Implementation of V2X with the integration of Network ...

Implementation of V2X with the integration of Network RTK: Challenges and solutions Scott Stephenson, Xiaolin Meng, Terry Moore, University of Nottingham, UK Anthony Baxendale, Tim Edwards, MIRA Ltd, UK

BIOGRAPHY Scott Stephenson is a postgraduate student at the Nottingham Geospatial Institute within the University of Nottingham. He holds a BSc degree in Surveying and Mapping Science from the University of NewcastleUpon-Tyne. After completing his degree, he was a Senior Engineering Surveyor working in the UK. Scott‟s postgraduate research is sponsored by MIRA Ltd through an EPSRC CASE studentship. Xiaolin Meng is Associate Professor, Theme Leader for Positioning and Navigation Technologies, and MSc Course Director for GNSST & PNT at the Nottingham Geospatial Institute of the University of Nottingham. Dr Meng‟s main research interests include ubiquitous positioning, location based services, intelligent transportation systems and services, network real-time kinematic GNSS positioning, etc. He is author of more than 200 papers and founding Director of Sino-UK Geospatial Engineering Centre. He also holds a few Professorships in renowned academic organisations in China. Terry Moore is Director of the Nottingham Geospatial Institute (NGI) at the University of Nottingham; where he is the Professor of Satellite Navigation and also currently an Associate Dean within the Faculty of Engineering. He holds a BSc degree in Civil Engineering and PhD degree in Space Geodesy, both from the University of Nottingham. He has 30 years of research experience in surveying, positioning and navigation technologies and is a consultant and adviser to European and UK government organisations and industry. He is a Member of Council and a Fellow of the Royal Institute of Navigation; a Fellow of the Chartered Institution of Civil Engineering Surveyors; and a Fellow of the Royal Astronomical Society. Anthony Baxendale graduated in 1982 with a BSc (Eng) honours degree in Aeronautical Engineering from Imperial College of Science and Technology and in 1986 with a PhD in Offshore Engineering from Heriot Watt University. In 2003 he graduated with a Master of

Business Administration from Henley Management College. Dr Baxendale joined MIRA Ltd. in 1991 after a period of 5 years at the Aircraft Research Association. He is now Head of Advanced Technologies & Research. He was also formerly a board director of innovITS the UK National Centre of Excellence in Telematics and chairman of the European Car Aerodynamics Association. Dr Baxendale is responsible for MIRA‟s research strategy and the management and development of the programme to deliver this. The key pillars of this programme are low carbon vehicle technologies, intelligent transport technologies and unmanned ground vehicle technologies. He also has an effective and innovative management portfolio with a proven track record of successfully delivering a broad range of transport related research projects for a wide range of government agencies, commercial organisations as well as internally funded R&D programmes. Tim Edwards is the Lead Engineer of the Intelligent Transportation Systems (ITS) research group at MIRA Ltd. Tim graduated from the University of Leicester in 2002 with a BEng (Hons) in Electronic and Software Engineering. He returned to the University to complete an MPhil in the area of “Fault-tolerant software architectures for control applications”. Tim has been with MIRA for six years where he his work has spanned a number of areas including: Leading research projects, developing embedded software for commercial applications, conducting functional safety analysis, and developing design and test processes to ensure software quality and safety. Recently Tim completed work on a number of control systems for the unique, purpose-built, ITS test facility that opened last year at MIRA (innovITS ADVANCE city circuit). ABSTRACT This paper highlights the major improvement in vehicle localisation that is possible through the adoption of Network Real-Time Kinematic (N-RTK) GNSS positioning, while assessing in detail the real-world challenges that need to be overcome before its implementation. From previous research, it was shown

