10th International Workshop on Research and Education in MechatronicsSeptember 10th-11th 2009, Glasgow, UK

RANGE DATA IN VEHICLE DYNAMIC SIMULATION

Hakan Fredriksson1, Mikael Nybacka2, Kalevi Hyyppa1

Lulea University of Technology1Department of Computer Science and Electrical Engineering

2Division of Functional Product DevelopmentLulea, Sweden

Abstract: This paper presents a way to merge range data into the vehicle dynamic simulation software CarSim7.1. The range data consists of measurements describing the surface of a road, and thus, creates a close to reallife 3D simulation environment. This reduces the discrepancy between the real life tests and simulation ofvehicle suspension systems, dampers, springs, etc. It is important for the vehicle industry to represent a reallife environment in the simulation software in order to increase the validity of the simulations and to studythe effects that uneven roads have on the systems. Furthermore, a 3D environment based on real life data isalso useful in driving simulators, when for example, analysing driver behaviour, testing driver response, andtraining for various driving conditions. To measure and collect data, a car was equipped with instruments anda computer. On top of the car, a SICK LMS200 2D lidar was mounted tilted downwards, facing the roadin front of the car. To create the 3D environment, all the individual measurements were transformed to aglobal coordinate system using the pose (position and orientation) information from a high-class navigationsystem. The pose information made it possible to compensate for the vehicle motion during data collection.The navigation system consisted of a GPS/IMU system from NovAtel. To reach high navigation performance,the raw GPS/IMU data were post-processed and fused with data from three different fixed GPS base stations.The range data were modified with a Matlab script in order to parse the data into a file that could be read byCarSim software. This created the 3D road used in the vehicle dynamic simulations. The measurements werecollected at a go-kart track in Lulea, Sweden. Finally, tests have been performed to compare simulation resultsbetween using a 2D surface (i.e. flat) and a 3D surface (close to real life). It is seen that the simulation resultsusing the 2D surface is clearly different from the 3D surface simulation.

Key words: laser, range measurement, lidar, vehicle dynamics, simulation

Figure 1: CarSim simulation environment based ondata from a real go-kart track.

I INTRODUCTION

Due to the increasing complexity of embedded sys-tems and software in vehicles, the automotive indus-try faces an increasing need for testing and verificationof components and subsystems under realistic condi-tions. At the same time, development cycles must beshortened for vehicle manufacturers to be competitiveon the global market, and a increased amount of test-

ing and verification must thus be performed in lesstime. However, simply increasing the testing volumecan be prohibitively costly, meaning that testing andverification processes must be made more efficient toreduce the need for more prototypes. Testing and ver-ification in real life conditions have to be supported bymore simulations in the front end of the developmentprocess. An example of such a simulation environ-ment is shown in Figure 1.

A. Automotive industry

In some simulation cases like Software-In-the-Loop(SIL) or Hardware-In-the-Loop (HIL), the use of asimulation environment that is as close to real life aspossible is of importance. Having a simulation en-vironment that is close to reality makes it possible tostudy the components behaviour on surfaces that is un-even. Studies that are of interest to run on uneven sur-faces in vehicle dynamic software is for instance activedamping influence on stopping distance and handling.

The use of driving simulators in the automotive in-

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dustry is more and more frequent. The driving sim-ulators make it possible to study the driver vehicleinteraction, active safety systems, system evaluation,etc. One interesting automotive driving simulator isthe one at Toyota Motor Corporation Higashifuji Tech-nical Center [3]. Also for these simulators it is impor-tant to use a simulation environment that is as close toreal life as possible.

B. Service providers

Test entrepreneurs and service providers that own ormanage test tracks are increasing their technical ser-vices to the vehicle manufacturer and OEM suppli-ers of the automotive industry. This is done by of-fering engineering and management related services,for instance by providing measurement equipment andweather data. The service providers knowledge of theirtest tracks would be of great help to automotive com-panies in order to prepare for the upcoming winter orsummer test. Service providers also need to know howtheir test tracks changed during winter freezing andspring thawing to keep a high quality and repeatabil-ity of their tracks.

C. CarSim

CarSim [1] is a software for simulation of vehicle dy-namic properties. Many automotive companies useCarSim for SIL, HIL and driving simulators. The soft-ware is used to simulate the dynamic behaviour of racecars, passenger cars, light trucks and utility vehicles.CarSim uses math models that are parametric and in-clude measured properties like kinematics and com-pliance test data etc. The math models are chosen insuch a way that the software can simulate full vehiclesfaster than real-time.

D. Range data in vehicle simulation

This paper presents a way to scan a road and mergethe captured range data into the vehicle dynamic sim-ulation software CarSim 7.1. The range data as usedin this paper consists of measurements describing thesurface of a road, and thus, create a close to real life3D simulation environment. This reduces the discrep-ancy between the real life tests and simulation of ve-hicle suspension systems, dampers, springs, etc. Asimilar approach to create 3D track models for usein vehicle simulations is presented in [4]. By using adifferent type of range measuring sensor they achievehigher resolution in their measurements. However, theaccuracy of their measurements, described in absolutecoordinates (i.e. GPS coordinates) is still, as well as inour work, mainly dependent on the GPS/IMU equip-ment. The focus of our work is on describing the over-

all large transverse and longitudinal unevenness of theroad. Shown in the result section is the difference be-tween running simulation on a flat road compared to aroad with elevation data.

