Validation of vehicle location estimation based on cellular positioning

Abstract

A method for verifying an estimated location of a vehicle in a vehicle, the method comprising: establishing a radio link to a radio base station (RBS) (150); transmitting downlink data for estimating the vehicle's location from the RBS (150) to the vehicle (100) via a radio link (145); estimating the vehicle's location based on the data; transmitting a location verification request to the RBS (150); receiving a response from the RBS (150) to the transmitted request, the response comprising verifying the location estimation based at least in part on the data transmitted uplink from the vehicle (100) to the RBS (150) via the radio link (145); and verifying the estimated location of the vehicle (100) by comparing the estimated vehicle location with the verified location estimation.

Description

Background Technology

[0001] This disclosure relates to a positioning system suitable for automotive applications. Methods and apparatus for verifying the accuracy of estimated vehicle positions are disclosed.

[0002] Radar transceivers are commonly used in vehicles to monitor the vehicle's surroundings. Automatic cruise control (ACC), emergency braking (EB), advanced driver assistance systems (ADAS), and autonomous driving (AD) are some examples of applications where radar data represents a crucial source of information for vehicle control. Radar transceivers provide information about the vehicle's surroundings in a relative sense, that is, information related to the transceiver's location.

[0003] Global Navigation Satellite System (GNSS) receivers can be used to locate vehicles in a coordinate system. For example, this information can be useful if it is desired to locate a vehicle relative to other objects on a map or location register.

[0004] Cellular positioning is a positioning method that uses signals in a wireless access network for location. Cellular positioning has now become good enough for automotive applications similar to those currently supported by radar systems through the introduction of modern communication systems such as fifth-generation cellular systems (5G) as defined by the 3rd Generation Partnership Project (3GPP).

[0005] The accuracy of these different positioning systems (i.e., radar, GNSS, and cellular positioning) often varies depending on the environment and positioning scenario. For example, it is well known that GNSS-based positioning systems sometimes suffer performance degradation in so-called urban canyon environments with limited sky visibility. It is also well known that some radar systems may suffer performance degradation if there is strong radar signal interference.

[0006] It is necessary to detect the period during which the accuracy of the vehicle positioning system temporarily decreases. Summary of the Invention

[0007] The purpose of this disclosure is to provide a method for verifying estimated vehicle locations.

[0008] This objective is achieved through a method for verifying an estimated vehicle location within the vehicle. The method includes establishing a radio link to a radio base station (RBS). The method also includes obtaining data for estimating the vehicle location and estimating the vehicle location based on that data. The method further includes transmitting a location verification request to the RBS and receiving a response from the RBS to the transmitted request, the response including verification of the location estimate based at least in part on data obtained from uplink transmissions on the radio link from the vehicle to the RBS. The method includes verifying the estimated vehicle location by comparing it with a verified location estimate.

[0009] Therefore, by using a verified location estimate generated by an uplink transmission from the RBS to the RBS, the location estimate available at the vehicle can be verified by means of, for example, GNSS systems, a combination of radar transceivers and digital maps, or cellular positioning methods such as 5G positioning. This provides a mechanism for detecting when one or more positioning systems in a vehicle suffer a temporary performance degradation.

[0010] The processing resources and information available at the RBS can be greater than those available in the vehicle. Therefore, the larger processing resources available at the base station can serve as a backup to ensure the vehicle positioning system does not suffer unexpected performance degradation. For this verification purpose, the latency constraints imposed on the positioning algorithm can also be relaxed, allowing for the use of more advanced algorithms at the RBS.

[0011] According to various aspects, the method includes acquiring data via downlink transmission on the wireless link from the RBS to the vehicle, and estimating the vehicle position based on downlink data including any of the following: angle of arrival (AoA), angle of departure (AoD), time of flight (ToF), time of arrival (TOA), and time difference of arrival (TDOA).

[0012] Vehicle location estimation is then based, at least in part, on cellular positioning methods, where input to the positioning algorithm is transmitted from the RBS to the vehicle via a downlink established over a wireless link between the RBS and the vehicle. With the introduction of 5G networks, the accuracy of this location estimation is expected to be high enough to support a wide range of automotive functions, such as adaptive cruise control (ACC), emergency braking (EB), advanced driver assistance systems (ADAS), and autonomous driving (AD). However, such cellular location estimation may suffer from temporary accuracy degradation. The method disclosed in this paper allows for the detection of such periods of accuracy degradation.

[0013] Depending on the circumstances, the method includes estimating the vehicle's position based at least in part on data obtained from a Global Navigation Satellite System (GNSS) receiver included in the vehicle and / or on radar data obtained from a radar transceiver included in the vehicle. This allows for verification of the GNSS position and detection of unexpected performance degradation in the GNSS system.

