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Position System

To obtain a position of a vehicle several different techniques can be used. This chapter will introduce the techniques which have been investigated. The major problem of the positioning is the accuracy. The systems considered in this chapter are positioning by satellite navigation, vision units, and dead reckoning.

Satellite Navigation

Positioning by satellite navigation is nowadays a very common feature. The most used system is the NAVSTAR Global Positioning System (henceforth referred as GPS in this thesis).

Global Positioning System

The basic function of satellite navigation and GPS function is described in Ap-pendix A. Many vehicles nowadays can have a GPS wayfinder integrated within the vehicle. This is often a typical commercially available GPS receiver1 unit with an update frequency of 1 Hz and with a standard deviation accuracy2 of 15 m. This accuracy is too low to fulfill the demands of keeping a vehicle within one lane of the road. To obtain the demands of the positioning system the standard deviation needs to be less than 1 m [1]. Even the update frequency of the position in a typical GPS is too low (see Example 2.1). A GPS unit with a higher update frequency and with a standard deviation accuracy of 15 m has an accuracy which is too imprecise. Our conclusion is that the typical GPS not qualifies to be a part of the positioning system.
Example 2.1: 1 Hz GPS example
If the GPS update frequency is 1 Hz and the test vehicle is traveling at 15 m/s (54 km/h). The vehicle will advance 15 m between measurement positions. This can be a serious problem in for instance cornering manoeuvres. To obtain the wanted resolution (in meters) the GPS update frequency can be estimated by the following equation.
F requency[Hz] = V elocity[m/s] (2.1)
Resolution[m]

Differential GPS

A differential GPS is an enhancement to the standard GPS system. It operates by a stationary ground network or by fixed ground local stations. By knowing the exact position of the stationary receiver, it can calculate the errors from satellite signals and send out the differential corrections to the vehicle. A base station covers a small area and the differential correction is a local correction. There are several different techniques that are currently in use to obtain the differential correction signals. The two most common techniques are Wide Area Correction System (WACS) and Local Area Correction System (LACS) [48].
EGNOS/WAAS
European Geostationary Navigation Overlay Service (EGNOS) is a Satellite Based Augmentation System (SBAS) that is under development in Europe. The EGNOS system is a WACS. The system started operations in July 2005, and will be cer-tified for use in 2008. The North American Wide Area Augmentation System (WAAS) is similar but has no European coverage [21]. EGNOS uses three geosta-tionary satellites which send out a ranging signal (similar to ordinary GPS signal). EGNOS also uses a network of ground stations that calculates the errors (clock, ionospheric disturbances, etc.) and sends out a correction signal (see Figure 2.1). This correction increases the accuracy of the GPS to approximately 2 m [19]. The problem with this system is that the accuracy is not good enough to keep the vehicle within one lane of the roadway.
SwePos
The Swedish GPS correction service EPOS is available for use. The service is provided by the Swedish company SwePos. It uses the FM-radio frequency to send out the correction signals. The coverage of this technique is very good for use in Sweden but the update frequency is between between 3 and 5 seconds. The accuracy is good, but the update frequency is too slow [57]. For that reason this technique is not suitable for this project.
Figure 2.1. Wide Area Correction System (WACS). Two GPS satellites (1 and 2) with stationary reference stations (3 and 4) that supplies the user with position information and correction signals to obtain a high accuracy position [50].
SwePos also offers a Network Real-Time Kinematic correction. This is based on a subscription provided by the GSM network. This provides with centimeter accuracy but the correction service is expensive and every user needs a subscription [57]. This technique is not suitable to our demands due to the subscription cost.
OmniSTAR
The OmniSTAR is a GPS system which offers GPS correction which can improve the accuracy of the GPS receiver. The OmniSTAR concept is a subscription service to their GPS receiver. The subscription supplies the customer with access to the correction signal of their satellites. It works like a WACS system, where multiple OmniSTAR GPS reference sites calculate the error of the signal. By sending up correction signals to the satellites from the American and Australian Network Control Center the correction data is received and applied in real-time. The system is available with an accuracy below 10 cm with the OmniSTAR service subscription [45]. This technique provides great accuracy but is still dependent on a subscription service for every user and because of this service it is not suitible for this prodject.
Local Area DGPS
One option to get differential correction signals is to use a separate DGPS base station. The range of the base station is limited, and the position accuracy de-creases with increasing distance to the base station. The base station is stationary and sends out correction signals to the DGPS receiver with e.g. a radio modem. With the local area correction signals the DGPS receiver obtains great accuracy. A position accuracy below 50 cm is achievable with this technique. The local area DGPS system is fairly expensive to implement, but it is free from any subscrip-tion services and is very suitable for implementation within a restricted area. The cost of implementing a local DGPS system is according to given indications, in the same range as one single year of subscription fees for eight units using e.g. Omnistar services. The local DGPS system is not limited to a number of users and it offers a high grade of accuracy [48]. This technique is very suitable to our demands and will be further investigated.

