Perception Estimation of Vehicle Motion

Syed Riaz un Nabi Jafri Electronic Engineering Department

NED University of Engineering and Technology Karachi, Pakistan

riazun1036@yahoo.com

Ahmed Zeeshan Pervaiz Electronic Engineering Department

NED University of Engineering and Technology Karachi, Pakistan

ahzeeper@gmail.com

Abstract—Vehicular movements are bounded to some rules and regulations based on the environment and road networks. Visual signals are the most common way of transferring perception of motion to immediate vehicles but these indications do not portray correct perception of the motion. Visual indications are reserved for forward vehicle intensions but such indications can not provide surety of avoiding collisions between vehicles because these indications are generated in result of human decisions. Main reasons of collision are either wrong estimation of motion of immediate vehicles by the driver or a delayed understanding of visual indications by followers of particular vehicle. If this delay is transformed into immediate understanding by using intelligent controllers in vehicles then result will be a collision free motion between them. In this paper, an understanding is developed against perception of motion and its effects on followers and a recommended Intelligent Controller is presented for vehicles.

Keywords-Latitude; Longitude; vector; velocity; Controller.

I. INTRODUCTION On road networks heavy vehicular movements are

common. With the increase of vehicular density on roads different rules and regulation are followed to increase humans and vehicles safety. These rules are evolved after deep thoughts and observations of different scenarios present on daily life. To prevent unwanted events it is suggested to transfer signals between moving vehicles and environment around it. The best way of communication is selected are Visual Signals which are adopted on both vehicles and on road sides. These visual indications are more effective for understanding Road Signals but they are less effective to communicate efficient estimation of motion between neighboring moving vehicles. All emerged indications from moving vehicles are the virtue of human’s decisions driving particular vehicle so these indications are derived against human’s estimations of motions for neighboring vehicles. These human perceptions may cause serious accidents because these are not accurately estimated but a guess of human observation around its environment.

In this paper this problem is discussed and it is explained how human perception can be dangerous. To understand complete picture of the topic a simple study is developed for a simple one way road having different vehicles moving on it. Different parameters of motion are explained and then a calculated perception estimation result is developed for

moving vehicles. It is then desired to utilize an intelligent controller which can provide assistance to the drivers for finding correct estimation of motion. For routine inspections an intelligent controller is integrated for the purpose of Vehicle tracking in most of the vehicles in these days. It is recommended to utilize similar Tracker controller to additionally calculate perception of the vehicle motion. For this purpose an advanced version of the Tracker Controller is required having sophisticated calculations capability for finding perception estimation of the motion.

II. PERCEPTION ESTIMATION To understand perception of motion of vehicles for one

another, it is consider that a moving vehicle is in motion with a single vehicle following it. Estimation of front vehicle movement will understand by considering its motion style and after that its impacts will observe on the follower for that vehicle. When a vehicle is in motion many parameters are associated to it such as covered displacement “S” or S(t) as a function of time, velocity “V” or V(t), acceleration “a” or “a(t) “ and momentum “P” or “P(t). These all parameters are vector in nature & valuable source of information for observing perception of movement of the vehicle. It is recommend that avoid scalar parameters associated to vehicle for better understanding of motion orientation of the vehicle. Consider Displacement & velocity of the vehicle and converting both parameters into its two main components which are X & Y-components. A mathematical representation of Displacement & Velocity vectors are shown in eqn.1 & in eqn. 2. S(t) = Sx(t) + Sy(t) (1) V(t) = Vx(t) + Vy(t) (2) It is considered that motion surface is a plain surface with no crust and troughs so ignoring third component in Z-axis for maintaining simplicity for motion observation. If vector components of velocity are plotted then different results will observe for different style of motion. Considering nose direction of the vehicle as Y-axis during plots & Fig. 1 is showing a velocity representation in only y-axis means a straight motion is performed by vehicle.

2009 Third UKSim European Symposium on Computer Modeling and Simulation

978-0-7695-3886-0/09 $26.00 © 2009 IEEE

DOI 10.1109/EMS.2009.84

70

2009 Third UKSim European Symposium on Computer Modeling and Simulation

978-0-7695-3886-0/09 $26.00 © 2009 IEEE

DOI 10.1109/EMS.2009.84

70

2009 Third UKSim European Symposium on Computer Modeling and Simulation

978-0-7695-3886-0/09 $26.00 © 2009 IEEE

DOI 10.1109/EMS.2009.84

70

Different displacements are plotted in comparison of velocity to explain orientation of motion. This style of motion is common on particular lanes of road. Only Y-component of velocity is explaining that vehicle is continuing straight motion and no change of heading is occurring during motion. Displayed values of velocity are taking on same intervals and their distances are shown with them. It should be in mind that parameters associated to motion of a vehicle are continuous in nature. A continuous plot is the best presentation for any particular parameter associated to motion but these parameters are presenting in this paper by sampling them during regular interval of time. This style of presentation is selected because intelligent controllers are digital systems having some sampling rate to sense analog (continuous) parameters and then process sampled values according to they are coded. In Intelligent Controller, velocity or other parameters will sample on equal intervals for processing as shown here. In this presentation, sampling rate is developed of one second for better understanding but it can be modified to a faster sampling frequency. Vy Sy 20 Vx 20 Sx (m/s) (m) 10 10 t t 1 2 3 4 1 2 3 4

Time (sec.)

