WO2025223789A1

WO2025223789A1 - Routing in an off-road area

Info

Publication number: WO2025223789A1
Application number: PCT/EP2025/058713
Authority: WO
Inventors: Dibyendu Palai; Bugra Turan
Original assignee: Jaguar Land Rover Ltd
Current assignee: Jaguar Land Rover Ltd
Priority date: 2024-04-23
Filing date: 2025-03-31
Publication date: 2025-10-30
Anticipated expiration: 2026-10-23
Also published as: GB2640522A; GB202405664D0

Abstract

Aspects of the present invention relate to a computer-implemented method (100) for a routing system for a vehicle (600), to computer readable instructions, to a control system (10), to a system, and to a vehicle (600). The method (100) comprises receiving vehicle sensor information corresponding to an off-road area; determining, in dependence on the vehicle sensor information, a navigable route through the off-road area; determining, in dependence on the identified navigable route, route context data relating to the navigable route, wherein the route context data comprises classifying characteristics of the navigable route; and storing the navigable route and associated route context data in a data store. The route context data may be used to classify and label the available navigable routes. The navigable route and route context data may be updated as data from more journeys in the off-road area becomes available.

Description

ROUTING IN AN OFF-ROAD AREA

TECHNICAL FIELD

The present disclosure relates to routing in an off-road area. Aspects of the invention relate to a computer- implemented method for a routing system for a vehicle, to computer readable instructions, to a control system, to a system, and to a vehicle.

BACKGROUND

It is known for vehicles, particularly off-road vehicles, to travel in off-road areas. However, driving in such areas is often unpredictable. Such off-road areas typically have indistinctly bounded terrain and apparent routes that are not in fact driveable. An apparent route may have surface conditions, topology, and obstacles, such as fallen trees, that may prevent the route from being driveable.

Even on routes that are driveable, difficulties may exist that make the journey unpleasant. For example, the terrain may be more difficult to traverse due to recent conditions, or may be very rutted, leading to a very bumpy journey for the occupants of the vehicle.

It is an aim of the present invention to address one or more of the disadvantages associated with the prior art.

SUMMARY OF THE INVENTION

Aspects and embodiments of the invention provide a computer-implemented method for a routing system for a vehicle, computer readable instructions, a control system, a system, and a vehicle as claimed in the appended claims.

According to an aspect of the present invention there is provided a method for a routing system for a vehicle the method comprising determining, in dependence on sensor information corresponding to an off-road area, a navigable route through the off-road area; determining route context data relating to the navigable route, wherein the route context data relates to characteristics of the navigable route; and storing the navigable route and associated route context data in a data store. In this way, a smart routing method is provided for off-road driving, where the routing may be customized in a variety of ways.

According to an aspect of the present invention there is provided a computer-implemented method for a routing system for a vehicle. The method comprises receiving vehicle sensor information corresponding to an off-road area; determining, in dependence on the vehicle sensor information, a navigable route through the off-road area; determining, in dependence on the identified navigable route, route context data relating to the navigable route, wherein the route context data comprises classifying characteristics of the navigable route; and storing the navigable route and associated route context data in a data store. In this way, a route and details about that route may be compiled for users who wish to traverse the off-road area. The route and associated details may include up-to-date information about surface and topology variations due to weather conditions. Such information is not available from maps, thus, the invention provides improved information than is available from traditional sources. The method may also be used reduce environmental impact in relevant areas. The vehicle sensor information may comprise information obtained from one or more sensors of a vehicle, including perception sensors and inertial sensors. In this way, the vehicle sensor information may comprise vehicle perception sensor information and vehicle inertial sensor information. Example perception sensors include camera, radar, Lidar, and ultrasonic sensors. Such sensors may be included in an Advanced Driver Assistance System (ADAS) of the vehicle. Example inertial sensors may include gyroscope, accelerometer, and magnetometer. Such sensors may be included in an Inertial Measurement Unit (IMU) of a vehicle.

In an embodiment, the method may comprise receiving further vehicle sensor information corresponding to the off-road area; determining, in dependence on the further vehicle sensor information, an updated navigable route; and storing the updated navigable route in the data store. In this way, if there has been a change in the terrain such as a fallen tree, mudslide, deterioration in surface, etc. which may lead to a change in the route, this information may be noted and acted upon.

