRC-value is non-zero, there is a disturbance in radio transmission. In this case, the “If action”-subsystem keeps the existing value, until CRC is zero again. In the second phase in this subsystem, the signal values are compared to high and low parameters in order to evaluate if they are within reasonable range. If not, the previous value is kept. Note: there are two CRC:s, one from the CAN and another one from the radio. Only CRCinformation from the radio transmission is considered here, because radio transmission errors are more common than CAN-bus errors. 3.5.3

CONTACT RECOGNITION

The contact is not directly recognised. Instead the upright position of the sensor is detected. The inductive sensor (magnetic pick-up) is installed into the rim with same position as the optical sensor. Secondly, a magnet is installed into the suspension of the vehicle (or to the test rig). When the sensor passes the magnet, a voltage output can be measured. The offset is removed from the signal (Figure 18). The zero crossing point has to be detected, but only when the sensor is close to the magnet (excluding zero crossings induced by noise). This is realised with a relay-block in Simulink. When the certain threshold (“ind_on”) is exceeded, the relay is switch on and the zero crossing is activated. After the certain threshold (“ind_off”) the relay is switch off. When the relay is on and signal first time reaches zero or negative, the sensor is considered to be upright. Note: The pole of the magnet has influence for the signal. If the pole has changed, the signal goes down first and the up to zero crossing. (This was the case with truck rim in Aachen).


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The problem can be avoided by changing sign of the parameters „ind_on , „ind_off , and search for first positive value (instead of negative) when relay is on.

Figure 18 Contact recognition relay

If the sensor position is not properly recognised, none of the further outputs are real and the tyre force estimates are NOT reliable. 3.5.4

FORCE CALCULATION-BLOCK

3.5.4.1 ROTATIONAL VELOCITY-SUBSYSTEM

Rotational velocity is needed in force estimation algorithm. It can be also implemented to judge the plausibility of the contact recognition, because rotational velocity from tyre sensor can be compared to the ABS-sensor values. The time difference between two sequential “sensor up” peaks is calculated and rotational velocity follows as:

3.5.4.2 LONGITUDINAL FORCE

Several different algorithms capable for longitudinal force estimation were studied. Many of them were powerful in some simple conditions, but the following one performs quite well even with some disturbances. The behaviour of longitudinal signal is shown in Figure 19. The peak values change, but on the other hand the signal level is raised as well. Thus, the mean value of longitudinal signal for an independent rotation is calculated. Thus the longitudinal force can be calculated:


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In the Simulink-model the “mean (x)” is realised by calculating sum of all x-signal values and dividing that with the number of values (for one rotation).

Figure 19 Longitudinal signal when braking applied at 13s

3.5.4.3 LATERAL FORCE

The optical sensor measures the movement of the light spot on sensor surface, not the actual movement of the LED (Figure 20). During the contact, the LED moves closer to the sensor and the lateral position of the light spot (reflected from the lens) is moving, even though the LED stays still laterally (Figure 21).


Figure 20 - Lateral signal before compensation

Figure 21 - The influence of lateral LED position during contact

This phenomenon is removed from the signal in “Compensate y”-subsystem.

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and the area of deflection is:

where the influence of velocity is removed by multiplying with rotational velocity. Finally the equation force reads:

where an additional compensation term from xgap is found to be reasonable (explained in “vertical force”-chapter. Note: It seems that the “Compensate y”- subsystem is not necessary for the truck sensor, because of the greater distance of the LED to the sensor (in relation to lateral movement). 3.5.4.4 VERTICAL FORCE

The intensity signal is natural starting point for vertical load calculation and its performance is really good when no other forces exist at the same time. The longitudinal signal includes in-formation about the contact length, which correlates strongly with vertical load as well. It is proposed to be much more robust against the other disturbances than the intensity signal.

Figure 22 - Longitudinal signal behaviour on wheel loads 5000, 3000, 1000N

The vertical force is mainly calculated from gap between the minimum and maximum value (longitudinal signal) of one rotation:


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In practise, the integrator is reset when the sensor is up, and the following triggered subsystem holds the existing value until new estimate is available. The cz equals vertical force gain in the model and vertical force estimate reads:

The longitudinal signal is influenced by lateral and longitudinal movements as well and compensation terms are in parentheses. The yA and xmean are calculated in lateral and longitudinal force blocks. Note: Because maximum and minimum values are used for xgap, the noise has quite remarkable influence for the estimation and parameters as well. It is possible that some filtering would improve the performance of vertical force estimate.