Fig. 1 Velocity & Displacement plots in straight motion Now considering a typical vehicle motion on roads in which heading of the vehicle will change with the passage of time as shown in Fig. 2. A plot of changing nose direction of the vehicle shows that both X & Y components will create during the movement. Vy Vx Sy Sx 10 10 9 9 (m/s) 6 (m) 6 5 5 t t 1 2 3 4 1 2 3 4

Time (sec.)

Fig. 2 Velocity & Displacement plots in X-Y motion

Fig. 2 is showing results observed for both Velocity and Displacement during motion. Displayed values are sampled on regular intervals and showing an average response of the vehicles during consecutive intervals. The above plot is showing that particular vehicle will eventually not move in straight path means it will make projections of vehicle dimensions along X & Y axis and if projection is appearing on X-axis then neighboring vehicles will face some part of it. To further investigate this point, consider Fig. 3 which is showing vehicle orientation as a function of time. Here symbol “L” is used to present length of the vehicle and Lx & Ly are its components. Ly Lx 3 2.8 2.6 (m) t 1 2 3 4 (sec.)

Fig. 3 Vehicle Orientation Plot

By observing above plot it is clearer that projection of the vehicle along X axis will cause real problem of collision as it may appear in the path of neighboring vehicle. This scenario will be more explainable if consider a two lane road and two vehicles are moving in same direction as shown in the Fig. 4. If leading vehicle starts to change its orientation with some passage of time then there is a vital chance of entering this vehicle in the path of vehicle behind it so certain collision will eventually happen as shown in the diagram. The leading vehicle is generating more projection towards X-axis which in result facing another vehicle approaching towards this direction. Three time intervals as t1, t2 and t3 are showing a continuous motion style for explaining this scenario. Y-axis Ly t = t3 t = t3 Lx Ly t = t2 t = t2 Lx t = t1 t = t1 X-axis

Fig. 4 Occurrence of Collision

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As previously discussed this collision is happened due to the wrong guess of the driver controlling front vehicle or in some cases follower vehicle and it is a day to day observations that almost every driver may create wrong estimations of motion of other vehicles and in some cases if these estimations are too narrow to cause collision then results are dangerous for both humans and vehicles. Fig. 4 is presenting same picture in which such collision occur due to a wrong guess of driver. If wrong decision has been made by the driver in front vehicle then it will be difficult to understand future estimations of the motion by the driver in the back vehicle in a very short duration of time. If at the moment of taking wrong estimation by first driver, an intelligent assistance is provided to him telling about estimation of motion of both vehicles then the driver will change its decision or at some advance level, Intelligent Controller will not pass instructions generated by driver to the vehicle to stop activation of the wrong decision before time. So it is very important to have such intelligent controller which can assist humans with more concrete knowledge of the estimation of movements of the vehicles around it.

III. VEHICLE RESPONSE

Before going to develop main components of the intelligent system it is important to focus vehicle behavior during motion. Consider a moving vehicle which is going with some Velocity and for some reason suddenly it stops its motion then this motion will not stop due to inertia of the vehicle and motion continues for some time covering some distance. It is also observed that time taken by vehicle to stop will be different for different surfaces. Each surface has different rate of friction so vehicle response also changes on each surface. In this discussion for simplicity ignoring variable effect of friction offered by individual surfaces. These behaviors are pointing here to discuss that every action of the vehicle will definitely prolong with time and perception estimation about sudden actions are too difficult to analyze accurately without calculated figures. All neighboring vehicles will monitor sudden change but with out accuracy. Fig. 5 (a) is showing particular vehicle behavior if it applies suddenly breaks to stop motion.

Circles By t =T+∆t Bx Vehicle to stop t= T (a) (b)

Fig. 5 Orientation of Vehicles after application of breaks

Circles are indicating particular vicinity if the vehicle suddenly stops. If the velocity of the vehicle is greater then vicinity circles will be more in dimension. Dotted circle is representing that case in which vehicle speed is less while continued circle is for the case at which speed is greater. These circles are not showing perfect circular shape. This shape of the vicinity is developed to understand that if during application of breaks some angle is created at the nose of the vehicle then vehicle will reach at some point in the vicinity circle as shown in Fig. 5 (b). The last point at which movement is stopped will depend on velocity of the vehicle and friction offered by the road (path). So it is important to pass out some basic data of the moving vehicle to neighboring vehicles. From this discussion it is concluded that this data should contain an approximate displacement values in both axis at which vehicle will stop if breaks are activated. It will very helpful if this data is provided to the followers because then they can predict very accurately where particular vehicle will be if it continues motion for next instants. Right now displacement covers after application of breaks is pointing by By and Bx as shown in Fig. 5 (b).