[0014] Depending on the circumstances, the estimated vehicle position is associated with a timestamp that indicates the moment corresponding to the vehicle position estimation. The timestamp allows interpolation between position estimates obtained at different times, which is advantageous because comparisons become more precise due to the temporally aligned position estimates.

[0015] According to some aspects, this method involves the RBS estimating the vehicle's position based on data obtained from uplink transmissions over the wireless link from the vehicle to the RBS. This means that the RBS performs the verified position estimation locally, for example, within the RBS's control unit. This reduces latency, which is an advantage.

[0016] According to other aspects, the method involves forwarding data obtained from uplink transmissions to a remote server, whereby the remote server estimates the vehicle's position. This means the remote server bears the computational burden of estimating and verifying the position. Compared to RBS, the remote server may have higher processing power and therefore may be able to execute more advanced localization algorithms, such as evaluating numerous localization hypotheses or large neural networks trained for localization purposes.

[0017] Depending on the context, the verification involves comparing the difference between the estimated vehicle position and the verified position estimate with a pre-determined threshold. This is a low-complexity operation, which is an advantage. Furthermore, the pre-determined threshold can be adjusted based on the scenario and application.

[0018] Depending on the context, this verification involves comparing the difference between the probability density function of the estimated vehicle position and the probability density function of the verified position estimate with a predetermined threshold. This is a more advanced comparison operation that provides more information about the accuracy difference between the vehicle position estimate and the verified position estimate. Thus, the accuracy of the maximum error or a given percentage error associated with the estimated vehicle position can be verified based on the comparison results.

[0019] According to various aspects, this method includes cyclic verification of the estimated vehicle location by RBS scheduling. Therefore, performing cyclic verification in an ordered manner means that any temporary performance degradation in accuracy of the onboard positioning system will be detected as it occurs and will not be allowed to persist indefinitely. An advantage is that the repetition frequency can be adjusted according to the application and driving scenario.

[0020] The objective is also achieved through a method for verifying the estimated location of a vehicle using a radio base station (RBS). This method includes establishing a wireless link to a communication transceiver in the vehicle, obtaining data for estimating the vehicle's location via an uplink from the vehicle to the RBS, and estimating the vehicle's location based on that data. The method also includes transmitting a location verification request to the vehicle, receiving a response from the vehicle to the transmitted request, the response including a verified location estimate generated by the vehicle, and verifying the estimated vehicle location by comparing the estimated vehicle location with the verified location estimate.

[0021] This allows for verification of the RBS estimation of the vehicle position, which is an advantage. As mentioned above, vehicle position estimation can be obtained in various ways and is generally unrelated to RBS position estimation. Using the method disclosed herein, RBS can verify that the position estimation process performed at the RBS is not associated with large errors.

[0022] Depending on the context, the method involves estimating the vehicle's location based on data by executing a localization algorithm on a remote server. Compared to RBS, remote servers can have significantly higher processing power and are therefore potentially capable of executing more advanced localization algorithms, such as evaluating a large number of localization hypotheses.

[0023] Depending on the circumstances, the method includes triggering an event by the RBS if the difference in the verification indication exceeds a predetermined level. This event could be, for example, some form of emergency maneuver of the vehicle or a warning message broadcast to other vehicles nearby.

[0024] This document also discloses control units, vehicles, and radio base stations associated with the aforementioned advantages. The vehicles, radio base stations, and control units disclosed herein are associated with the same advantages discussed above using different methods.

[0025] Generally, unless otherwise expressly defined herein, all terms used in the claims are to be interpreted according to their ordinary meaning in the art. Unless otherwise expressly stated, all references to “an / a / the element, apparatus, component, method, step, etc.” are to be interpreted as referring to at least one instance of an element, apparatus, component, method, step, etc. Unless expressly stated otherwise, the steps of any method disclosed herein need not be performed in the exact order disclosed. Further features and advantages of the invention will become apparent when examining the appended claims and the following detailed description. Those skilled in the art will recognize that different features of the invention can be combined to create embodiments other than those described below without departing from the scope of the invention. Attached Figure Description

[0026] This disclosure will now be described in more detail with reference to the accompanying drawings, in which:

[0027] Figure 1 The vehicle is shown schematically.

[0028] Figure 2 An exemplary cellular positioning system is shown;

[0029] Figure 3 A vehicle with an antenna array is shown;

[0030] Figure 4 An exemplary cellular positioning system is shown;

[0031] Figures 5 to 6This is an example signaling diagram;

[0032] Figures 7A to 7B This is a flowchart illustrating the method;

[0033] Figure 8 The control unit is shown schematically; and

[0034] Figure 9 An exemplary computer program product is shown; Detailed Implementation

[0035] Various aspects of this disclosure will now be described more fully with reference to the accompanying drawings. However, the different devices and methods disclosed herein may be implemented in many different forms and should not be construed as limited to the aspects set forth herein. Throughout the text, the same reference numerals refer to the same elements in the drawings.