II APPROACH

This section describes the setup of the measurementequipment for the range data acquisition, the conver-sion of the data to a readable format for CarSim, andthe set up of the simulations in CarSim.

A. Measurement setup

The measurement system was mounted on the roof-rack on top of a car, see Figure 2. In addition toa computer, power supply, and some other necessaryelectronics, the main parts of the system consisted ofa range measuring laser and a GPS/IMU system.

Figure 2: Car with measurement equipment on theroof-rack.

The range measuring laser used is a Sick LMS200.It measure distances to surrounding objects in a sec-tor of 180 degree with an angle increment of 1 degree.The range span is 0−80m with a resolution of 0.01m.A complete scan with this setup consist of 181 mea-surements. The equipment used allowed a scan fre-quency of 5Hz. The laser scanner was mounted facingthe road in front of the vehicle with a tilt/pitch angleof approximately 18o.

The GPS/IMU system used is a NovAtel SPANsystem [2], consisting of a GPS receiver1 and an IMUpackage2.

B. Managing data

GPS and IMU data from the SPAN system where post-processed using Waypoint Inertial Explorer softwarefrom NovAtel [2]. From this software the output pose(position and orientation) is expressed for the IMUpackage. The IMU where mounted on the measure-

1NovAtel GPS ProPak-G2plus2NovAtel IMU HG1700-AG58

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ment vehicle with the X axis pointing in the right di-rection, the Y axis in the forward driving direction,and the Z axis pointing upwards. To increase the ac-curacy of the navigation solution the collected datawhere fused with GPS data from three different ref-erence base stations. With this setup the estimatedstandard deviation of the IMU position, expressed ina global coordinate system, was less than 10cm in alldirections (X, Y, and Z). The estimated standard devi-ation of the heading, roll and pitch angles of the IMUwhere less than 0.03o.

The range measurements from the laser where ro-tated and translated, using a standard rotation matrix[5], from the laser coordinate system to the IMU co-ordinate system using the fixed mounting parameters.They where then rotated and translated further to aglobal coordinate system using the rotation matrix spec-ified by the Waypoint software combined with the posedata from the GPS/IMU system. Figure 3 show trans-lated measurement data from the go-kart track.

Figure 3: 3D range data with colour coded elevationinformation from a go-kart track in Lulea, Sweden.The numbers 1-9 marks all the curves on the track.The range data is collected while driving three lapsaround the track with the measurement system de-scribed in this paper.

To import the range data into CarSim a .par filecontaining the parameters of the road and other simu-lation properties has to be created. The road in the .parfile is described by three tables, one for the x-y hori-zontal data, one for the centre-line elevation data andone for the off-centre elevation data as seen in Figure4. Further explanation of these is found in section IIC.

The X and Y coordinates of the centre line of theroad was for this paper estimated by the driver of themeasurement vehicle by manually driving the vehiclearound the go-kart track as close to the centre as possi-

Figure 4: View in CarSim when importing the .par filecontaining the parameters describing the road.

ble. The left and right sides of the road where set to beat fixed distance from the centre in the measurementvehicle coordinate system, see Figure 5. The fixeddistance was chosen to represent the wheel base of theFormulae SAE vehicle used in the simulations.

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Figure 5: A close up view of a part of curve 7, shownin Figure 3. The red, blue, and black lines representthe right, centre, and left sides of the road, as importedto CarSim. The circles show how the data that repre-sent the elevation of the road in CarSim is selected.

The elevation information for every left, right, andcentre position of the road was found by calculatingthe average elevation of the measurement points clos-est to the specific position. This was done by search-ing for measurements that has X and Y coordinatesthat lies within a circle with a certain radius from thedesired position, see Figure 5. To reduce the influence

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of noise in the range measurements, and also to makesure that range measurements can be found, the circlehas to be adequately large. This is solved by using anadaptive search algorithm that increases the size of thecircle until a sufficient amount of measurements arefound within the circle. To further remove some ofthe noise in the elevation data a moving average filterwhere used on the elevation data on the three lines.

C. Simulation setup

For the simulations in the vehicle dynamic softwareCarSim we used a model of a Formula SAE formulacar built at Lulea University of Technology. This vehi-cle model has individual a-arm suspension with pull-rod set-up and Pacejka tyres, see Figure 6. The vehiclehas been tested on the Kallax go-kart track extensivelyand logged data can be used in simulations for a num-ber of different runs.

Figure 6: Setup of the FSAE vehicle in CarSim.