Optionally, the method comprises receiving further vehicle sensor information corresponding to the off-road area; determining, in dependence on the further vehicle sensor information, updated route context data; and storing the updated route context data in the data store. In this way, if there has been a change in the terrain such as a deterioration in surface, etc. which may lead to a change in the characteristics of the route, such as the journey time, energy consumption, comfort of the vehicle occupants or the like, this information may be noted and acted upon.

In an embodiment, the method may comprise outputting at least one of the navigable route, associated route context data, updated navigable route, and the updated route context data, to a vehicle. In this way, the data determined by the method may be shared with a vehicle that may traverse or is traversing the route.

Optionally, the method comprises receiving a notification when a vehicle travels the identified route and updating a route usage counter for the navigable route in dependence on the notification. In this way, the most popular routes may be identified. If sensor data is received from some or all of the journeys along route, the accuracy of the route and route context data can be maintained. The method may comprise receiving a timestamp of the notification. In this way, the most recently received data is identifiable. The validity of the route at that time is then known. Furthermore, if sensor information is received from that journey, the method may be able to provide an updated route and updated route context information from a known time period.

In an embodiment, the method may comprise prioritising outputting the updated navigable route, and the updated route context data in dependence on the route usage counter and timestamp.

Optionally, receiving vehicle sensor information corresponding to an off-road area comprises receiving vehicle sensor information corresponding to an off-road area from a plurality of journeys through the off-road area. In this way, information from a number of vehicles that have traversed the off-road area can be combined to provide an improved navigable route with detailed route context data. The vehicle sensor information may relate to a plurality of journeys through the off-road area by a single vehicle or by a plurality of vehicles. The method may also comprise receiving the vehicle sensor information corresponding to an off-road area from a single vehicle.

Optionally, the route context data comprises one or more of the following classifying characteristics: weather context data relating to weather conditions, timing context data relating to time of day, journey time context data relating to time taken, energy consumption context data relating to energy consumed, surface friction context data relating to slipperiness of a driving surface; surface topology context data relating to unevenness of the driving surface, vehicle attitude context data relating to the pitch, roll and yaw of a vehicle, average journey time context data relating to average time taken for the route, average energy consumption context data relating to average energy consumed on the route, and segment average speed context data, wherein the navigable route comprises a plurality of segments, and the segment average speed relates to the average speed for a segment.

In an embodiment, the method may comprise determining a label for the navigable route in dependence on the classifying characteristics of the route context data of the navigable route. Labels may relate to difficulty, duration, energy consumption and the like. In this way, a navigable route will have a useful classifying label to allow a user to assess the route quickly, and the user may choose a navigable route according to their preferences.

Optionally, determining a label for the navigable route comprises calculating a score for at least one of the classifying characteristics of the route context data. The label may be determined on whether a score for a characteristic is above or below a predefined threshold. This is an effective and efficient way of providing a label for a route.

According to another aspect of the invention, there is provided computer readable instructions which, when executed by one or more processors, cause the one or more processors to perform the method described herein.

According to yet another aspect of the invention, there is provided a control system for controlling a routing system for a vehicle, the control system comprising one or more processors collectively configured to implement the method described herein. The control system may comprise one or more controllers collectively comprising at least one electronic processor having an electrical input for receiving an input signal; and at least one memory device electrically coupled to the at least one electronic processor and having instructions stored therein; and wherein the at least one electronic processor is configured to access the at least one memory device and execute the instructions thereon so as to: receive vehicle sensor information corresponding to an off-road area; determine, in dependence on the vehicle sensor information, a navigable route through the offroad area; determine, in dependence on the identified navigable route, route context data relating to the navigable route, wherein the route context data comprises classifying characteristics of the navigable route; and store the navigable route and associated route context data in a data store. According to a further aspect of the invention, there is provided a system comprising the control system described herein and a vehicle.

According to a still further aspect of the invention, there is provided a vehicle comprising the control system described herein.

