The development of motorcycle adaptive rearview system using grey

THE DEVELOPMENT OF MOTORCYCLE ADAPTIVE REARVIEW SYSTEM
USING GREY PREDICTION
Yaojung Shiao, Quang-Anh Nguyen and Chun-Fang Hou
National Taipei University of Technology, Taipei, Taiwan
E-mail: [email protected]
ICETI 2012-H1006_SCI
No. 13-CSME-36, E.I.C. Accession 3494
ABSTRACT
The conventional rearview mirrors in a motorcycle are usually fixed, which is easy to cause instant rear
blind area for driver on turns or hubbly roads. This study proposes an adaptive rearview mirror system
using grey prediction for motorcycle. It can provide up-to-date information and ease to be embedded.
According to sensor signals, this system adaptively adjusts the rearview mirrors to compensate the changes
of vehicle dynamics in pitching, steering and rolling under different road conditions. A grey predictor was
also developed to estimate the change of vehicle dynamics in advance. This system can provide wide and
appropriate rear views to the driver to enhance driver’s safety.
Keywords: adaptive rear-view mirror; grey prediction; motorcycle dynamics.
LE DÉVELOPPEMENT D’UN SYSTÈME DE RÉTROVISEUR ADAPTIF
UTILISANT LA PRÉDICTION GRISE
RÉSUMÉ
Les rétroviseurs de motocyclette conventionnels sont habituellement fixes, ce qui peut provoquer un instant
de zone non visible dans les virages ou sur les routes cahoteuses. Cette étude propose un système de rétroviseur adaptif utilisant la prédiction grise. Il peut fournir des renseignements pertinents et aider l’intégration
à la circulation. Selon les signaux émis par les capteurs, ce système ajuste le rétroviseur pour compenser les changements dynamiques du véhicule, le tangage, la direction et le roulement, selon les conditions
de la route. Un système de prédiction grise a aussi été développé pour évaluer le changement anticipé de
dynamique. Ce système peut procurer une vue large et appropriée pour augmenter la sécurité du conducteur.
Mots-clés : rétroviseur adaptif ; prédiction grise ; dynamique d’une motocyclette.
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Table 1. BikeSim vehicle parameters.
1. INTRODUCTION
In daily usage of motorcycle, insufficient rear traffic information via conventional fixed rear-view mirrors
is a major threat for driver safety. By the conventional system, blind spots of driver vision not only existed
on curved roads but also happened on straight road. Moreover, limited driver eyesight prevents driver from
observing and recognizing objects clearly at high motorcycle speed. It is necessary to develop an adaptive
rearview mirror system and proper actuation for motorcycle to avoid any blind area behind.
This article developed a suitable way for intelligent rear-view mirror systems to improve the shortcomings of viewing angle on traditional system while maintain its basic structure and require lower cost for
modification. In developing actuation for an adaptive rearview system, motorcycle dynamics was simulated
and analyzed. Besides, a grey predictor was also added into the actuation to provide estimated motorcycle
dynamics.
2. MOTORCYCLE DYNAMICS
This paper applied motorcycle dynamic simulation software to estimate the relations among riders, motorcycle and road conditions. Matlab/Simulink was used to control complex models combined to BikeSim.
That offered benefits in effective dynamic simulation and analysis for different motorcycle designs and road
conditions. Table 1 shows parameters of the motorcycle used in simulation. The vehicle weight is 165 kg,
while the driver model is also selected as general people with average weight of 70 kg.
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Table 2. Simulated speeds and cornering radius.
General road design specifications
Velocity (km/h)
30 40
50
60
70
80
Turning radius (m) 80 110 140 170 200 220
Speeds and cornering radius for simulation
Velocity (km/h)
30, 40, 50, 60, 70, 80, 90, 100
Turning radius (m) 20, 30, 40, 50, 60, 70, 80, 90, 100, 120, 140, 160, 180,
200, 220, 240, 260, 280, 300
Fig. 1. Illustration of steer angle and roll angle of motorcycle.
Fig. 2. Motorcycle steering angle simulation results.
2.1. Motorcycle Dynamics in Cornering
When a motorcycle makes cornering, it also causes changes in yaw and roll to motorcycle rearview mirrors.
Theoretical models for these changes can be directly derived from motorcycle dynamics by the vehicle
motion analysis proposed by Nakano et al. [1]. However, this method does not take into account driver
models and weight shift. Thus, the simulated results cannot fit actual motorcycle dynamics accurately.