[0036] The terminology used herein is for describing aspects of this disclosure only and is not intended to limit the invention. As used herein, the singular forms “a” and “described” are intended to include the plural forms as well, unless the context clearly indicates otherwise.

[0037] This disclosure relates to the verification of an estimated location. The general principle of the method disclosed herein is that a radio base station performs cellular positioning to locate the vehicle. This location estimate is then compared with a location estimate obtained locally in the vehicle based on cellular positioning or some other method, such as positioning using a Global Navigation Satellite System (GNSS) or radar-based positioning. If the two match, the location estimate is considered to have been verified as accurate.

[0038] Figure 1 A vehicle 100 is schematically shown, which is arranged to position itself relative to a certain coordinate system and is also arranged to detect the relative position of an object 125 in the vicinity of the vehicle 100.

[0039] Vehicle 100 includes a radar system 130. This radar system is associated with at least one field of view 131, 132, 133. A front radar is associated with a field of view 131 extending in front of the vehicle, and its range is typically about 150 m to 200 m. Radar system 130 may optionally also include further short-range side radars with fields of view 132 extending laterally to the sides of vehicle 100, and a rear-view radar associated with a field of view 133 covering the area behind the vehicle. The typical range of a corner radar or rear-view radar may be around 80 meters.

[0040] The radar transmission 135 is reflected or scattered by the target 125 and then detected by the radar transceiver 130. The radar transceiver 130 is connected to a central control unit 110 that controls the radar transceiver. This control may include transmission timing, transmission frequency content, and the actual transmission time waveform.

[0041] Vehicle 100 can perform global localization using its radar transceiver 130 and control unit 110 by matching the images seen by the radar with a database or a map of the surrounding environment. For example, if three or more objects are detected at ranges d1, d2, and d3, and these objects can be identified on the map, the vehicle's position on the map can be inferred through triangulation. In this way, the position of vehicle 100 in the coordinate system can be obtained.

[0042] In this paper, a coordinate system is a common reference system defined for at least some local areas. An example of a coordinate system is the well-known World Geodetic System 1984 (WGS-84), and of course, other examples exist. Special coordinate systems can also be defined as needed, simply by defining multiple anchor points at explicitly defined locations within the coordinate system. Coordinate systems are typically three-dimensional, but they can also be two-dimensional.

[0043] Vehicle 100 optionally includes a GNSS receiver 140, which allows direct positioning of vehicle 100 in a coordinate system (such as the WGS-84 system) based on GNSS radio signals 185 received from one or more satellites 180. Despite recent advancements in centimeter-level accuracy in global navigation satellite systems (such as multi-band receivers, multi-constellation receivers, and new correction schemes), receivers (user equipment (UE)) still rely heavily on, for example, the line-of-sight (LOS).

[0044] Control unit 110 is connected to a communication transceiver 120 included in vehicle 100. This transceiver is arranged to establish a wireless communication link 145 to a radio base station (RBS) 150. In this document, an RBS is an access point that provides wireless access to some communication network. An example of an RBS is a 4G eNodeB and a 5G gNodeB as defined by 3GPP. RBS 150 is part of a communication network 160 that includes one or more nodes and / or remote servers 170, via which communication links can be established from vehicle 100 to these nodes and / or remote servers. An RBS typically includes control unit 155 arranged to perform various operations, such as performing calculations and performing positioning. The processing power of remote server 170 may be much greater than that of RBS 150. For example, a remote server may be able to perform signal processing algorithms based on evaluations of a variety of different hypotheses and / or perform extremely complex artificial intelligence algorithms (such as large neural networks with many layers).

[0045] Location based on radio access signals (145) is already well-established. For example, in 2001, the U.S. Federal Communications Commission (FCC) began requiring cellular operators to be able to locate wireless devices with an accuracy of approximately 100 meters. This accuracy is clearly insufficient for automotive applications (such as ADAS and AD) that are traditionally supported by radar systems and, in some cases, by vision-based systems. However, initiatives such as 3GPP have recently explored the possibility of using cellular access systems for highly accurate sub-meter location of wireless devices (such as transceivers 120). For example, 3GPP TR 22.872 V16.1.0 (2018-09) provides research on location use cases. For example, the improved location accuracy of 5G cellular access systems stems from increased bandwidth and the more advanced antenna systems deployed in 5G systems. These advanced antenna systems include antenna arrays, sometimes with a large number of antenna elements. Antenna arrays with a large number of antenna elements are often referred to as massively multi-input multiple-output (MIMO) antenna systems. Such antenna systems are capable of estimating both the angle of arrival and the angle of departure of the received and transmitted radio signal components separately.