Within the scope of this application it is expressly intended that the various aspects, embodiments, examples and alternatives set out in the preceding paragraphs, in the claims and/or in the following description and drawings, and in particular the individual features thereof, may be taken independently or in any combination. That is, all embodiments and/or features of any embodiment can be combined in any way and/or combination, unless such features are incompatible. The applicant reserves the right to change any originally filed claim or file any new claim accordingly, including the right to amend any originally filed claim to depend from and/or incorporate any feature of any other claim although not originally claimed in that manner.

BRIEF DESCRIPTION OF THE DRAWINGS

One or more embodiments of the invention will now be described, by way of example only, with reference to the accompanying drawings, in which:

Fig. 1 shows a flow chart of a method according to an embodiment of the invention;

Fig. 2 shows a block diagram illustrating a control system according to an embodiment of the invention;

Fig. 3(a) shows a flow chart of a further method according to an embodiment of the invention;

Fig. 3(b) shows a flow chart of another method according to an embodiment of the invention;

Fig. 4 shows a flow chart of a further method according to an embodiment of the invention;

Fig. 5 shows a flow chart of route dissemination method according to an embodiment of the invention;

Fig. 6 shows a flowchart of a method of data aggregation according to an embodiment of the invention; and Fig. 7 shows a vehicle in accordance with an embodiment of the invention.

DETAILED DESCRIPTION

Throughout the specification, the term “off-road area” may be understood to refer to an area that does not have road infrastructure such as paved road lanes, traffic signs, or well-defined road edges. An off-road area may be understood to refer to natural terrain surfaces such as sand, grass, mud, gravel, and may include areas such as trails, forest routes, riverbeds and the like. A navigable route through such an off-road area may be a through-route, in that in begins at one point and finishes at another; or may be a loop route that begins and ends at substantially the same place. A navigable route may include one or more way points along the route.

With reference to Fig. 1 , there is illustrated a flow chart of a method, indicated generally by the reference numeral 100, for a routing system. The method 100 comprises, at block 102, determining, in dependence on sensor information corresponding to an off-road area, a navigable route through the off-road area. The sensor information may be vehicle sensor information obtained from a vehicle or plurality of vehicles that have travelled, or are travelling, in the off-road area. The dimensions of a vehicle of interest may be considered while determining the navigable route. At block 104, the method comprises determining route context data relating to the navigable route, wherein the route context data relates to characteristics of the navigable route. The method 100 comprises, at block 106, storing the navigable route and associated route context data in a data store. The navigable route and associated route context data may then be shared to assist in travelling in the off-road area. The method 100 may be implemented by a control system, such as the controllers described herein in relation to Fig. 2, but are not limited thereto.

With reference to Fig. 2, there is illustrated a control system 200 for a routing system. The control system 200 comprises one or more controllers 202 and is suitable to implement the method described in relation to Fig. 1 . The control system 200 is configured to determine, in dependence on sensor information corresponding to an off-road area, a navigable route through the off-road area; determine route context data relating to the navigable route, wherein the route context data relates to characteristics of the navigable route; and store the navigable route and associated route context data in a data store. The control system 200 may comprise a navigable route determining means, route context data determining means, and a data store for storing the navigable route and associated route context data.

Throughout the description, the route context data may comprise one or more of the following classifying characteristics: weather context data, timing context data, journey time context data, energy consumption context data, surface context data, including surface friction context data, surface type context data, and, surface topology context data, vehicle attitude context data, obstacle context data, and other data relating to classifying characteristics of the route or journey. The weather context data may relate to weather conditions that were associated with a journey in the off-road area from which vehicle sensor information was obtained. The timing context data may relate to the time of day at which such a journey was taken. The journey time context data may relate to the time taken for such a journey. The energy consumption context data may relate to the energy consumed by a vehicle over such a journey. The surface friction context data may relate to the resistance/friction of the driving surface experience during such a journey (i.e. the slipperiness of a driving surface). The surface type context data may relate to the surface type of the route, including for example grass, sand, gravel and so on. The surface topology context data may relate to the unevenness e.g. rutted-ness of the driving surface experience during such a journey. The vehicle attitude context data may relate to attitude of the vehicle during the journey, including the pitch, roll and yaw experienced by a vehicle during such a journey. Obstacle context data may relate to significant obstacles, such as rocks, fallen trees, overhangs, and dips, along the route of the journey.