that vehicle positioning with the aid of a Network RTK GNSS receiver was limited by the cell network communication link problems causing degradation of the important correction messages, and GNSS outages typical with GNSS receivers. During a thorough assessment of each of these issues, and tests carried out in the laboratory and real world driving, the extent of the problem is detailed. It is found that the cell network handling the communication of the correction messages is fairly robust, but once the signal strength drops to approximately -100 dBM, message losses and message delays degrade the position solution. The extent of the GNSS signal outage is assessed in a motorway environment with regular overhead obstructions. The average total GNSS outage period is found to be around 1.13 seconds, with an average time of 13.13 seconds to resolve the ambiguity for the Network RTK GNSS solution. These figures are useful in the assessment of complimentary positioning sensors for a ubiquitous positioning system. INTRODUCTION Real-time vehicle localisation is one of three key enabling technologies for the concepts of Vehicle to Vehicle and Vehicle to Infrastructure (V2V and V2I, collectively termed V2X), a classification of Intelligent Transport Systems (ITS). The further enabling technologies are adhoc dynamic networking of agents and accurate dynamic local traffic maps [1]. The position must be accurate, reliable, available, and continuous, as described in the Required Navigation Performance (RNP, [2][3]). V2X technology is a natural evolution in road transport that has been claimed to be the next major safety breakthrough [4]. The concept moves away from vehicles making individual decisions about road safety (such as in Advanced Driver Assistance Systems (ADAS)), towards a cooperative driving approach that further shifts the emphasis from collision protection to collision prevention. A graphical representation is shown in Fig. 1. The National Highway Traffic Safety Administration (NHTSA) in the US estimates that V2X technology is capable of avoiding or minimising up to 80 percent of collisions of unimpaired drivers, and that even a small number of deployed vehicles will provide tangible safety benefits [4]. The US is a particularly unique example in that many collisions and fatalities occur at road intersections, due partly to the historically inadequate prioritisation and drivers that like to take a chance. The EU published a White Paper in 2011 stating aims to reduce transport emissions by 60 percent by 2050, partly through intelligent mobility that will improve the flow of transit, and the efficient use of infrastructure and communications networks [5].

Fig. 1: US Department of Transportation visualisation of V2X technology [6]. With technology currently available, it is more accurate and reliable to share the position and velocity information between vehicles (that may be out of sight of each other or hiding around the corner), than trying to measure this information with vehicle bourn sensors (such as with imagery and terrestrial measurement devices). Other information about the state of the vehicle can also be transferred including data about the condition of the road ahead - such as whether the road surface has low traction or that there is traffic congestion - allowing the vehicle to enhance its situational awareness. Vehicles in V2X applications require ubiquitous positioning, operating in environments ranging from highly developed regions with dense infrastructure and communications networks to areas of undeveloped dirt tracks in barren landscapes. Satellite-based positioning is a strong contender that can deliver widely available absolute positioning globally. Early research into the most advanced form of ITS autonomous vehicles - evolved from rudimentary dead reckoning towards GNSS positioning, but has recently moved away from GNSS positioning due to its known frailties and complexity. Modern examples of vehicle autonomy utilise terrestrial measurement devices and advanced camera imaging techniques that sensibly relies on no external infrastructure, without the use of GNSS positioning (examples include Google‟s autonomous vehicle [7] and the Bowler Wildcat from the Mobile Robotics Group at the University of Oxford [8]). However, GNSS positioning is available, and the most recent positioning techniques can offer high accuracy and reliability. With modern GNSS signals together with additional GNSS constellations from Galileo and Compass, the future availability of GNSS positioning will increase, and new techniques should offer increased usability and ubiquity in vehicle positioning. Due to the mass adoption of GNSS positioning for absolute vehicle tracking and positioning, and the subsequent criticism of its drawbacks (e.g. signal outage; cycle slips (in carrier phase measurement); low position accuracy; poor position continuity and integrity; and

GNSS signal jamming or spoofing), researchers have tried to offset these deficiencies through the integration of additional sensors such as low-cost Inertial Measurement Units (IMU), utilising dead reckoning (DR) mechanisms, and integrating map matching techniques. During periods when GNSS positioning does not meet the RNP, alternative sensors can either combine with GNSS positioning for an integrated solution or simply „bridge the gap‟. Past research has shown that this gap can become too large to be bridged and so research effort focused on sensor integration. These additional sensors and techniques also have their own deficiencies, and although some combinations have complementary strengths and weaknesses – such as GNSS and IMU sensors – these systems are complicated and often lack significant integrity and robustness. A simple navigation system scheme is shown in Fig. 2, where Dead Reckoning (DR) and GNSS positioning systems are processed through a Kalman filter to produce a Navigation Solution. The filter can take its estimates from either the GNSS solution, or if this is unavailable, from the predicted position from the previous iteration. The comprising parts of the DR solution can be calibrated and verified by the navigation solution, and the GNSS solution has an additional integrity check built in based on vehicle constraints. The focus of this paper is the constitution of the GNSS solution, identifying the two major weaknesses, and detailing the requirements of the sensors that are needed in the DR solution to minimise these weaknesses.