3.5.5

Implementation for Tyre Force Calculations

This chapter gives an explanation on the implementation of the tyre force calculations performed in the Secondary RPU (PDU). 3.5.5.1 Hardware

The PDU features a Star12-processor called MC9S12DG128 with two CAN controllers, labelled CAN0 and CAN4. The processor features 128KB flash memory and 8KB.

Figure 23 – Secondary RPU (PDU) Hardware


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3.5.5.2 Software Tools

Development studio used was Codewarrior Development Studio. 3.5.5.3 The algorithm and its implementation

For the actual algorithm please refer to chapter 3.5.1. The algorithm was implemented using ANSI-C. The implementation uses the main function only for initialization and startup code. The rest is implemented using interrupts of different priorities. The important interrupts and functions used are: void interrupt 7 RTI(void) This interrupt has the highest priority of all the interrupts and is called approximately 976 times per second (976Hz). It’s used for measuring time via the counter1 global variable. The RTI interrupt is also used for outputting a CAN message once per second that reports the amount of CRC errors in the radio packets received from the tyre sensor. void interrupt 54 CAN4Rx(void) The CAN4 is set up to run at 1Mbps. This interrupt is called when a new CAN message has been received in the CAN4 controller. The CAN4Rx interrupt service routine reads the desired parts of the packet and then calls the TyreForceCalc(). Look further down for its description. void interrupt 39 CAN0Tx(void) The CAN0 is set up to run at 250Kbps. This interrupt is triggered everytime at least one of the three hardware slots in the CAN0 controller is empty and therefore ready for accepting a CAN message for transmission. Sending CAN messages is implemented using a circular buffer. The CAN0Tx service routine reads the CAN message data from the circular buffer at a position indicated by the CANQReadPos global variable, and writes the data to one of the free hardware slots of the CAN0 controller. It then marks the slot as full, which automatically starts transmission. If no CAN messages were available in the circular buffer, then transmission interrupts is turned off in the CAN0 controller to prevent further interrupts. This is later turned on again by the CAN0Transmit() function. int CAN0Transmit() This function is called to equeue a CAN message for transmission. The contents of the message are copied to a free slot in the circular buffer pointed to by the variable CANQWritePos, which is then increased to point to a new position. Before exiting the function enables transmission interrupts, so that the CAN0Tx interrupt gets called. If no free slot was available in the circular buffer the message is not copied and an error code is returned. void TyreForceCalc(unsigned char *data) This is the function that implements the actual Tyre Force Calculation algorithm. It is structured as a state machine and expects a new CAN message from the tyre sensor each time it is called. Note that TyreForceCalc() is “fall-through”, so it features no busywait loops anywhere. Since the tyre sensor sends up to 5200 messages per second, the