To make the simulation results more realistic, this paper used motorcycle dynamic simulation software
(BikeSim) to do the simulation of motorcycle when approaching and turning changed the steer and roll
angle as seen in Fig. 1, and to explore how that changes affect rear-view mirror visual. zone. According to
the simulated results, the changes of yaw, pitch and roll to rearview mirrors can be realized. Besides, the
simulated results and 3D virtual animation are very helpful in understanding the effective rearview zone to
motorcycle rider. In the simulation, the road curvature radius and motorcycle speed were considered as two
variables. Table 2 showed the general road design specifications about road curvature and turning speed
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Fig. 3. Road curvature vs steering angle simulation results.
Fig. 4. Motorcycle roll angle simulation results.
[2]. However, to acquire more realistic road conditions, several motorcycle speeds and road curvature radius
were included to simulate the motorcycle driving in corners.
2.1.1. Dynamic Analysis of Steering Angle
Motorcycle cornered mainly by rolling, not by wheel steering. The steering angle of a motorcycle in cornering was then small, and usually neglected by most research papers. However, the small steering angle
in rearview mirrors resulted in large change in rearview zone to produce large blind zone to cycle riders.
In addition, this usually made riders making wrong driving decision and caused traffic accident. The motorcycle was simulated in road with different road radius and speeds. To establish maximum steering angle
when cornering, the curve fitting method is used to find the linear equations between steering angle with
vehicle speed and road curvature, as shown in Fig. 2. The horizontal axis in Fig. 3 shows the curvature
which is exchanged from radius of curvature of the road. Higher speeds result in smaller steering angles
and curvatures. It is better to seek vehicle speed with steering angle and road curvature on the approximate
linear equation. In addition, steering angles for cornering were always smaller than 5 degrees.
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Fig. 5. Simulation results of roll angle vs road curvature.
Fig. 6. Vehicle posture angle during acceleration and deceleration.
2.1.2. Dynamic Analysis of Roll Angle
Using the same way as conducting the simulation of motorcycle cornering, the maximum roll angle when
cornering can be established. The curve fitting method also can find out the approximate linear equation of
roll angle with vehicle speed and road curvature, as shown in Fig. 4. The horizontal axis in Fig. 5 shows
the road curvature. The curves showed that larger roll angle was needed for curvier road and higher cycle
speed. Besides, it also showed that rolling, not steering was the major motion for motorcycle cornering on
curved roads.
2.2. Motorcycle Dynamics in Straight Road
2.2.1. Motorcycle dynamics when acceleration and deceleration
These two situations or changes in road conditions will produce changes in vehicle posture. The vision of
rearview mirror will al. so be affected that may cause a blind vision. As shown in Fig. 6, when acceleration,
the vehicle posture angle will rise; and it will sink when deceleration or braking.
2.2.2. Affects of Load on Motorcycle Dynamics
In motorcycle dynamic simulation, when the backside of vehicle is loaded with stuffs or humans as seen in
Fig. 7, the pitch angle (different level of front and backside of vehicle) will be changed. Figure 8 can show
clearly about the effect of load on pitch angle as the rear wheel loaded heavier resulting in the backside of
vehicle body sink.
2.2.3. Motorcycle Dynamics under Bumpy Road Surface
When travels through bumpy surfaces, the motorcycle posture will be changed that may cause dead rearview
vision. This study examined the driving under different road conditions. The observation of changing in
pitch angle is shown in Fig. 9. This study applied three common situations in traffic to conduct the dynamic
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Fig. 7. Difference in pitch angle under heavy load.
Fig. 8. Change of pitch angle (before and after under heavy load).
Fig. 9. Motorcycle dynamic when passing through pothole [3].
Fig. 10. Common situations when driving on general road [3].
simulation: pothole (sunk 5 cm), speed breaker (raise 5 cm) and road bump (raise 12 cm) as shown in
Fig. 10.
Simulation speeds were at three levels: low (20 km/h), medium (60 km/h) and high (100 km/h). The
effects of different speeds, roads and other simulation conditions to pitch angle are shown in Figs. 11–13.
From the results, it can be concluded that sunken and raised surfaces caused poor rear vision due to the
rise of sinking of vehicle body. Therefore, the mirror horizontal. angles need to be adjusted in the contrary
through those conditions. The angle adjustment equation can be obtained as follows:
θ p = θvelocity + θload + θbumper ,
(1)
where θ p is the mirror horizontal angle that should be adjusted. θvelocity , θload , θbumper are the adjustment
angles required for different speeds, load and bumpers, respectively.