Where the vehicle sensor data relates to a plurality of journeys through the off-road area, the route context data may also comprise one or more of the following classifying characteristics: the average journey time context data, average energy consumption context data, and segment average speed context data, wherein the navigable route comprises a plurality of segments, and the segment average speed relates to the average speed for a segment. The average journey time context data may relate to an average time taken for a particular navigable route through the off-road area. The average energy consumption context data may relate to the average energy consumed for a journey on a particular navigable route. For the segment average speed context data, the navigable route comprises a plurality of segments, and the segment average speed relates to the average speed for a particular segment.

The route context data may be determined in the context of the vehicle that will be travelling in the off-road area.

Referring now to Fig. 3(a), there is illustrated a flow chart of a computer-implemented method, indicated generally by the reference numeral 300, for a routing system for a vehicle according to an aspect of the disclosure. The method 300 comprises, at block 302 receiving vehicle sensor information corresponding to an off-road area. At block 304, the method comprises determining, in dependence on the vehicle sensor information, a navigable route through the off-road area. The method comprises, at block 306, determining, in dependence on the identified navigable route, route context data relating to the navigable route. The route context data comprises classifying characteristics of the navigable route. At block 308, the method comprises storing the navigable route and associated route context data in a data store.

Receiving 302 the vehicle sensor information corresponding to an off-road area may comprise receiving the vehicle sensor information from a plurality of vehicles. The vehicle sensor information may be received from a single vehicle that has travelled in the off-road area, or may be received from a plurality of vehicles that have travelled in the off-road area. The vehicle sensor information may be received in real time as a vehicle is travelling in the off-road area, may be received at the end of the journey, or may be saved data from one or more previous journeys.

The vehicle sensor information may comprise information obtained from one or more sensors of a vehicle or a plurality of vehicles, over one or more journeys in the off-road area of interest. The sensors may include perception sensors and inertial motion sensors, such that the vehicle sensor information may comprise vehicle perception sensor information and vehicle inertial motion sensor information. Example perception sensors include camera, radar, Lidar, and ultrasonic sensors. Such sensors may be included in an Advanced Driver Assistance System (ADAS) of the vehicle. Example inertial motion sensors may include gyroscope, accelerometer, and magnetometer. Such sensors may be included in an Inertial Measurement Unit (IMU) of a vehicle. The vehicle sensor information may also include location information from a navigation system such as a satellite navigation system, such as GPS or GNSS. Each sensor measurement will include a timestamp i.e. information as to the time the measurement was taken or captured. In this way, the timestamp can be used as a key/identifier to allow sensor data from multiple sensors and multiple sensor types to be grouped/collated in an appropriate manner. Vehicle sensor information may also include an indication of the type of vehicle providing the vehicle sensor information. In this way, it is possible to assess the vehicle sensor data in the context of the vehicle from which it originates. It will be understood that the vehicle sensor information is not limited to sensor information from the above-mentioned sensors and sensor types.

A preliminary path for the navigable route may be based on location information included in the vehicle sensor information. The path taken by a vehicle on a journey through the off-road area may be referred to as a track, and may be considered to comprise a plurality of track segments. Location information, such as GNSS information, may be first analysed to identify a track followed by the vehicle. The track may include one or more waypoints, which may define the segments of the track. GNSS data of the vehicle sensor data, that may in its unprocessed state comprise sets of latitude and longitude coordinates, may be processed to define the track though the relevant area. Such a course or trail may include a set of waypoints. Data from the LiDAR point cloud may be associated with the data defining the track.