Fig. 2: A simple vehicle navigation system flow chart. The emergence of Network RTK GNSS positioning in the surveying industry over the last six years has dramatically increased the performance of satellite-based positioning, and now offers a solution that provides high accuracy, wide availability and mobility, robust integrity, and relatively low overall cost for the end user. Although initially designed for the demands of static positioning in surveying, it is possible to transfer the technology to the

more dynamic environment of vehicle tracking. Network RTK GNSS positioning can significantly improve the GNSS performance of the navigation solution, and reduce the period during which DR sensors are required. This paper aims to identify the size of this gap, and highlight the requirements of complimentary sensors that together with Network RTK GNSS positioning will provide vehicle positioning for ITS and V2X applications. Network RTK GNSS positioning, like V2X applications, requires a communication system; and by its nature V2X has a positioning solution requirement. Thus the integration of Network RTK GNSS positioning is complimentary to V2X systems. The consensus between car manufacturers and research organisations is that the future of V2X communication lies with Dedicated Short Range Communication (DSRC) devices, and a large scale pilot study is currently underway in Ann Arbor, Michigan [9]. However, in the short term many V2X applications could be applied using existing communications technology, such as cellular communication. This would offer a legacy solution, and initiate the early uptake of V2X applications. This paper builds on previous research carried out by the Nottingham Geospatial Institute (NGI), where Network RTK positioning was shown to provide a high accuracy positioning solution during real-world trials. There were two areas of concern however: The loss of the fixed integer ambiguity during satellite line-of-sight outages; and the fragility of the data communications service that delivers the real-time correction information. During road tests of different scenarios, a fixed ambiguity Network RTK GNSS solution was available for less than 50% of the time on UK roads [10]. A series of pilot road trials have shown that the Network RTK GNSS technique is able to consistently perform a fast integer ambiguity resolution after significant cycle slip and GNSS outage events, and that a degraded differential GNSS (DGNSS) solution can still deliver decimetre accuracy during less significant cycle slip events. This means for instance, that following a complete loss of lock of all satellite signals (such as passing under a bridge at 60 mph), the period of reacquisition of the fixed integer ambiguity is typically less than twenty seconds. This short period still requires bridging using alternative sensors, but the period is within the tolerance of the traditional sensors that suffer from unbounded error sources. NETWORK RTK VEHICLE POSITIONING The proliferation of Network RTK GNSS positioning systems has increased dramatically over the last decade. Networks of continuously operating reference stations (CORSs) are liberally spread across Europe, North

America, Australia, and East Asia. Networks vary in size from five or six reference stations serving as a positioning system for agriculture, to systems containing hundreds of CORSs that provide national or regional levels of service, primarily for various geosciences, environmental and engineering applications. As an example, Fig. 3 shows the location of the OS Net CORS run by Ordnance Survey in Great Britain.

Fig. 4: The improved navigation performance from RTK (left) to Network RTK (right).

Fig. 3: OS Net reference station network in Britain, owned by Ordnance Survey [11]. Fig. 4 shows the main advantage of Network RTK GNSS positioning as compared to traditional RTK GNSS positioning. In the first diagram, the individual RTK GNSS reference stations suffer from the spatial decorrelation of errors as distance between reference and rover receivers increases. This is a major deterrent for vehicle positioning, as a wide range of mobility is required, which would require individually operating reference stations to be placed approximately 20-30km apart. However, a network of GNSS reference receivers (a CORS network) can be used to develop a model of differential corrections, as shown in the second diagram, from which a rover receiver can interpret RTK GNSS correction information and utilise this during the computation of its position. A minimum of four or five reference stations are needed for a successful network, depending on the network correction technique and the region size that one intends to cover [12], [13]. The geometry of a CORS network allows two adjacent reference stations to be located up to 80-100km apart without degrading the accuracy [14], although in practice most systems tend to locate them closer together than this. This is essentially a reduction from 30 reference stations per 10,000km² for conventional RTK, to 5-10 reference stations for Network RTK GNSS positioning, which is a very cost-effective approach that can deliver high precision services to virtually unlimited users [15].