The 3D track of the scanned Kallax go-kart trackis built in CarSim by importing the created .par filedescribed in section II.B. The .par file holds the differ-ent tables for x-y horizontal, centre-line elevation andoff-centre elevation data.

Figure 4 show the imported .par file as seen inCarSim. The ”Centerline geometry” table holds thex-y horizontal data and distance along the path. Thetable ”Centerline elevation” holds two columns, dis-tance along the path and elevation. Table ”Off-centerelevation” holds three columns, distance along the path,left change in elevation from the centre line and rightchange in elevation from the centre line. The top rowof this table define the lateral distance from the cen-tre line for each column, e.g. you can divide the roadprofile into several segments. One could also spec-ify the friction as a table with distance along the pathand lateral values in the same manner as for off-centreelevation. However, in these simulations a constantfriction coefficient of 0.7 is used for the whole track.Rolling resistance coefficient is set to 1. For the ani-

mator in CarSim definitions of road shapes are needed,here one can use some standard values or create theirown.

To make the simulations as realistic as possiblelogged speed data were used in the simulations of theFormula SAE vehicle. The CarSim function ”Targetspeed vs. time” with the proportional gain 0.141 andintegral gain of 0.157 were used for this purpose. Tosteer the vehicle the function ”Driver path follower”were used in CarSim with a preview of one secondand no lag. For the simulation a time step of 0.002were used and the model were exported to Simulinkto save and compare data in Matlab environment.

III RESULTS

A. Range data

One interesting part of the go-kart track is curve num-ber 5. It is a highly banked curve, i.e. a velodrome.The tilt angle in the velodrome is measured to approx-imately 6o with an angle gauge. The measured anglecorresponds to the laser measurements as seen in Fig-ure 7. In the figure it can also be noticed that the anglevaries slightly along the curve.

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Figure 7: The upper plot show the elevation data forthe centre path of the track. The lower plot show thetilt angle of the road. The numbers in the figure markwhere the curves on the go-kart track are.

B. Simulation

Two roads where created in CarSim in order to vi-sualise the difference between using range data or aflat road. Both use the same x-y data and in the flatroad a constant of 0 is used for the elevation. Thelargest difference between simulations on flat and un-even road can be seen in the circles in Figure 8 and

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9. Figure 8 show the roll angle of the vehicle and

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Figure 8: Simulated roll angle of the FSAE vehicle, ona flat surface (Red), and on a uneven surface (Blue).

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Figure 9: Simulated pitch angle of the FSAE vehicle,on a flat surface (Red), and on a uneven surface (Blue).

here we can see two main differences. First discrep-ancy is the somewhat constant angle difference beforeturn 5 which can be explained by a tilt angle of theroad before turn 5. Secondly there is a spike inside thecircle which is created when the vehicle travels overa diagonal bump, seen also in Figure 3 between turn4 and 5. Figure 9 shows the pitch angle of the vehi-cle and the biggest discrepancy can be seen in turn 5.An explanation for this is that the vehicle is drivingdownhill when simulating the uneven road, thus thedriver model will release the throttle and an increasein negative x-acceleration and pitch will be induced.

For reference, Figure 10 show the logged y-accele-ration and yaw from Formula SAE car compared withthe simulated data. The simulated and real data arevery close. What can be noted though, is that the real

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Figure 10: Simulated Y-acc and Yaw data, comparedwith real measurement data from the Formula SAEcar. The black line represent real measurement dataand red represent simulated data.

driver did not negotiate the turns in the same way asthe driver model of the Formula SAE car. This can beseen especially in the sharp turns of 3 and 4.

IV CONCLUSION

Presented in this paper is a method of using a SICKrange measuring laser scanner and a GPS/IMU nav-igation system, to capture the roads topography andimporting it to a vehicle dynamic software CarSim.Also presented are comparisons and discussions be-tween running simulations on a flat and uneven trackwhich show a number of areas of discrepancy. Usingthis methodology of capturing range data can be ofhelp to the automotive industry when wanting a spe-cific test track or public road scanned for simulationpurposes in vehicle dynamic software.

ACKNOWLEDGEMENT

Funding from the Center for Automotive System Tech-nologies and Testing through Norrbottens Forsknings-rad and Lulea University of Technology, as well asthe European Regional Development Fund, throughTillvaxtverket and project I2, is greatly acknowledged.The help from Dr. Sven Ronnback and Lulea go-kartclub is also deeply acknowledged.

REFERENCES

[1] CarSim. http://www.carsim.com, May 2009.

[2] NovAtel. http://www.novatel.com, May 2009.

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[3] Takahiko Murano, Takashi Yonekawa, MasamiAga, and Sueharu Nagiri. Development of high-performance driving simulator. In SAE 2009World Congress, April 2009.

[4] Bernhard Schick, Steffen Witschass, and DavidLegrand. 3d-track - give the simulation the chancefor a better work! In SAE Technical publication,2006.

[5] Eric w. Weisstein. Rotation matrix. Math-World - A Wolfram Web Resource, May 2009.http://mathworld.wolfram.com/RotationMatrix.html.