A further method according to an aspect of the disclosure, indicated generally by the reference numeral 350, is illustrated in a flow diagram shown in Fig. 3(b). The method 350 of Fig. 3(b) is similar to the method 300 of Fig. 3(a) with like features using the same reference numerals. However, the method 350 includes, at block 303, aggregating the vehicle sensor information when vehicle sensor information is received relating to a plurality of journeys, which may be from a single vehicle or a plurality of vehicles. The method 350 then continues as in the method 300 of Fig. 3(a) by determining 304 the navigable route in dependence on the aggregated vehicle sensor information. In some examples, the vehicle sensor data from a journey may be analysed separately before aggregation. This may result in a reduced quantity of data to be aggregated, such that the data processing load of aggregation is reduced. In an example, the kinematic sensor data may be processed at a journey level to identify surface-type transitions, such as a transition from gravel to sand and the like. The identified transitions from various journeys may then be aggregated. Transition data may be linked with the relevant vehicle heading for aggregation. This facilitates identifying the driveable direction, for example, distinguishing if the vehicle is heading from north to south or from south to north. Other kinematic data, for example vehicle attitude data relating to the roll, pitch and yaw, may be processed initially at a journeylevel to identify variations that exceed predefined values. Then, these above-threshold variations may be aggregated with similar data from other journeys. Perception sensor data, and other sensor data may also be processed prior to aggregation.

Aggregating the vehicle sensor information for multiple journeys is useful to provide reliable navigable routes, and useful route context data for those navigable routes. In an example, aggregating data from kinematics sensors can help identify a road surface profile. In particular, aggregated data relating to pitch rate, vertical acceleration, longitudinal acceleration are useful for identifying a road surface profile.

Referring now to Fig. 6, there is shown a flowchart of an example method of aggregating the vehicle sensor information, as per block 303 in Fig. 3(b). At block 3032, the vehicle sensor information from a plurality of journeys may be received into a data lake. At block 3034, the vehicle sensor information in the data lake may be processed to identify anomalous data, which may be referred to as outlier data. Anomalous data may refer to situations where incorrect readings are received from one or more sensors. Cross-checking data between sensors can be a useful way to identify data errors. Anomalous data may be identified when data from one sensor does not agree with data from another sensor where there would expected be to a correlation. For example, if the vehicle’s speed is zero, it would be expected that the vehicle’s location is constant. However, if the vehicle’s location is changing, this would appear to contradict the speed measurement. In another example, a correspondence would be expected between steering wheel angle and vehicle heading, such that if one changes without the other, the data may be considered anomalous. The data may be filtered to remove the identified anomalous data. Reducing the amount of anomalous data can reduce the possibility for creating an erroneous route. Identifying and filtering anomalous data may be carried out using a machine learning model, for example, a model that allows unsupervised learning, such as support vector machines.

At block 3036, the remaining data may be denoised and restored to address image artefacts that may have arisen due to sensor issues. Sensor issues may include sensor blinding where environmental issues may impact the output of an image sensor. The environmental issues may include direct sunlight, mud, dust accumulation, and/or water drops. Image restoration and denoising may be implemented using Convolutional Neural Networks or Generative Adversarial Networks (GAN) where a trained model can denoise or decrease the visibility degradation.

At block 3038, feature extraction may be carried out on the denoised data. Feature extraction may include identifying and labelling track segments and/or tracks through the off-road area; identifying and labelling the off-road geometry; identifying and labelling off-road objects; identifying and labelling the off-road surface type; and identifying and/or labelling navigable routes in relation to with respect to vehicle dimensions. In relation to the off-road geometry, a track or a track segment may be labelled as one or more of as straight, curved, flat, downhill, uphill, left inclined, right inclined, and so on. In relation to the off-road objections, these may include boulders, fallen branches and trees, water, and the like. In relation to off-road surface types to be identified, these may include sand, gravel, mud, grass, rock or the like. In relation to the dimensions of the vehicle for a potential navigable route, each track or track segment may be labelled as driveable or non-driveable in relation to the vehicle dimensions. The identification and labelling may be implemented using machine learning, for example fully convolutional neural networks.