Although there are other terrestrial-based positioning systems, the Network RTK GNSS positioning method using CORS networks is still the only reliable, real-time, centimetre-level accuracy, and wide area coverage technique available [16]. It is expected that the CORS networks will become a critical part of a country‟s infrastructure, and countries like the UK are leading the way. This makes Network RTK GNSS positioning one of the most promising positioning technologies for road vehicles and ITS applications. However, previous research has shown that Network RTK positioning of a road vehicle has two important limitations: The level of coverage of communication networks to deliver the important Network RTK GNSS correction messages; and the effect of GNSS signal obstruction and multipath. Finding effective solutions to these current barriers, which are preventing the wide adoption of Network RTK GNSS positioning, is seen as a key enabling step for ITS [1]. As shown in previous research [10], Network RTK GNSS positioning can deliver a vehicle positioning accuracy of better than 5cm, and in real world tests this level of accuracy had an availability of between 41% and 45% (depending on the environment). It was also found that the correction information was available via the GSM network for over 80% of the time. In these same tests the total time without any GNSS position solution (Network RTK, DGNSS, or stand-alone) was up to 16% in a motorway environment. The tests were carried out using the Nottingham Geospatial Institute‟s test vehicle (see Fig. 9), using post processed GPS and IMU data, and digital map data, as ground truth. The results showed that Network RTK GNSS positioning was able to provide lane-level positioning accuracy, but the sensitivity of the technique to GNSS signal loss and coverage of the communication network had a significant effect on availability. GNSS outages could be caused simply by passing under a road bridge, and the Network RTK GNSS solution would be lost, although there would continue to be a DGNSS solution for a short period.

NETWORK RTK ACCURACY ASSESSMENT In much more controlled tests to assess the accuracy of Network RTK GNSS positioning on a dynamic vehicle, the Network RTK GNSS receiver was compared to the NGI‟s Applanix POS/RS inertial navigation system (INS). This consists of a NovAtel OEM4 dual-frequency GPS receiver combined with a navigation-grade Honeywell Consumer-IMU [17]. This test was carried out using the NGI roof laboratory, which houses a 200 millimetre gauge rail track that is 120 metres long and in the shape of a pinched obround, running an electrically powered locomotive, shown in Fig. 5. Both the Network RTK receiver and the Applanix POS/RS used the same antenna (Leica AS10), fed separately through a signal splitter. The Network RTK GNSS solution was recorded in real time onto an SD card in NMEA GGA format. The Applanix POS/RS data was recorded and post-processed in a tightly coupled solution (TC) using a continuously operating dual-frequency GNSS receiver base station located inside the rail track circuit. There were no recorded GNSS outages as there is a clear sky view from the roof laboratory.

Fig. 5: The roof lab electric locomotive at the Nottingham Geospatial Institute. The antenna point was also tracked using a Leica TS30 total station, recording observations at 10Hz stamped with GPS time (the 360° reflector prism was located just below the antenna). Although the accuracy of the tracking mode of the total station is not high enough to assess the accuracy of the Network RTK GNSS solution (due to time synchronization issues), it is used to ensure that any gross errors in GNSS observations that could affect both the Network RTK GNSS and Applanix POS/RS solutions did not occur. The rail track has been scanned to produce an accurate 2 millimetre resolution map, and this can also be used to compare the accuracy of the solutions, but is not explored here. The results in Table 1 show that the Network RTK GNSS solution consistently performs to a high accuracy, giving a low standard deviation from the mean in all directions.

Listed are three laps of the rail circuit recorded at different times. There are a small number of epochs that encounter large differences of over 200 millimetres, such as during laps 2 and 3, although these appear to be very short term anomalies, possibly caused by dynamic GNSS signal multipath or delays and message loss in the communication system. The worst absolute accuracy is shown during lap 3, although even in this case, with a mean of 21 millimetres and 99% of the observations lying within 15 millimetres, this solution still delivers a solution within 36 millimetres of the ground truth. 50% of the Network RTK GNSS observations are within 1 millimetre of the mean difference between the two solutions, showing remarkable consistency and precision. Table 1: Comparison of the Tightly Coupled (GPS+IMU) Solution with the N-RTK Solution (use same font in the table). Tightly Coupled Solution minus N-RTK Solution (mm) Easting Northing Height 2D Lap 1 S.D. 0.004 0.006 0.008 0.007 MAX 0.055 0.064 0.067 0.084 MEAN 0.004 -0.002 0.040 0.005 99% 0.008 0.011 0.020 0.014 95% 0.005 0.009 0.014 0.011 90% 0.004 0.007 0.011 0.008 50% 0.000 0.000 0.001 0.000 Lap 2 S.D. 0.014 0.013 0.008 0.019 MAX 0.198 0.185 0.067 0.271 MEAN 0.007 -0.006 0.040 0.009 99% 0.010 0.013 0.020 0.016 95% 0.006 0.009 0.014 0.011 90% 0.005 0.007 0.011 0.009 50% 0.000 0.001 0.001 0.001 Lap 3 S.D. 0.010 0.010 0.010 0.014 MAX 0.197 0.201 0.042 0.281 MEAN 0.009 -0.018 0.007 0.021 99% 0.009 0.012 0.022 0.015 95% 0.007 0.008 0.017 0.011 90% 0.005 0.007 0.014 0.009 50% 0.000 0.000 -0.001 0.001 NETWORK RTK CHALLENGES 1.