[0046] For example, suppose x represents a position in a coordinate system, i.e., a set of coordinates. Further assume that a measurement result r = f(x) + n is available, where r is a function of x, and there exists some form of noise or interference n. Both r and x are typically vectors.

[0047] The least squares estimator of x obtained from r is

[0048]

[0049] The maximum likelihood estimate of x obtained from r is

[0050]

[0051] Here, p(r|x) is the likelihood of observing r given x. Of course, other exemplary localization methods also exist. For example, localization can be performed by jointly locating scattering objects and vehicles based on measurements of radio propagation in the environment. The algorithm for positioning repeaters and point scatterers is discussed by the authors in “An Algorithm for Positioning Relays and Point Scatterers in Wireless Systems” (IEEE Signal Processing Letters 1070-9908 (ISSN) 2008, Vol. 15, pp. 381-384).

[0052] Figure 2An example 200 of a vehicle 100 connected to an RBS 150 is shown. The RBS 150 includes an advanced antenna system 210 with multiple antenna elements. Using this advanced antenna arrangement, the RBS 150 is able to determine the angle of arrival (AoA) 240 of a signal component 220 arriving at the RBS from a communication transceiver 120 in the vehicle 100. The RBS is also able to determine the angle of departure (AoD) 250 of a signal component 230 transmitted from the RBS 150 toward the vehicle 100.

[0053] Based on the AoA and / or the AoD, and the signal delay or time of flight between RBS 150 and vehicle 100, the vehicle's position can be determined in a known manner (e.g., based on the least squares method or the maximum likelihood principle described above). The determination of the vehicle's position can be performed by, for example, a control unit in RBS 155 or a control unit 110 in the vehicle.

[0054] Figure 3 Another example 300 of vehicle 100 is shown, which includes an advanced antenna array 310, such as an antenna configured for massive MIMO operation. This onboard antenna array allows for the determination of the AoA 340 of the signal components on the uplink 320 from vehicle 100 to RBS and the AoD 350 of the signal components on the downlink 330 from RBS 150 to vehicle 100.

[0055] Radio signals do not always travel from the transmitter to the receiver along a direct line-of-sight (LoS) path. Figure 4 An exemplary scenario 400 is illustrated, in which uplink and downlink transmissions are carried through LoS path 410, and also through two indirect paths. One such signal component 420 passes through an obstacle (such as a wall or other reflective surface 430). Another such signal component 440 passes through a point scatterer (such as a lamppost). The scatterer 450 may also be a repeater configured to amplify and relay radio signals.

[0056] In this paper, a signal component is a part of a radio signal. A signal component can be, for example, a signal traveling along a given path in a multipath propagation radio channel. Different signal components can correspond to copies of the same transmitted signal that have traveled different lengths of path and are therefore associated with different delays.

[0057] A radio propagation channel can be associated with impulse responses describing different propagation paths of the radio channel. These impulse responses consist of pulses with varying delays and phases. Each such pulse generates a signal component.

[0058] The system can use scattering objects 430 and 450 to improve positioning performance. In fact, this is one of the key fundamental reasons why 5G-based positioning systems are expected to outperform previous cellular positioning systems. Witrisal and Antón-Haro (eds) in "Whitepaper on New Localization Methods for..."

[0059] The paper "5G Wireless Systems and the Internet-of-Things" (COST CA15104 (IRACON); April 2018) provides an overview of novel positioning methods. These methods primarily rely on measurements such as signal strength (which is roughly related to the distance the signal has traveled and the reflections it has experienced), as well as delay and angle, as described above. Figure 2 and Figure 3 As shown. Delay measurements can be, for example, time of flight (ToF), two-way time of arrival (TOA), and / or time difference of arrival (TDOA). Some systems also utilize the Doppler shift, which indicates the relative (typically radial) velocity of the transmitter and receiver in the case of a Loss of Speed ​​(LoS) path, to which the signal components have undergone. These types of measurements are known and will not be discussed in detail here.

[0060] Many localization methods have been proposed for cellular systems. Some examples are the maximum likelihood (ML) based estimators and the least squares (LS) based estimators mentioned above. Maximum a posteriori (MAP) based estimators have also been proposed.

[0061] The accuracy of a cellular positioning system typically depends on the characteristics of the radio channel between the transmitter and receiver (e.g., between vehicle 100 and RBS150). Numerous reflections can be beneficial in some cases but not in others. For some systems, the presence of a strong Loss of Sight (LoS) path can also be advantageous.