IV. DETERMINATION OF REFERENCE LOCATION FOR VEHICLES

If some parameters associated to motion are required to

pass to neighboring vehicles then it should determined against some reference locations because on road networks every vehicle moves with continuous changes in direction. So it is impossible to declare a particular vehicle a reference to other vehicles as shown in Fig. 6 (a). If some heavy sensor array is placed on both vehicles then it may be possible to create reference calculations between those two vehicles but this scenario is impossible to develop on a path with a number of vehicles. Any pair of vehicles can create reference to each other but in the whole it is impossible for each vehicle to be referenced by a single vehicle.

t = T+∆T Lat1,Long1 Dx Dy Dy = Lat1 - Lat2 Lat2,Long2 Dy Dx = Long1 - Long2 Dx t = T

(a) (b)

Fig. 6 Orientation of Vehicles

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On a populated road it is required to declare a reference location which can be understandable to all vehicles and it is suggested to use GPS values as a reference location to identify displacements between vehicles. GPS is very common and available on many tracker units. So if any two vehicles want to identify their positions with respect to each other then they can determine it by passing their current positions in terms of Latitude & Longitude values as shown in Fig. 6 (b). Displacements Dy and Dx are representing in terms of latitude and longitude differences between vehicles.

V. DEVELOPMENT OF INTELLIGENT CONTROLLER

Fig. 7 is showing a basic Block Diagram of Intelligent Controller who can assist driver of that particular vehicle about safe driving during travelling. This basic diagram is derived from a common tracker unit diagram. Main difference between the two systems are connectivity of a Display module and proposed ABS/Inertial sensor in parallel with GPS sensor as shown in following diagram. Main inputs of the controller are taken from GPS receiver and from ABS/inertial Sensors. GPS receiver will provide current position in terms of Lat. & Long. Values and ABS/inertial sensor will more precisely calculate rate of motion i.e.; velocity and displacement of the vehicle.

Fig. 7 Block Diagram of Intelligent Controller

Although GPS receiver can pass out rate of motion but it is recommended to use ABS/inertial sensor for more accurate calculation. After taking basic parameters regarding motion intelligent controller will evaluate some parameters and pass out this information to neighbors. These parameters include current position in Lat. & Long., Components of Velocity, and heading and estimated displacement for some next instants. The last parameter is the most important one which will show correct estimation of the particular vehicle in next instants to neighboring vehicles. This packet of information then transmits to other vehicles and a similar response is achieved in same passion from others. These responses then display to driver of the vehicle and then

efficient estimations can generate from the system. This proposed system is not requiring a huge design changes in available tracker units so it will be very easy to modify current units with one specified here.

VI. CONCLUSION

Intelligent Controller will definitely decrease chances of collision due to incorrect estimations made by humans. But to establish this system very accurate and reliable some other basic points should be clarified. One very important point is the development of communication protocol and selection of correct hardware for it. During heavy traffic every vehicle will respond to one another so it is critical how each vehicle differentiate only neighbors but not all vehicles present on the road. Moreover accurate reference location should predict if some sensor array is interfaced between neighbors so it will also a thoughtful task to integrate some basic sensors with each other. At first stage this system will only assists humans and inform correct estimations but in second stage they will also have capability to take control of the vehicle if for any reason humans are involved in issuing wrong decisions. After sorting out all indicated points the resulted system will be definite requirement of every vehicle and it will definitely reduce rate of accidents.

ACKNOWLEDGMENT

We are very thankful to Mr. Waqas, Mr. Adnan & Mr. Imran from NED UET for their valuable suggestions and participation during this work. Some technical help is taken from auto engineers so we also thank to provide some technical knowledge. In special we are very thankful for department technicians to provide some useful help against creating hardware for the system.

REFERENCES

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[3] E. Abbot and D. Powell. “Land-Vehicle Navigation using GPS”. Proc. of the IEEE, vol. 87, NO. 1, Jan. 99.

[4] J. lijima “locomotion control system for mobile robots,” Proc. 7th IJCAI, 1981, p. 779.

[5] Hongo, Takero Gunji, Yuzo. “An Automatic Guidance System of a Self-Controlled Vehicle,” Proc. of the IEEE Conference on IE, vol. IE-34 ,Feb.87.

Intelligent Controller

GPS Reciever

ABS/Inertial sensor

Transmission Unit

Internal Display

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