Carrier-Phase, L1\L2

A typical GPS receiver calculates its position by the data that is sent from the GPS satellites. A second form of precise monitoring is called Carrier-Phase (CP) Enhancement. In order to obtain greater accuracy such a GPS receiver uses the CP from the satellite signal. The CP approach utilizes the L1 carrier wave3, which has a period a thousand times smaller than the bit period of the Coarse/Acquisition code (C/A), as an additional clock signal in order to reduce the uncertainty. The phase difference error in the normal GPS results in a position error within 2 to 3 meters. Using the CP method, this position error could, in the ideal case, reach 3 cm resolution4. Realistic use of a CP-GPS (L1) coupled with differential correction, Carrier Phase DGPS (CDGPS), gives a normal position accuracy of approximatly 50 centimeters. If this technique is expanded with a L1\L2 receiver, the accuracy is at centimeter level (see appendix A.2). An accuracy comparision is presented in Figure 2.2 and Table 2.1 [48, 42]. To keep the vehicle within the roadway, a CDGPS would be recommended.

Inertial Navigation System

An inertial navigation system is a completely independent system5. The position-ing is based on integration of the small changes in direction and velocity. This is detected by an Inertial Measurement Unit (IMU). Due to the minor offset in the change of the position, the new calculated position can quickly drift to a great er-ror. See Figure 2.3 for a schematic drawing of an inertial navigator. This system is not suitable for use as a stand alone system due to the increasing error, but the technique can be used as a complement to increase the total accuracy of the combined systems [28, 48].

Combined DGPS/INS System

To obtain greater accuracy than the DGPS provides, several systems use a com-bination of a DGPS unit and an IMU. To increase the position accuracy between DGPS samples inertial gyros and/or accelerometers are used to calculate the new position. Due to the DGPS combination, the system will not suffer from severe drifting in the calculation of the new position. After every new DGPS sample, the inertial system has a known position to calculate from. This technique can deliver position with a very high sample rate (e.g. 250 Hz [39]). When adding a Kalman filter to this setup, the system obtains even greater resolution. The Kalman filter uses the input errors to give the system an even more exact position. The stan-dard deviation is below 2 cm in some products6. To further improve the position accuracy a single/double antenna GPS, differential GPS correction, and an IMU unit can be used. See Figure 2.4 for a block diagram of DGPS/INS unit. The input to the figure is the measured value of the gyros and accelerators [47, 48].
The ordinary use of this technique in the automotive industry is to measure vehicle handling (roll-, pitch-, yaw-angles7, slip, etc) [47]. A combined DGPS/INS system would be an appropriate choice for this application, but this technique leads to very expensive hardware.

Vision System

This section presents different vision systems that are used for automotive imple-mentation such as collision avoidance, adaptive cruise control, and lane detection systems. Vision systems can be used for positioning with reference points by measuring distance and heading to the reference points.
Figure 2.3. A schematic drawing of a Inertial Navigation System (INS). The system contains gyros and accelerometers to obtain information in three dimensions and a com-putional unit to process the information signals.
Figure 2.4. Schematic block diagram of a combined DGPS and INS unit. The com-putional unit combines the information from the GPS receiver (single or dual antenna), the INS system, and receives differential correction signals from a differential base sta-tion via the radio modem. All this information supplies the user with a high accuracy in position [47].
Table 2.1. Accuracy of different navigation types
Navigation Type Theoretical performance
GPS ≈15 m [1]
GPS with EGNOS ≈2 m [19]
GPS L1 Carrier Phase 1.8 m [42]
GPS L1\L2 Carrier Phase 1.5 m [42]
OmniSTAR GPS sub m [42]
Local Area DGPS L1 Carrier Phase 0.45 m [42]
Local DGPS L1\L2 Carrier Phase sub dm [42]
Local DGPS L1\L2 with INS sub dm [47]

Line Following Systems

A system that is commonly used by Automated Guided Vehicles (AGVs) is the line following principle. By using a guidance system the vehicle can follow a predefined guidance line by itself. Vehicles with monotonous driving schedules are suited for this system. The principle of implementation is usage of a vision system (e.g. a laser scanner) that detects a significant marking or reflection material and uses it as guidance. This technique can also be implemented with magnetic force, which the PATH project (see Appendix C.1) in California is one example of. Using permanent magnets in the roadway and detectors in the vehicle results in a robust system. However this technique suffer from problems as relocation and mobility of the system, due to the need of static implementation [49]. Due to our demands of movability of the system, this technique is not of interest to our needs.