[0062] In summary, vehicle 100 can obtain information about its position in a given coordinate system or reference frame in a variety of different ways, such as via its communication transceiver 120 for cellular positioning, via its radar transceiver 130 (supplemented by maps or other location registers), and / or via GNSS receiver 140. All of these systems may suffer temporary performance degradation. Cellular positioning, operating via communication transceiver 120, can depend on, for example, the characteristics of the radio channel to provide robust, high-accuracy position estimation. Radar systems may suffer performance degradation due to interference from other radar transceivers and / or inaccuracies in map data used to locate the vehicle in the coordinate system based on locally acquired radar data. GNSS systems are known to be inaccurate in urban environments with limited sky visibility and strong multipath signal components. Therefore, the accuracy of different systems varies over time, and it is desirable to be able to verify that the current accuracy meets the requirements of specific functions.

[0063] As described above, this disclosure relates to verifying vehicle location estimates using a cellular network. An estimate is generated in the vehicle, for example, by the vehicle control unit 110 using one or more on-board vehicle positioning systems. Then, another, at least partially unrelated, estimate of the vehicle location is estimated at the RBS by the RBS control unit 155 or a remote server 170. The two estimates are then compared to verify that they at least adequately match. Large discrepancies indicate problems related to positioning accuracy.

[0064] According to the first embodiment, vehicle 100 locates itself by, for example, using radar, GNSS, and / or downlink signaling-based cellular positioning, thereby estimating its position in a coordinate system. Vehicle 100 then requests the cellular network to perform a position orientation of vehicle 100 using signals on uplinks 220, 320 from vehicle 100 to RBS 150. The two position orientations are then compared to verify a match. If there is a large discrepancy, there is a risk that the position estimated by vehicle 100 is associated with a large error. Factors constituting a “large” discrepancy typically depend on the scenario. For example, a threshold may be defined based on the driving scenario and / or application, and the difference between position estimates (in the Euclidean sense or squared error) may be compared to the threshold.

[0065] Figure 5 Example 500 of this first embodiment is shown, where the down arrow represents time t, and where the block represents processing for a given time period. The processing time period is shown only schematically. The vehicle control unit 110 first issues a position verification request 501 to the RBS control unit 155. Figure 1The request is transmitted via wireless link 145, as shown. Vehicle 100 and RBS 150 then collect data 502, 504, such as signal component delay and angle, and estimate the position 503, 505 of vehicle 100 based on the collected data. Thus, the vehicle's position is estimated in two at least partially different ways. Of course, the collected data may also include data from other sources, such as GNSS position data, radar data, and data from digital maps. At vehicle control unit 110, the two or more position estimates are compared 507, and any discrepancies can then be assessed. Timestamps may optionally be attached to the position data to enable interpolation, etc.

[0066] The RBS control unit 155 can also forward data 508 to processing resources, such as a server 170 in a communication network. The remote server 170 then undertakes the computation 509 and delivers the result 510 as a location estimate back to the RBS control unit 155, which relays the result 511 to the vehicle control unit 110. The vehicle can then compare two or more results 512 to see if there are discrepancies or if the differences are within acceptable limits.

[0067] It should be understood that, compared to the RBS control unit 155 and the vehicle control unit 110, the server 170 may include additional computing resources and thus can execute more advanced and computationally demanding positioning algorithms. Therefore, additional processing power is available by sending measurement data to the remote server 170. This increased processing power can be used, for example, to evaluate a larger number of hypotheses or to run large artificial intelligence architectures such as large neural networks with many layers, which might be too large to execute in the RBS control unit 155 or the vehicle control unit 110.

[0068] Figure 7A A flowchart summarizing the above discussion related to the first embodiment is shown. See also: Figure 5 The diagram illustrates a method 500 for verifying the estimated position of vehicle 100 within vehicle 100. The position of vehicle 100 is estimated in a coordinate system. The estimated position can be a single coordinate or a set of coordinates, such as a bounding box. The vehicle's position may also include the heading, attitude, roll, and yaw of vehicle 100. Furthermore, the estimated position of vehicle 100 can be a trajectory describing the movement of vehicle 100 over time. The method includes establishing a wireless link 145 from Sa1 to RBS 150. The above is combined with examples... Figure 1The link is discussed. The link to the RBS can be, for example, a 5G cellular system link to the RBS or gNodeB operating according to the specifications of the relevant 3GPP standards. The method also includes obtaining Sa2 data 502 for estimating the vehicle's location, and then estimating the vehicle's location Sa3 based on that data 503. This vehicle location estimation can be based on any of a radar system, GNSS system, or cellular positioning system. (The above is combined with...) Figures 2 to 4 This paper discusses estimating vehicle location based on downlink transmission from the RBS to the wireless device. This estimation can be performed in several different ways, such as by combining... Figure 4 The object discussed is based on point scattering.

[0069] The method also includes transmitting an Sa4 location verification request 501 to the RBS 150. This request is transmitted from the vehicle when it wants to verify whether its current location concept is correct. The request can be transmitted if a large error is suspected, or it can be transmitted periodically according to some schedule. In denser traffic scenarios where high "guaranteed" accuracy is more important, such as in traffic-heavy urban environments, the request may be transmitted more frequently. In suburban environments where the vehicle can provide greater margin for other objects in the traffic environment, the frequency of request transmission may be lower.