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total time available for the CAN4RX interrupt to execute, including calling the TyreForceCalc(), is limited to 1/5200 = 180us before the next CAN message is expected. This puts some great limits on the tyre force calculations in the last states of the state machine, but it is solved by “pipelining” (splitting up) the calculations in several states. In that way every state is guaranteed to execute within 180us. The algorithm uses a mixture of fixed point and floating point calculations. Since the Star12 processor doesn’t have native floating point instructions, these have to be simulated which takes a lot of time for the processor. Fixed point is used for all calculations during the earlier states of the state machine to make sure the processor gets it done within the 180us. During the final states where the friction forces are calculated floating point is used because both precision and ability to store large values is required. First the algorithm retrieves the required data from the CAN message according to Table 4 and stores it in the variables CRC, x, y, z and ind. Then the data is compared to certain limits. If any limit is exceeded or if the CRC is not zero, the data from the last CAN message is used instead. This is done every time the tyre force calculation algorithm is called regardless of the state it is in. The rest of the TyreForceCalc() consists of the actual state machine. The first state CALC_MEANIND is used for calculating a trustworthy mean value of the inductive voltage. After 5200 received CAN messages and with enough fluctuations in the inductive value the mean inductive value is considered useful. The mean value is required later to detect the peaks in the inductive voltage, which indicate that the tyre has rotated 360 degrees. The second state, FINDFIRSTPEAKS_RELAYOFF, looks for the first pair of voltage peaks to be received. We need this because most of the friction force calculations are done using data collected over an entire rotation of the tyre. When the inductive voltage reaches above or below a certain threshold the algorithm assumes that a pair of voltage peaks is coming. It’s possible to change whether the algorithm should look for a voltage increase or decrease by using the two defines called POSNEG and NEGPOS. Which one to use depends on how the magnet was mounted on the rim of the tyre. The third state, FINDFIRSTPEAKS_RELAYON, detects the transition from positive to negative (or vice versa) in the inductive voltage. This indicates that the tyre has rotated one complete turn and that from now on we should collect the necessary data for x, y, and z to do the tyre force calculations in the later states. The fourth state, RELAYOFF, is almost a copy of FINDFIRSTPEAKS_RELAYOFF, since it also looks for the inductive voltage to reach above or below the same threshold. But meanwhile it also performs some calculations like x_mean, x_min, x_max and y_mean on top of the same old inductive mean value calculation. The fifth state, RELAYON, is where all the action starts. Here the algorithm looks for the negative-to-positive transition (or vice versa) like in FINDFIRSTPEAKS_RELAYON. When this is found some of the previously gathered information, like x_mean, x_min, x_max and y_mean is used for calculating a few floating point values that are used in the later states.


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The three last states called RELAYON_pipe1, RELAYON_pipe2 and RELAYON_pipe3 is just a way to spread out the intensive tyre force calculations over time, because they use floating point. Since no new pair of peaks is expected in the next three CAN messages from the tyre sensor (this would require the truck to travel at super sonic speed), this should be a safe way to do it. 3.5.6


For the optical tyre sensor a new rim design needed to be developed because all previous optical tyres sensor systems had been adapted for car rim and tyres. One obstacle was that for truck rims it was not possible to use dividable rims as was the case for e.g. the APOLLO project. Another problem was the physical size of a truck tyre compared to a car tyre. In a truck tyre, the PSD will physically be at a grater distance from the LED, which will lead to a less optical power and a weaker signal. In order to overcome those hurdles it was decided to design a manufacture a new rim for the truck. By making a hole in the rim and weld a flange in to it, it was possible to make the installation of the LED through that hole, after the tyre was mounted in the rim. It also made it possible to make a sensor module in order to decrease the distance between the LED and the PSD. A special sensor module was designed to interface the rim .The sensor module can be quickly removed and reinstalled without special tools.

Figure 24 Sensor module installation to the test rim (1. rim, 2 flange joint welded to the rim, 3. sensor module 5. sealing O-ring)

The tyre is installed before the sensor module and the LED is glued into the inner liner of the tyre (Error! Reference source not found.). The LED is powered with wires from the sensor module. In addition to the sensor module, a magnetic pick-up sensor is installed on the inner edge of rim (Error! Reference source not found.). The magnet is installed on the suspension to indicate the upright position of the sensor. This enables very accurate information on the sensor rotation angle and the data is certainly on the same time axis as the actual tyre sensor data, due to same signal path.


Figure 25 Cross-section of optical tyre sensor for truck

Figure 26Optical tyre sensor components, magnet and magnetic pick-up sensor

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Figure 27 Making of the hole in the rim and flange welded in to the hole.

Figure 28 The sensor module mounted on the truck rim

Figure 29 The LED mounted on the inner liner and the lid mounted along with inductive sensor


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Additionally, the antenna box with the receiver also needed to be installed on the truck. This was a bit cumbersome because it was difficult to find the sweet spot, where a minimum number of CRC errors occurred regardless of the position of the wheel.

Figure 30 Installation of the antenna box

4

HMI Hardware

For the commercial vehicle demonstrator the dashboard of the existing vehicle is used for visualization.

Figure 31 The configurable instrument cluster in the truck demonstrator

Deliverable D9 Dissemination PU 5

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FRICTION estimation system

The FRICTION estimation system implemented in the Volvo FH12 is based on invehicle sensor together with one tyre-sensor, an IMU and an environmental sensor (Road eye). It executes in the FRICTION xPC gateway according to Figure 3. Information about the system is listed in the following table.