Signal Strength

A fundamental aspect of Network RTK GNSS positioning is the delivery of reference station data used in the

processing of the receiver‟s position [15]. Although there are various methods used to deliver this data, the most secure and reliable method involves transmitting raw reference station observations, so that the receiver may perform the calculation of the position with all the possible data. This is shown to provide the highest integrity [18]. The vulnerability here is not the algorithmic method used to transmit the data, but is in fact the communication system, and in three ways:

OpenSignalMaps has found that a 3G service is only available 58% of the time [22]. The UK government published a report in 2011 [20], detailing the extent of 2G and 3G services, and part of the result is shown in Fig. 6. As can be seen, there are areas of the UK that have poor data communication coverage (below 50%), and this would be a significant problem for using Network RTK GNSS positioning for road vehicles. B. Data loss

  

There is no connection between reference and rover receivers There is data loss from the connection There is an inadequate delay in the transmission of the data.

A. Lack of coverage The preferable communication system is to use mobile internet over the GSM/GPRS cell network, which is already well established [19]. The major network operators claim over 99% coverage of the population in the UK, but this does not take into account physical and local conditions such as land and building obstructions, atmospheric conditions, and interference from vegetation and other radio signals.

Continuity tests show that when using GSM/GPRS mobile communications to transfer the Network RTK corrections, the availability was approximately 88% and the connection could be lost after a few hours of continuous use. This can be caused either by SIM cards that use dynamic IP addresses, creating interruptions when renewing the addresses, or where voice data was prioritised on the network [23]. Other research carried out by the NGI has shown that a typical mobile internet connection (a combination of wired public internet and GPRS) suffers from approximately 20% data loss [24]. When the receiver passes from one cell to the next in a cellular network, this is known as a cell handover. This process is managed by the cell network, and not the cellular modem. This handover process is assessed later in the paper, to discover whether there is deterioration in the cell network connection during this time. C. Message delay

Colour

Coverage

Colour

Coverage

>90%

25-50%

70-90%

-113

Rating Excellent Excellent Good Average (workable) Poor (marginal) Very Poor

Fig. 11: The GSM signal strength around the NGI circuit in Nottingham, with the subjective RSSI ratings.

Table 2 details the RSSI observations measured during the signal strength trials around the NGI circuit. The range of values shows the typical maximum and minimum RSSI values experienced by a mobile phone user (other than no signal being received). The signal strength is recorded every 5 metres, in order to achieve a good geographic spread across the area (as opposed to biasing the results with observations recorded whilst the vehicle is stationary). The RSSI observations do not correspond to a typical Gaussian distribution, suggesting that there are external influences on the strength of the signal and the handover between one cell tower and the next. As shown in Fig. 12, there is an increase in the age of correction (AoC) of the messages following a drop in signal strength (RSSI) to approximately -100 dBm. This is visible from the peaks in the age of correction message to over 8 seconds. The graph shows three laps of the NGI circuit, noticeable by the repeated pattern of signal strength. The increase in the AoC occurs at approximately the same geographic location on each lap – an area in the North West of the circuit that suffers from weak signal strength, as seen in Fig. 11. As described by [28], the received signal strength is the sum of the direct and indirect (or reflected) waves, varying with distance between a series of maximum and minimum values. On a moving vehicle, the RSSI will vary with time as it moves between these maximum and minimum values, and is especially complicated in urban areas where there may be no direct waves at all, and waves are propagated by a series of reflections. A moving receiver also suffers from a Doppler shift in the received signal‟s frequency. Table 2: The spread of RSSI observations recorded during the trials around the NGI circuit. RSSI (dBm) -50 -55 -60 -65 -70 -75 -80 -85 -90 -95 -100 -105 -110