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Fig. 11. Pitch Angle changes as vehicle passes through Pothole.
Fig. 12. Pitch Angle changes as vehicle passes through Speed Breaker.
Fig. 13. Pitch Angle changes as vehicle passes through Road-bump.
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Fig. 14. Prediction of roll angles.
Fig. 15. Pitch angle as vehicle passing through pothole road at (a) 20 km/h; (b) 100 km/h.
3. ACTUATION OF ADAPTIVE REARVIEW MIRROR SYSTEM
Adjustment of rearview mirror was adaptively controlled according to motorcycle postures in pitch, steer
and roll under different road radius and road conditions. The controller was built in Matlab/Simulink, and
connected to BikeSim to form a closed-loop control. BikeSim provided information of dynamic motorcycle
posture to the controller to determine mirror-adjusting angles, which went back to BikeSim to change the
angles of virtual rearview zone. A virtual rider’s rearview zone in BikeSim was also created. Therefore, the
effects of rearview mirror adjustment could be thoroughly examined in the 3D animation in BikeSim.
3.1. Design of Gray Predictor
Grey theory al. gorithm was commonly used to predict the next system output response [4, 5]. This study
applied the four most previous data y(k − 3), y(k − 2), y(k − 1) and y(k) to gray model to predict next step
output ŷ(k + 1). The data sets of pitch, steer and roll angles from cycle dynamics were fed into the gray predictor to predict their next dynamics. By this way, it could estimate those angles in advance and give mirror
system more time to response. Figure 14 showed the simulated and predicted roll angles with prediction
error less than 1◦ .
3.2. Low-pass Filter Design
As conditions changes, pitch angle is also changed but maximally around 4 degrees. It produces high
frequency of vibration on the mirror. Later, using electric motors to adjust the mirror is challenged because
the motors must be adapted to quick changes in angles.
At the frequency around 10 Hz or less, the larger amplitude of the pitch angle is occurred and it significantly reduced if the frequency is higher. Therefore, Fast Fourier Transform (FFT) frequency analysis is
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Fig. 16. Pitch angle as vehicle passing through speed breaker at (a) 20 km/h; (b) 100 km/h.
Fig. 17. Pitch angle as vehicle passing through road bump at (a) 20 km/h; (b) 100 km/h.
Fig. 18. Complete control structure of adaptive rearview mirror system.
applied to where heavy noise happened. However, adjusting the mirror with too high frequency will cause
driver eyes tired. Then, the appropriate low-pass filter was applied to mirror system at a cut-off frequency
of 6 Hz. Figures 15–17 presents the pitch angle changes at low speed and high speed with different road
surfaces and with/without low pass filter. Comparing to original waveform, the filter response slowed down
and the amplitude was smaller, providing appropriate adjustments to the mirror and giving the driver a good
rearview vision.
The structure controller is shown in Fig. 18 including gray predictor and low pass filter. The output from
BikeSim (pitch, roll, steering angle signals) are processed through Matlab/Simulink controller to calculate
the adjusted angles, and finally come back to BikeSim model to get the animation results as shown in Figs. 19
and 20.
4. EXPERIMENT RESULTS
Figure 21 shows the flow chart of this system. Angles will be measured by angle sensors and then input to
Compact RIO to receive angle signal. value, processing and send control signals to Motors. The two-step
motors and one R/C servomotor will get the controlled signal to adjust the mirror to appropriate angles
as shown in Fig. 22a. The detailed structure of experiment platform was introduced in Fig. 22b. The
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Fig. 19. Rear vision when cornering without (left) & with (right) controller.
Fig. 20. Rear vision when passing pothole without (left) & with (right) controller.
Fig. 21. Adaptive rearview mirror system flow chart.
Fig. 22. (a) Stepper motors and servomechanism control system; (b) vehicle experimental platform.
motorcycle’s roll, pitch, steer angles were measured by angle sensors. The controlled signals go to stepping
motor and servomotor to adjust the rearview mirror three-axis mirror angle. The circles present two-step
motors installed at the two axes separately to conduct linear elongation movement.
5. CONCLUSION
This study proposes an adaptive rearview mirror system, which has sensors to get the three angle val. ues.
Moreover, the gray predictor and low-pass filter have been used to optimize the control process. Through
two-step motors and an R/C servomotor, the weak points of conventional. rearview mirrors in a motorcycle
have been improved, and help driver to have a good rearview vision for various driving conditions.
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