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Camera Systems

Camera systems can use one or several cameras in combination with a computer to perform image processing. The camera systems can give a very high resolution, and advanced target classification is possible thanks to the detailed images. The camera systems are very dependent on good light conditions and free sight of view. Darkness and weather conditions as rain and snow, lower the resolution of the images which leeds to lower reliability of the camera system. When combined with infrared, or thermal, cameras the system can see in the dark. Such camera systems suffer from reflection of heat radiation which makes it hard to use within navigation and safety purposes. The image processing algorithms are computation intensive which may make it difficult to maintain reliability when the environment changes rapidly (such as at high speed driving) [31]. Advantages and disadvantages of this system are presented in Table 2.2.

Radar Sensors

Radio detection and ranging (Radar) is one of the most common tracking sensors. It has been used for automotive purposes, such as adaptive cruise control. A radar emits electro-magnetic radiation to illuminate targets. It uses the same antenna to emit as to receive, by switching between sending and receiving mode. It sends out a conical lobe that is reflected by the object. To obtain information about the target, the system receives an echo of the emitted signal and can calculate the distance to the target. One sensor can do a mechanical sweep, or electronically switches can be used to alternate between different sensors, each located at different emission angles. These techniques make it possible to survey a wider area. In general for automotive purposes the field of view is 10◦-15◦. For short distances (less than 200m), the radar has good performance in bad-weather conditions, e.g. darkness, rain, haze, and snow. Although good performance, the resolution to verify the objects’ identities is not very good due to the wide lobe. For this project, the radar needs assistance of other devices to obtain acceptable performance. The radar suffers from unwanted reflections called clutter. Reflections from the road surface might give « ghost » obstacles. Multipath propagation might also occur. The precision of the radar is not suitable as a stand alone implementation of navigation. The best use of this application would be as an Automatic Cruise Control (ACC) system [31]. Advantages and disadvantages of this system are presented in Table 2.3.

Laserscanners

The laser scanner (also known as Lidar) works like a radar. A laser pulse with a defined duration is sent and reflected by an object. The reflection of the object is captured by a photo diode and transformed into signals in an optoelectronic circuit. The time interval between the pulse of light being sent and its reflection being received indicates the distance to the object which reflected the light. In addition to the radar, the laser pulse is quite narrow. This gives the laser scanner a higher resolution of the object.
By rotating a mirror, the laser range finder operates as a scanner and the mirror deflects each outgoing beam. The mirror’s continuous rotation, in conjunction with the pulsing laser, generates a complete environmental profile of the vehicle within the laser scanners visible range (see Figure 2.5). The laser scanning system has been adapted by several autonomous prototype vehicles. The lidar technique has also been implemented by Volvo Technology at their Volvo Integrated Safety Truck (see Appendix C.4.2). Usage of the lidar is for example collision avoidance, pedestrian safety, blind spot surveillance [31].
Lidar Performance
The laser scanner has a very high sample rate. This makes it suitable for scanning the environment at high speeds. This technique is similar to millimeter-radar (mm-radar), but is a less expensive technique to use. The range and the narrow lobe of the laser makes the system very precise. It provides a high resolution of the pixel map and could give more detailed information than the mm-radar. The laser scanner system is also very tolerant to clutter. Again, the narrow beam does not suffer from reflections of nearby objects in the same degree as a radar [31].
The intensity of the reflected laser pulse can be detected by the lidar and can easily be projected into a gray scale picture. This is very useful to implement in the lane detection feature (see Figure 2.6). The laser scanner is relatively insensitive to the surrounding light conditions [31, 35, 58, 54]. Despite all of these advantages, the laser scanner suffers from a couple of weak-nesses. In the automotive industry, most of these systems are at prototype stage. This makes the price high at this stage, but will probably drop when prototypes go to large series production. In similarity with the camera system the laser scanner must have a free line of sight. Rain and fog could also interfere with the correct echo detection. A single pulse can be reflected by rain or other weather obstacles. Due to the technique of reflection the lidar has difficulties to detect dark and rough objects. These objects are hard to detect due to absorbation of the laser beam. The lens also needs to have a clear view to avoid false detections [31, 43].
The lidar vision system uses several different techniques to increase its perfor-mance. Dirt on the lens could result in a false detection. The dirt reflections can to a certain extent be filtered by processing the signal. This applies to limited surface elements. The obstacles of the lidar, such as bad weather performance is improved by using four-echo technology. An object, such as a raindrop or another vehicle, would normally generate one reflection or echo per laser pulse. By increas-ing the number of echoes to as many as four per pulse, and by filtering the echoes and removing the false echoes, the lidar has significantly optimized and refined object detection [26]. For implementation at a truck that is supposed to drive under very rough road conditions, the system is exposed to hard oscillation. The system handles this problem with a multilayer technique (see Figure 2.7). The laser beam is split into four different layers and the distance measurements are taken independently for each of these layers with an aperture angle of 3.2◦. This allows compensation for pitching of the vehicle, caused by an uneven surface or driving manoeuvres such as braking and accelerating. Since the beam, generated by each laser pulse, is split into four layers, the lidar sensors can evaluate the data from the reflections (up to 16 reflections per measurement, four echoes and four layers). This technique gives a high grade resolution and reliability [24].
All products are in a prototype stadium. A truck implementation is available as well as the possibility to produce products according to customer specifications. The scan of the surrounding environment detects objects, due to the many reflec-tion points, in a high resolution picture. This also results in that the detected object can be identified by its significant structure. The detected objects can be assigned with an ID number, a velocity, and a heading. Due to the high scanning frequency a high resolution model of the surrounding environment can be esti-mated. In the model can objects be classified as a car, a truck, a pedestrian, a fixed object, etc. By using the heading and distances to known objects, navigation is possible. In Figure 2.8 the different cars’ velocity and headings are marked with circles and arrows. The fixed object is marked with a square. In the picture to the left, it is shown how the lidar detects objects and different contours in the sur-rounding environment. The precision of the position can also be increased when using precise high level maps [62]. Detection of the lanemarkings can also be used for road navigation and vehicle control [13, 35, 37].
An installation of two laser scanners in the front of the truck will give a sat-isfying visual coverage to prevent collisions and the ability to navigate by nearby objects (see Figure 2.9). The lidar function and performance is suitable as a vision system to an autonomous system. The lidar system is used for safety applica-tions by many developing companies and is frequently used by the D.A.R.P.A8 autonomous vehicles [6, 26]. Advantages and disadvantages of the system is pre-sented in Table 2.4.