No. Obs 79 75 23 171 103 210 134 186 116 201 44 0 0

% Obs 5.9% 5.6% 1.7% 12.7% 7.7% 15.6% 10.0% 13.9% 8.6% 15.0% 3.3% 0.0% 0.0%

During Network RTK positioning, the receiver considers messages older than 10 seconds unusable for a fixed

Network RTK solution, although messages younger than 60 seconds can be used to give an accurate DGNSS solution. So in this scenario, there is a brief occasion during the loop in which the loss of the Network RTK solution is attributable to the weak GSM signal strength. A close inspection of Fig. 12 highlights a slight delay between the drop in RSSI to -100 dBm and the increase in the AoC. This delay needs further analysis, but is assumed to relate to the slower update rate of the ionospheric and tropospheric corrections (10 seconds and 60 seconds respectively). There are also periods of increased AoC that are uncorrelated with a drop in RSSI, for which there is no clear explanation, although none of these occasions results in a loss of the fixed ambiguity Network RTK GNSS solution.

Fig. 12: The effect of GSM RSSI on the age of correction messages. There were 80 cell handovers recorded during the trials, which is higher than average as this area is liable to carry a large volume of cellular traffic (there is a University, a large hospital, and major roads, as well as general housing and business properties). The cell handovers showed an average improvement of +1.2 dBm from just before the handover until just after. The maximum improvement is +22 dBM, although there are occasions when the RSSI gets worse, the biggest fall in received signal strength being -12 dBM. Fig. 13 displays the frequency distribution of the change in RSSI during a cell handover. Note that the resolution of the RSSI measurements is 2 dBm. Cell handovers occur at a range of RSSI, not just low signal strength. This suggests that cell handovers are managed by the network operator in a way that does not disrupt the data connection. There appears to be no correlation between a cell handover and a problem with the correction message delivery.

bridge. At 60 mph this translates into a distance of almost 130 metres without any GNSS solution, which is much further than the width of the overhead object. Once the GNSS signal is reacquired, there is a short period during which the fixed integer ambiguity is resolved, in order to achieve the centimetre-level accuracy. The longest duration between the start of a GNSS outage and the reacquisition of the fixed ambiguity for the Network RTK solution is 52.10 seconds, or approximately 1,450 metres. Although during this period, a DGNSS solution is available as soon as the satellites are reacquired. DISCUSSION Fig. 13: Frequency histogram of the RSSI change during a cell handover (2 dBm bins). Although this part of the experiment was not a test of the receiver performance, during the NGI circuit trial 63.1% of the receiver observations were Network RTK fixed, and 33.0% of the observations were DGNSS observations. Therefore, 3.9% of the possible epochs had no observations, partly due to passing under bridges. The largest GNSS outage during the NGI circuit trials was 4.85 seconds. These values show an improvement over previous research, particularly as this is considered a difficult GNSS positioning environment. 2.

GNSS outages

During the GNSS outages tests, the vehicle travelled at a constant speed of 60 mph, mostly in lane 1 of the motorway. Table 3 shows the statistical breakdown of the GNSS outages and the resulting reacquisition of the fixed ambiguity in Network RTK positioning. Table 3: Statistical breakdown of GNSS outages caused by overhead objects. Obstacle Footbridge

Road bridge

Gantry

Outage

The nationwide adoption of mobile internet services by mobile phone users has provided a useful communication system for positioning systems. However, the network providers do not guarantee the type of communication service demanded by advanced ITS and V2X applications. The quality of service is too easily disrupted by passing into an area with weak signal strength, or when there are many users congesting the bandwidth. Future generations of mobile networks, such as 4G, will significantly increase the available bandwidth and increase download speeds, but there is an unknown increase in the demand of the system from non-critical mobile phone users. The issues in the existing system can minimised slightly through improvements at the user end, such as using stronger gain antennae or accessing multiple networks with different SIM registrations. The nature of cell networks also leads to a decrease in signal strength occurring prior to the cell handover, which can cause delays in the message delivery, so the management of this process could be improved. Future testing of the GSM network can be carried out at the new innovITS ADVANCE test facility at MIRA in the UK, as shown in Fig. 14. Here the private network can be controlled and manipulated as desired.