[0070] Furthermore, the method includes receiving a response Sa6 from RBS 150 in response to the transmitted request. This response includes verifying the location estimate based at least in part on data obtained from uplink transmissions 220, 320 on the radio link 145 from vehicle 100 to RBS 150. The method then performs verification Sa7 of the estimated vehicle location by comparing the estimated vehicle location with the verified location estimate 507. Thus, RBS 150 generates an estimate of the vehicle location based on radio transmissions on the uplink of radio link 145. This data may differ from data obtained from downlink transmissions, and the estimated location may therefore be at least in part unrelated to the location estimate determined by the vehicle.

[0071] According to some aspects, the method includes estimating the Sa5 vehicle position by the RBS based on data obtained by uplink transmissions 220, 320 on the radio link 145 from vehicle 100 to RBS 150. This data may, for example, include data obtained from a large antenna array.

[0072] Verification can be performed by comparing two estimates of the vehicle's position. If the two estimates match, then both estimates are likely correct. However, if there is a discrepancy, one or both may be associated with a large error, in which case some action may be necessary. Such discrepancies can, for example, trigger a safe stopping maneuver of the vehicle or issue a warning signal to the driver using an ADAS system. This type of detected discrepancy can also be used to prevent, for example, the activation of AD modes in the vehicle. In this case, verification can be triggered before the AD or ADAS mode is activated, and it is performed periodically according to a predetermined schedule as long as the AD or ADAS mode is enabled. If verification fails, activation of the AD or ADAS system can be prevented.

[0073] According to some aspects, the method includes obtaining Sa21 data via downlink transmissions 230 and 330 on a radio link 145 from RBS 150 to vehicle 100, and estimating the vehicle position based on downlink data including any of the following: angle of arrival (AoA), angle of departure (AoD), time of flight (ToF), time of arrival (TOA), and time difference of arrival (TDOA). The above is combined with... Figures 2 to 4 This type of data has been discussed. Location methods based on these types of data are known, therefore they will not be discussed in detail here.

[0074] According to some aspects, the method includes estimating the Sa31 vehicle position 503 based on data obtained from a Global Navigation Satellite System (GNSS) receiver included in vehicle 100 and / or based on radar data obtained from a radar transceiver 130 included in vehicle 100. Vehicle position estimation can be improved by combining sensor fusion techniques with additional information sources, such as GNSS position data and environmental data obtained from the radar system of vehicle 100.

[0075] According to some aspects, the estimated vehicle position is associated with a timestamp Sa32, which indicates the moment corresponding to the vehicle position estimation. In other words, the timestamp indicates the time when the vehicle was estimated to be located at that position. This timestamp allows comparison routines 507, 512 to interpolate between two or more estimated positions, for example, using motion information associated with the vehicle, thereby aligning the estimated vehicle position temporally with the verification position estimate. Various methods can be used for interpolating between position estimates, such as Kalman filtering and particle filtering techniques. Such techniques are known and will not be discussed in detail herein.

[0076] According to some aspects, the method also includes forwarding data obtained from the uplink transmission Sa51 to a remote server 170, where the remote server 170 estimates the vehicle's position. Compared to the RBS control unit 155, this remote server may have more processing power as described above. Therefore, more advanced positioning algorithms can be executed on the remote server 170. The execution of such more advanced algorithms may take some time, and communication with the remote server 170 may also add some latency. However, since this is a verification operation rather than a real-time control operation, such latency is acceptable. While the remote server processes the more advanced algorithms to deliver the second verification results 510, 511, the RBS can provide verification results more quickly.

[0077] According to some aspects, the verification involves comparing the difference between the estimated vehicle position and the verified position estimate with a predetermined threshold. This is a simple verification operation that includes, for example, determining the Euclidean distance or squared norm between a first vector representing the vehicle position estimate and another vector representing the verified position estimate. The distance between the two vectors can then be compared with a threshold, and if the distance is greater than the threshold, a difference has been detected.

[0078] Of course, more advanced comparison methods can also be used. For example, depending on some aspects, the verification involves comparing the difference between the probability density function of the estimated vehicle position and the probability density function of the verified position with a predetermined threshold. The distance between the two probability distributions can be quantified, for example, by the Kullback-Leibler distance. Percentage error can also be used as input for the comparison.