Input sensor

Corresponding preprocessed signal

Unit

steering wheel angle sensor lateral acceleration sensor Siemens IMU

SteeringWheelAngle

rad

LateralAcc

m.s-2

LateralAcc LongitudAcc VerticalAcc YawRate PitchRate RollRate YawRate WheelSpeed

m.s-2

yaw rate sensor wheel speed sensors (one for each wheel) brake pressure sensors (one for each wheel) tyre sensor environment sensor (Road eye)

Comment

rad/s

rad/s km/h

BrkPressinj

bar

-

-

See chapter 3.5 See chapter 3.1


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PART 2 – Safety Application Description 6

PReVENT Applications within the FH12 truck

As mentioned before a Volvo FH12 is used as a truck demonstrator. From production the truck is equipped with ABS, ESP and ACC systems. It also supports a number of applications developed in different PReVENT subprojects. These applications are: • • • • • •

Active Lane Keeping Support Collision Mitigation by Braking Start Inhibit Curve Speed Warning Lane Change Assistance All Around Warning

The truck has been equipped with a number of environmental sensors in order to support the applications mentioned above: • • • • • • • 7

1 long range forward looking radar 2 short range forward looking radars 3 short range side-looking radars 1 camera for the blind spot in front of the truck 1 camera for the blind spot to the right of the truck 1 lane tracker camera 1 laser scanner (225 deg FOV)

Truck Demonstrator Safety Application

The chosen safety application for demonstration of the benefits of having information about the road friction is the CMbB safety function, developed within the PReVENT subproject COMPOSE [COM08]. The COMPOSE project developed safety functions for the protection of road users and for mitigating the consequences of traffic accidents. The COMPOSE demonstrator truck of VTEC focussed on rear-end collisions in highway scenarios.

For the COMPOSE VTEC demonstrator platform, a laser scanner located at the front lower left corner is employed as perception system.


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Kamera

Radar

Laser Scanner Figure 32 VTEC Demonstrator with sensor locations. Red is COMPOSE-specific and green APALACI-specific.

The perception system provides the decision system with the objects detected ahead of the own vehicle. Based on this information, an assessment is performed whether a collision has become unavoidable. In this case, the demonstrator vehicle brakes fully automatically to reduce impact speed and thus crash energy. For this purpose, both the brake system as well as the engine is controlled electronically by the assessment unit. The demonstrator truck is also shared with other sub-projects within the IP PReVENT. For the FRICTION commercial vehicle demonstrator, it was not possible to use the laser scanner. Instead, the ACC radar was employed as perception system for the CMbB function.

Figure 33 FH12 ACC radar


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With friction information available, the CMbB braking distance can be calculated more precisely. Figure 34 below describes the relation between the braking distance and the friction coefficient.

Prediction of braking distance for stationary obstacle

Braking distance:

sB =

v² 2 ⋅ µx ⋅ g

Influence of max. friction available:

1

µ

Velocity [km/h]

v

250 200 150

Incline of 6%

100 50

Max. vehicle velocity (range of radar system: 160 m)

0 0,1

0,3

0,5

0,7

0,9

1,1

Max. friction available [ ]

Figure 34 Prediction of minimum braking distance

The braking distance (S) of a vehicle can be calculated by the velocity (v) and the maximum friction available (µ). Therefore besides the speed, the most influencing parameter on the braking distance is the maximum friction available (µ). For simplicity, the COMPOSE CMbB application was running on the same xPC as the friction estimation algorithm. The friction estimation application forwarded the friction information to a modified version of the CMbB application, that could recalculate the braking distance according to Figure 34 above.

Deliverable D9 Dissemination PU 8

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HMI Application

8.1 8.1.1

Previous studies Focus on slipperiness. Attitudes towards slippery road information systems (VTI-2008)