Complete Position System

To fulfill the demands of the problem specification in Chapter 1.3, the performance of the CDGPS is of interest as a positioning system and will be further investigated. The input signals to the position system will in this stage be from a DGPS, the CAN (Controller Area Network) information, and from the vision system. A block diagram over the system principle is presented in Figure 2.10. The vision system that, at this point, seems to have the most advantages is the lidar system. By integrating the lidar vision with the DGPS, the vehicle’s position system increases its robustness [25].

Table of contents :

1 Introduction 
1.1 Background
1.2 Volvo 3P
1.3 Problem Specification
1.4 Limitations
1.5 Thesis Outline
2 Position System 
2.1 Satellite Navigation
2.1.1 Global Positioning System
2.1.2 Differential GPS
2.1.3 Carrier-Phase, L1\L2
2.2 Inertial Navigation System
2.3 Combined DGPS/INS System
2.4 Vision System
2.4.1 Line Following Systems
2.4.2 Camera Systems
2.4.3 Radar Sensors
2.4.4 Laserscanners
2.5 Complete Position System
3 Communication Systems 
3.1 STDMA
3.1.1 VDL Mode 4
3.1.2 TACSYS/CAPTS
3.1.3 STDMA Summary
3.2 Wireless Local Area Network
3.2.1 IEEE 802.11
3.2.2 WLAN With Dual Antennas
3.2.3 Selective Channel Scanning
3.2.4 Handover Using Neighbour Graph
3.2.5 IEEE 802.11 Summary
3.2.6 ZigBee
3.2.7 WiMax
4 Collision Avoidance 
4.1 Collision Avoidance Prediction
4.2 Vehicle States Message
4.3 Collision Avoidance Vision
5 Measurements and Data Collection 
5.1 GPS coverage
5.1.1 Static GPS Coverage Hällered
5.1.2 Test Track GPS Coverage
5.1.3 GPS Accuracy
5.1.4 Dual GPS
5.1.5 Differential GPS
5.2 Laser Scanner Data Collection
5.3 WLAN coverage
5.3.1 WLAN range
6 Conclusions 
6.1 Positioning Conclusions
6.1.1 Positioning
6.1.2 Vision
6.2 Communication Conclusions
6.2.1 WLAN
6.3 Survaillence Conclusions
6.4 Collision Avoidance Conclusions
6.5 System Movability Conclusions
6.6 Future Work
6.6.1 Positioning System
6.6.2 Lidar System
6.6.3 Communication System
6.6.4 Collision Avoidance System
6.6.5 Fault Detection
Bibliography

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