[0079] According to some aspects, the method also includes cyclic verification of the estimated vehicle position by Sa8 scheduled by RBS 150. As mentioned above, the schedule can be adjusted according to the current driving scenario. For example, if the space margin is small and the driving scenario is compact (such as at a busy urban intersection), the verification operation may be requested more frequently compared to a suburban scenario where a larger margin is available for other road users and objects in the environment. The schedule can also be adjusted based on the functions currently performed by vehicle 100. For example, verification can be scheduled more frequently when AD or ADAS functions are enabled compared to when AD or ADAS functions are disabled. If verification fails at some point in time, activation of AD or ADAS functions can be prevented.

[0080] According to the second implementation scheme, the verification is performed in the opposite manner, wherein RBS requests verification from vehicle 100. Figure 6Example 600 of this second embodiment is shown. The RBS control unit 155 (or network server 170) now collects data 602 and estimates 603 the position of vehicle 100. The RBS control unit 155 requests a position estimate from vehicle 100 601 to verify its estimation result. Then, vehicle control unit 110 collects data 604 and performs a position estimate 605, which is reported 606 back to the RBS. The position estimate performed by vehicle 100 can be based on any device available in vehicle 100 for locating itself, such as radar, GNSS, or transceiver-based (cellular) positioning. The RBS control unit 155 can then compare the results 607. Again, if there are large discrepancies, there is a risk that at least one of the position estimates is associated with a large error.

[0081] It should be understood that messages can be represented in different ways and may include different types of information. For example, a response to a location request may include a location estimate based on coordinates, statistical characteristics of the location (such as a probability density function for the location estimate), or measurement data required to determine the location estimate. The response is again preferably associated with a timestamp or other indication related to the time corresponding to the vehicle's location.

[0082] Figure 7B A flowchart summarizing the above discussion related to the second implementation scheme is shown. It should be understood that the two implementation schemes can be freely combined, and the methodological steps of the two methods can be interchanged between the two methods. Figure 7B Also refer to Figure 1 , Figure 2 , Figure 3 and Figure 6 A method 600 for verifying the estimated position of vehicle 100 in a radio base station (RBS) 150 is illustrated. The method includes establishing a radio link 145 from vehicle 100 to a communication transceiver 130. The method also includes obtaining data 602 from vehicle 100 to the radio link 145 of the RBS 150 for estimating the vehicle position, specifically data 602 for Sb2. The method further includes estimating the vehicle position 603 based on this data. Therefore, a first position estimate of the vehicle position is obtained at the base station based on data transmitted from the uplink on the radio link 145. The position of vehicle 100 is estimated in a coordinate system. The estimated position can be a single coordinate or a set of coordinates, such as a bounding box. The vehicle position may also include the heading, attitude, roll, and yaw of vehicle 100. Furthermore, the estimated position of vehicle 100 may be a trajectory describing the movement of vehicle 100 over time.

[0083] The method also includes transmitting an Sb4 location verification request 601 to vehicle 100 and receiving an Sb6 response 606 from vehicle 100 in response to the transmitted request, the response including a verified location estimate generated by vehicle 100. This verified location estimate is based on data available to vehicle control unit 110, such as GNSS and radar data. The verified location estimate may also be based on downlink data transmitted from the RBS to the vehicle, which vehicle control unit 110 may use to generate the verified location estimate using an algorithm different from the one used by RBS control unit 155. Therefore, it is expected that the two estimates will be at least partially uncorrelated.

[0084] Then, the estimated position of vehicle Sb7 is verified by comparing the estimated vehicle position with the verified position estimate 607, thereby detecting errors in the position estimation. Thus, in a manner similar to the first embodiment, errors in the position estimation can be detected by comparing the two. As long as the error is within acceptable limits, the probability of a large error is small. However, if the difference between the two exceeds an acceptable amount, measures may need to be taken, such as performing a safe stopping maneuver or issuing a warning signal to the driver using an ADAS system. Therefore, according to some aspects, the method includes triggering an Sb8 event by the RBS if the verification indication difference exceeds a predetermined level.

[0085] According to other aspects, the method includes estimating the vehicle position 603 of Sb31 based on data by executing a positioning algorithm on a remote server 170. This remote server may include more powerful computing resources than the control unit 1545 at the RBS, and therefore may be able to execute more advanced positioning algorithms than the RBS control unit 155. Therefore, it is advantageous that the remote server 170 may sometimes execute advanced algorithms (such as, for example, a multi-hypothesis tracking algorithm) to determine whether the vehicle position estimate is consistent with the most likely hypothesis or follows a less likely hypothesis. Of course, the results of the verification process can be communicated to the vehicle 100 via a wireless link 145.

[0086] As described above, the method may include triggering an Sb8 event by RBS if the verification indication difference is higher than a predetermined level.