A group of 4 truck drivers and 4 bus drivers took part in a focus group in December 2007. Some of the questions asked were: How big is the problem with slippery roads, compared to other traffic problems and how difficult is it to drive on a slippery surface? A common view amongst the drivers is that the “black ice” (slippery asphalt) is the most annoying one. If there’s no ice or snow on the road you don’t slow down because of what you see. There are a lot of variations in the surface over a long stretch; therefore the grip can surprise you (this depends on the road administrations work which differs in different counties). The most unmanageable ice appears around 0 degrees Celsius. That is the most uncontrollable slipperiness. A slippery surface is perceived very differently depending on what vehicle you’re driving. Many of the drivers are also worried about driving downhill and the fact that they can cause a lot of damage if something happens. The loading of the truck is also very important. You have to even out the weight on all the tires to get good grip. How do you receive information about the road conditions today? The most common way for the drivers to receive information about the road conditions is by calling colleagues. They also use the information from the radio but feel that the coworker’s information is more precise and up to date. Many drivers know the temperature outside as a very good help, especially when the weather changes fast. They rely on the road temperature and make their own adjustments according to their knowledge and experience. What kind of demands would you have on a friction detection system, and how should the information be presented to the driver? Many drivers are afraid to be smothered with information and it can easily become annoying. When it comes to presentation they all prefer a big simple symbol, and maybe a voice message. Sometimes it is hard to read, that’s why a symbol is easier to interpret fast. It’s important that the system doesn’t lead do an increased risk taking.


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Would you trust a friction system? “A driver never trusts any systems” they say. They will never stop calling each other because that’s the most reliable information but a warning might be a good complement. 8.1.2

Effects of weather-controlled variable message signing on driver behaviour (VTT -2001)

The aim of the study was to investigate the effects of local and frequently updated information about adverse weather and road conditions on driver’s behaviour. The information was transmitted by several types of variable message signs (VMS). Operators manually controlled the signs. They classified the road surface conditions in three categories. Good, possibly slippery and verified slippery. One of the evaluations used road condition sign (shown below) that were turned off, in steady mode or in flashing mode together with other information like recommended headway depended on vehicle length, driving speed and road surface condition. Speed limit at the site in Finland was 80 km/h and the study was conducted during two winter periods.

8.1.2.1 Result

The system proved most effective when adverse weather and road conditions were not easy to detect. Most drivers accepted lowered speed limits and found variable speed limits useful. The flashing mode of the slippery road condition signs affected behaviour more than the steady mode. The effect was greater and lasted longer. 89 % of 114 interviewed drivers indicated that the sign had effect on their behaviour. In proportion to driving on “black ice”, which was 99 %. Drivers reported that slippery road condition sign influenced driving speed, particular in curves. Other effects where more frequent monitoring of oncoming vehicles than usual, concentrating more on one’s driving and monitoring road surface. The test was emphasized especially in black ice conditions. The slippery road condition sign and minimum headway sign decreased the mean speed of cars travelling in free flow traffic, by 1-2 km/h.


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The main human error leading to increased risk in winter is driver’s poor ability to recognize slipperiness and to adapt their behaviour to weather conditions. On slippery road surface, only 14 % of Finnish drivers estimate the road to be slippery and more than half considered the friction normal. The average speed on slippery road surface are about 4 km/h lower than in good winter conditions but that may not enough. 8.1.2.2 Decision making

One important fact to have in mind is that driving is not the primary task at all times. The drivers have several parallel goals while driving and perceiving slipperiness is difficult since there may be minimal visual cues to indicate the hazard. An operator revising a hypothesis is generally conservative and not extracting as much information as necessary. The concept of anchoring refers to difficulty for human operators in changing an initial hypothesis in line with subsequent sources of evidence, rather the opinion shifts only slightly. This makes it understandable why behavioural adaptation to slippery conditions is difficult for drivers. Another aspect is the speed. Drivers get used to the speed, and the decrease of speed feels subjectively greater than it objectively is. 8.2

Problems concerning friction value and HMI presentation

There are a few issues that affect the outcome of the HMI. First of all, the friction value will not be available at all, most of the time. This is probably the biggest issue to address. One way of solving this is to separate the Road Eye information and the information from the other sensors. Since the driver have to accelerate or brake for the sensors to register something you can drive on ice, without any sensors detecting this, apart from the Road Eye (at least if you drive from asphalt to ice). The Road Eye may recognize differences in the surface, but that doesn’t mean it’s slippery. Maybe the Road Eye should be used as a validation of values from the other sensors. To visualize this, the HMI could consist of two icons (see below). The “Information icon” (info from Road Eye) should indicate that “if you make any sudden moves, brake or accelerate the vehicle might slip” and the “Warning icon” (from other sensors) indicates that “You are slipping”.