Re-fix

(s)

(m)

(s)

(m)

Mean

0.79

21.9

9.78

273.0

Max Min

1.75 0.00

48.8 0.0

19.75 6.90

551.0 192.5

Mean

1.40

39.1

13.80

385.0

Max

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129.7

52.10

1453.6

Min Mean

0.15 0.28

4.2 7.8

5.10 15.77

142.3 440.0

Max

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29.3

27.55

768.6

Min

0.00

0.0

8.70

242.7

The longest total GNSS outage caused by an overhead obstruction was 4.65 seconds, when passing under a road

Fig. 14: Preliminary GSM RSSI signal strength tests at the innovITS ADVANCE test facility.

An alternative communication method, that has the same wide area coverage of a cell network, is satellite communication. In tests carried out by the University of Nottingham [29], observation of static positions showed 98% of messages were received correctly at a latency of less than 10s. This compares with the High-Speed Download Packet Access (HSDPA) cell network figures of 99.8% and 1.2s. When in a kinematic mode, the satellite communications fared less well. Testing three separate satellite communication systems, problems were encountered with reacquisition, long latency, and static initialization. The results showed that at best 70% of correct messages were received, with a latency of 4.2s, although often over 20s. Digital Audio Broadcasting (DAB) is capable of being used as a future communication method for Network RTK positioning. Compared to traditional VHF and UHF radio communication, it uses the frequency more efficiently and is more robust to degradation [30]. The design of the Leica GS10 receiver is aimed at delivering a very reliable and highly accurate solution. It was not intended for use on vehicles and in dynamic environments. The receiver deals well with multipath, rejecting low strength GNSS signals, allowing the resolution of the integer ambiguity. However, this means that within city environments it may provide fewer solutions than a modern Smartphone, albeit with a much higher accuracy when it does. Recent research has shown that it is possible to increase the speed of the ambiguity resolution, and customize the integrity controls, which would make the resolution process close to instantaneous in certain circumstances [31]. CONCLUSIONS This paper assessed the two major causes of the lack of performance of a Network RTK GNSS receiver for road vehicle positioning: Deterioration in the communication system; and GNSS signal outages. As the mobile communications networks evolve in the UK and other countries, the performance of the Network RTK receiver also improves. In this research it is found that once the RSSI drops to approximately -100dBm, the correction messages suffer from either message loss or message delay that causes the receiver to underperform. The performance of the communication link during a cell tower handover has shown that there is no deterioration in the performance linked to the handover, although cell tower handovers generally occur at the limits of a cell tower‟s coverage, and hence at low signal strengths. The resolution of the fixed integer ambiguity is crucial for the high accuracy solution available with a Network RTK

GNSS receiver. The resolution is relatively fast, typically within two minutes from a cold start, or fewer than twenty seconds from a hot start. During tests on the M1 motorway, passing under an overhead obstruction caused a maximum total GNSS outage of 4.65 seconds, and a maximum time until the ambiguity was resolved of 52.10 seconds. On average, the GNSS outage was 1.14 seconds with an average re-fix time of 13.13 seconds. Until the ambiguity is resolved, the receiver can continue with a DGNSS solution delivering lane-level accuracy. REFERENCES [1] X. Meng, L. Yang, J. Aponte, C. Hill, T. Moore, and A. H. Dodson, “Development of Satellite Based Positioning and Navigation Facilities for Precise ITS Applications,” in 11th International IEEE Conference on Intelligent Transportation Systems, 2008, pp. 962-967. [2] W. Y. Ochieng, K. Sauer, D. Walsh, G. Brodin, S. Griffin, and M. Denney, “GPS Integrity and Potential Impact on Aviation Safety,” Journal of Navigation, vol. 56, no. 1, pp. 51-65, Jan. 2003. [3] International Civil Aviation Organization, “Manual of Required Navigation Performance (RNP),” 1999. [4] “Stopping Crashes Consumer Reports, Apr-2012.

With

Smarter

Cars,”

[5] European Commission, “White Paper: Roadmap to a Single European Transport Area – Towards a competitive and resource efficient transport system.” [6] US Department of Transportation, “Fastlane: The official blog of the US Secretary of Transportation.” [Online]. Available: http://fastlane.dot.gov/2011/07/13/#.UGRKFo1lSP7. [Accessed: Aug-2012]. [7] C. Urmson, “The self-driving car logs more miles on new wheels,” Google Official Blog, 2012. [Online]. Available: http://googleblog.blogspot.hu/2012/08/the-self-drivingcar-logs-more-miles-on.html. [Accessed: Aug-2012]. [8] U. of O. Mobile Robotics Group (MRG), “Bowler Wildcat fully autonomous vehicle,” 2012. [Online]. Available: http://www.robots.ox.ac.uk/~mobile/wikisite/pmwiki/pm wiki.php?n=Main.WildCat. [Accessed: Aug-2012]. [9] US Department of Transportation Research and Innovative Technology Administration (RITA), “DOT

Launches Largest-Ever Road Test of Connected Vehicle Crash Avoidance Technology.” [Online]. Available: http://www.its.dot.gov/press/2012/roadtest_connectedvehi cle_tech.htm. [Accessed: Aug-2012].