[0087] Figure 8Components of a control unit 800 according to an embodiment of the present disclosure are schematically shown in the form of multiple functional units. The control unit 800 may, for example, correspond to a vehicle control unit 110, an RBS control unit 155, or a remote server 170. Processing circuitry 810 is provided using any combination of one or more of a suitable central processing unit (CPU), multiprocessor, microcontroller, digital signal processor (DSP), dedicated hardware accelerator, etc., capable of executing software instructions stored in a computer program product (e.g., in the form of storage medium 830). Processing circuitry 810 may also be provided as at least one application-specific integrated circuit (ASIC) or field-programmable gate array (FPGA).

[0088] Specifically, the processing circuit 810 is configured to cause the control unit 800 to perform a set of operations or steps. These operations or steps have been discussed above in conjunction with various radar transceivers and methods. For example, the storage medium 830 may store the set of operations, and the processing circuit 810 may be configured to retrieve the set of operations from the storage medium 830 to cause the control unit 800 to perform the set of operations. The set of operations may be provided as a set of executable instructions. Thus, the processing circuit 810 is thereby arranged to perform the methods and operations disclosed herein.

[0089] The storage medium 830 may also include a permanent storage device, which may be any or a combination of magnetic storage, optical storage, solid-state storage, or even remotely mounted storage.

[0090] The control unit 800 may also include a communication interface 820 for communicating with at least one other unit. Therefore, the interface 820 may include one or more transmitters and receivers, which include analog and digital components and a suitable number of ports for wired or wireless communication.

[0091] The processing circuitry 810 is adapted to control the general operation of the control unit 800, for example, by sending data and control signals to external units and storage medium 830, by receiving data and reports from external units, and by retrieving data and instructions from storage medium 830. Other components and related functions of the control unit 800 are omitted to avoid obscuring the concepts presented herein.

[0092] Figure 9 A computer program product 900 is shown, comprising computer-executable instructions 920 arranged on a computer-readable medium 910 to perform any of the methods disclosed herein.

Claims

1. A method for verifying the estimated position of a vehicle in a vehicle, the method comprising: Establish a wireless link to the radio base station. Obtain data for estimating the vehicle's position. The vehicle's location is estimated based on the data. Transmit a location verification request to the radio base station. Receive a response from the radio base station in response to the transmitted request, the response including at least in part a verified location estimate based on uplink transmissions on the radio link from the vehicle to the radio base station, and the verified location estimate being at least partially unrelated to the vehicle location estimate. The estimated vehicle position is verified by comparing the estimated vehicle position with the verified position estimate.

2. The method of claim 1, wherein the method comprises obtaining the data via downlink transmission from the radio base station to the vehicle on the wireless link, and estimating the vehicle position based on downlink data including any one of angle of arrival, angle of departure, time of flight, time of arrival, and time difference of arrival.

3. The method of claim 1, wherein the method comprises estimating the vehicle position based at least in part on data obtained from a Global Navigation Satellite System receiver included in the vehicle and / or on radar data obtained from a radar transceiver included in the vehicle.

4. The method according to claim 1, wherein, The estimated vehicle location is associated with a timestamp that indicates the time corresponding to the estimated vehicle location.

5. The method of claim 1, wherein the method includes estimating the vehicle location by the radio base station based on data obtained from uplink transmissions on the wireless link from the vehicle to the radio base station.

6. The method of claim 5, wherein the method includes forwarding the data obtained from the uplink transmission to a remote server, and the remote server estimating the vehicle location.

7. The method according to claim 1, wherein, The verification includes comparing the difference between the estimated vehicle location and the verified location estimate with a predetermined threshold.

8. The method according to claim 1, wherein, The verification includes comparing the difference between the probability density function of the estimated vehicle location and the probability density function of the verified location with a predetermined threshold.

9. The method of claim 1, wherein the method includes cyclic verification of the estimated vehicle location scheduled by the radio base station.

10. A vehicle control unit, the vehicle control unit including processing circuitry configured to perform the method according to any one of claims 1 to 9.

11. A vehicle comprising a vehicle control unit according to claim 10.

12. A method for verifying the estimated location of a vehicle in a radio base station, the method comprising: Establish a wireless link to the communication transceiver in the vehicle. Data for estimating the vehicle's location is obtained from the uplink of the wireless link from the vehicle to the radio base station. The vehicle's location is estimated based on the data. A location verification request is transmitted to the vehicle. Receive a response from the vehicle to the transmitted request, the response including a verified location estimate generated by the vehicle, and the verified location estimate being at least partially unrelated to the vehicle location estimate. The estimated vehicle position is verified by comparing the estimated vehicle position with the verified position estimate.

13. The method of claim 12, wherein the method includes estimating the vehicle location based on the data by performing a positioning algorithm on a remote server.

14. The method according to any one of claims 12 or 13, wherein the method comprises triggering an event by the radio base station when the verification indication difference is higher than a predetermined level.

15. A radio base station control unit, the radio base station control unit including processing circuitry configured to perform the method according to any one of claims 12 to 14.

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