Figure 35 ISO symbols


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There are different kinds of icons available. The ISO standard is shown (Figure 35) in the two modes, information and warning. Another is the one shown on traffic signs (Figure 36). The warning triangle is a well known indicator of danger which is preferable in a stressed situation when the driver just glimpse shortly on the dashboard. Also here, the use of different levels of warning is shown. The three (or two) steps gives the driver a good reference of the warning level which makes it easier to rate its graveness in a short amount of time.

Figure 36 Traffic signs

As a complement to the warning triangle, one could use an exclamation point for the informative warning (Figure 37).

Figure 37 Informative with exclamation

A more illustrative way to warn a driver is tire mark behind the truck icon (Figure 38).


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Figure 38 Tire marks

The Road eye detects a bit further ahead on the road than the other sensors. What Road eye sees can, by the vehicles sensors, later be confirmed when the tires reach the same area. If this confirmation is made, a warning appears. Then the HMI continue to warn as long as these two parameters correlate. But then again, if you drive on a snowy road the Road eye will detect something all the time, and a “Information icon” might loose its significance after a while and make the driver inattentive to variations in that value. To avoid this, a sound may be a good complement to an icon shifting from information to warning. But if this is the case every two minutes it gets quite annoying. 8.3 HMI simulation The simulation of HMI for test at Hällered test site includes two icons. An informative (orange-yellow) and a warning (red with triangle).

Figure 39 Final version


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Concerns

First and foremost these icons need to be tested by users to verify their appearance. Since the icons are shown in the display behind the steering wheel and is quite small, a sound could be a good complement to catch the drivers attention even more. A driver’s sight in daylight is better than the Road eye, so an “Information icon” may be unnecessary. At night, on the other hand, it can be a useful complement. Maybe there should there be a Day- and a Night Mode.

8.4

Other areas of use

The value of friction might also be interesting for the road administration. If they where presented the value as well as the position of the vehicle they might provide a more efficient and sufficient maintenance of the roads. They could also have an economic gain due to the more precise information that the value gives, unlike the information they rely on today in ways of predicting weather forecasts and act on experience. On the other hand the road administration needs information in advance to apply the right amount of work. The information from friction could instead be a measure on how well they have addressed the problems. If they get indications that a newly salted road is slippery, something is wrong. All efforts can be evaluated afterwards. The information could also be presented to drivers around the area, or people in general. Those who are planning a day on the roads might consider their route or speed if they knew someone before them have detected a significantly low value of friction. A problem though is that the road friction value for one vehicle is not directly corresponding to the road friction value of another vehicle which means that the information needs to be translated in to some sort of road condition information in a format that could be useful for other vehicles.


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PART 4 – Appendices

References [KIR08]

Kircher, K. et al (2008). Focus on slipperiness. Attitudes towards slippery road information systems (VTI notat 8- 2008)

[RÄM01] Räme, P (2001). Effects of weather-controlled variable message signing on driver behavior (VTT Publications). [KLI95]

Klinkner, W.: Electronic Stability Program: The new Active Safety System of Mercedes-Benz. EuroMotor: Vehicle-Vehicle and Vehicle-Roadside Interaction, Institut für Kraftfahrwesen Aachen (ika), Aachen, 1995.

[PAR00]

Park, K.; Heo, S.-J.: Design of a Control Logic for Improving Vehicle Dynamic Stability. 5th Intern. Symp. on Advanced Vehicle Control (AVEC), Ann Arbor, USA, 2000.

[PAU98]

Paul, J.: ESP – Elektronisches Stabilitäts-Programm – Ein aktives Fahrsicherheitssystem für einachsig- und allradgetriebene MercedesFahrzeuge. Vekehrsunfall und Fahrzeugtechnik, Heft 4, 1998.

[SFE01]

Sferco, R.; Page, Y.; le Coz, J.-Y.; Fay, P. A.: Potential Effectiveness of Electronic Stability Program (ESP) – What European Field Studies tell us. 17th Intern. Techn. Conf. on Enhanced Safety of Vehicles (ESV), Amsterdam, Niederlande, 2001.

[COM08] D51.82 COMPOSE deliverable: “Functional samples of the safety applications”

Description of comm. vehicle demonstration system

Description of comm. vehicle demonstration system

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