[21] BBC, “BBC News 3G mobile data network crowd-sourcing survey,” 2011. [Online]. Available: http://www.bbc.co.uk/news/business-14574816. [Accessed: Aug-2012].

[10] J. Aponte et al., “Evaluating The Performance of NRTK GPS Positioning for Land Navigation Applications,” in Royal Institute of Navigation NAV08 and International Loran Association ILA37, 2008.

[22] OpenSignalMaps, “UK Mobile Notspots Report - August 2011,” 2011. [Online]. Available: http://opensignalmaps.com/reports/uk-august-2011.php. [Accessed: Aug-2012].

[11] “Ordnance Survey OS Net reference station network in Britain,” 2009. [Online]. Available: http://www.ordnancesurvey.co.uk/oswebsite/gps/osnetfre eservices/furtherinfo/updatestatus.html. [Accessed: Aug2012].

[23] J. Aponte, X. Meng, T. Moore, C. Hill, M. Burbidge, and L. G. Limited, “Wireless Delivery,” GPS World, 2009.

[12] C. Rizos and S. Han, “Reference Station Network Based RTK Systems-Concepts and Progress,” Wuhan University Journal of Natural Sciences, vol. 8, no. 2, pp. 566-574, 2003. [13] Leica Geosystems, “SmartNet,” 2011. [Online]. Available: http://smartnet.leicageosystems.co.uk/SpiderWeb/SmartNet/smartnet.html. [Accessed: Jan-2011]. [14] G. Fotopoulos and M. E. Cannon, “An Overview of Multi-Reference Station Methods for cm-Level Positioning,” GPS Solutions, vol. 4, no. 3, pp. 1-10, Jan. 2001. [15] L. Wanninger, “Introduction to NRTK,” in International Association of Geodesy, Working Group 4.5.1, 2004. [16] R. Lorimer, “Still Scratching the Surface,” GPS World, 2010. [17] A. Taha, C. Hancock, G. Roberts, and X. Meng, “GPS and INS for centimeter precision during large GPS outages,” Coordinates, Sep-2009. [18] V. Janssen, “A comparison of the VRS and MAC principles for network RTK,” in IGNSS Symposium 2009, 2009, pp. 1-13. [19] V. Wegener and L. Wanninger, “Communication Options for Network RTK / SAPOS ® Realization,” in The 2nd workshop on positioning, navigation and communication (WPNC’05) & 1st ultra-wideband expert talk (UET’05), 2005, pp. 115-118. [20] Ofcom, “UK Communications Infrastructure Report,” 2011.

[24] L. Yang, C. Hill, X. Meng, and J. Aponte, “Quality evaluation of NRTK correction transmission,” Coordinates. [25] S. Stephenson, X. Meng, T. Moore, A. Baxendale, and T. Edwards, “Precision of Network Real Time Kinematic Positioning for Intelligent Transport Systems,” in European Navigation Conference, 2011. [26] K. Hunt, “RF Signal Tracker Android Application,” Google Play, 2012. [Online]. Available: https://play.google.com/store/apps/details?id=com.hotrod. utility.rfsignaltrackereclair&hl=en. [Accessed: Aug2012]. [27] G. Silberg and R. Wallace, “Self-driving cars : The next revolution,” 2012. [28] J. E. Flood, Telecommunication Networks, 2nd ed. Institution of Engineering and Technology, 1996, p. 518. [29] L. Yang, C. Hill, and T. Moore, “Implementation of Wide-Area Broadcast NRTK on a Communication Satellite Platform,” in Proceedings of the 23rd International Technical Meeting of The Satellite Division of the Institute of Navigation (ION GNSS 2010), pp. 2522 - 2533. [30] C. Gandy, “DAB: an introduction to the Eureka DAB System and a guide to how it works,” 2003. [31] D. Odijk and P. J. G. Teunissen, “Improving the Speed of CORS Network RTK Ambiguity Resolution,” in IEEE PLANS 2010, 2010, pp. 79-84.