In examples, the sensor data, in particular from the perception sensors, may be aggregated using confidence levels. In this way, data from more recent journeys may be understood to be more accurate than olderjourneys. As such, data from more recent journeys that conflicts with data from olderjourneys may be given a reasonably high confidence value, however, data from recent journey that agrees with data from olderjourneys would be given an even higher confidence value. In this way, it is possible to consider an example where an object that is potentially an obstacle is detected in a data from one or more perception sensors in the most recent journey in the off-road area, however the object may also simply be an imaging artefact or may be a temporary or transient obstacle such as an animal or an item of rubbish/trash. Aggregating the data may comprise considering the perception data forthat location from previous journeys. In an example, if the object is present in the data from previous journeys, then it may be assigned a high confidence value such 0.9 to indicate a high confidence that there is an obstacle present. However, if the object is not present in the data from previous journeys, then it may be assigned a lower confidence level of, say, 0.7. Similarly, if the earlier data indicates a potential obstacle but nothing is detected in the most recent data, then the object could be assigned a low confidence level of 0.1. Other factors may be considered in relation to the confidence levels, for example, visibility at the times the various data sets were collected. For example, day and night conditions, weather conditions where rain/snow may cause a reduction in sensor accuracy. In this way, sensor data obtained during clear weather and long-range visibility conditions may be assigned a higher confidence level. The confidence level values may be further processed in a thresholding operation, such that only features having a confidence level above the threshold are considered further. Aggregating data from a plurality of journeys facilitates identifying driveable terrain in an accurate manner. Combining data from a plurality of journeys may reduce uncertainty arising from having only individual measurements.

Once the vehicle sensor data from the plurality of journeys has been aggregated, the labelled, extracted features may then be further processed as part of identifying one or more navigable routes through the offroad area.

The methods described herein may be carried out on-board a vehicle, or may be carried out off-board, for example at a server. The methods may be carried out at a plurality of devices, for example, certain actions may be executed on-board the vehicle and other actions may be carried out off-board. For example, the vehicle or vehicles in question may transmit their accumulated vehicle sensor data to a suitable control system for determining the navigable route. The vehicles may transmit unprocessed sensor data such that analysis and processing of the sensor data is carried out off-board. As discussed above, the vehicle sensor data may be converted to object data, such as identification of positive, negative and/or hanging objects.

The methods described herein may comprise an additional step (not shown) of outputting the navigable route. The navigable route may be output to one or more vehicles, or to a central controller, which can output the route to one or more vehicles that wish to travel in the off-road area. The method may also comprise outputting some or all of the route context data, in a similar manner.

Once the vehicle sensor information corresponding to the off-road area for which a navigable route is to be identified has been identified, which may include aggregating multiple sets of vehicle sensor information for that off-road area, the determination of a navigable route can commence. Determining a navigable route may be based on identifying previously travelled tracks through the navigable area. Those tracks may be analysed to determine an associated driveable area or driveable areas. A navigable route may be derived from the driveable areas in combination with start and end points.

The vehicle sensor information may be analysed to identify a track or tracks in the off-road area that have been travelled previously. Such a track may represent a potential navigable route, or a portion thereof. Where there is vehicle sensor information from a plurality of journeys, the most-travelled tracks may be identified. Tracks identified via vehicle sensor information having high confidence levels may themselves be given a higher confidence level. The terrain of the identified tracks may be considered, in combination with other available data such as map data and aerial image data.

On the identified tracks, the vehicle sensor information may be analysed to identify significant objects, including negative objects, hanging objects, and positive objects. Tracks may be considered unsuitable if they have significant obstacles, result in severe vehicle attitudes, or for other reasons. If a track comprises an object large enough that a vehicle may not be able to manoeuvre to avoid it, the track will not be considered further. Such an obstacle may be referred to as a “route blocker”. The vehicle attitude context data may be analysed to compare one or more of the pitch, roll and yaw values to predetermined reference value or values, and if the reference values are exceeded, the track will not be considered further. The method may include enumerating the number of times that the reference values are exceeded and determining if the track is considered a navigable route based on the enumeration. Other manners of assessing of the severity of the attitude variations will be apparent to the skilled person. Furthermore, if the track includes a feature that resulted in a disruption of a journey, where a vehicle was required to turn back, the track would be considered unsuitable. Similarly, if a journey along a track was not completed for some other reason, the track may not be considered a navigable route. A track not excluded may be considered further to determine if it represents a driveable area.

Vehicle specification and capabilities may be considered when identifying navigable routes. For example, the vehicle dimensions and maximum wading depth may be considered. Some identified navigable routes may be suitable for some vehicle types but not others. For example, an object may be a route blocker for one vehicle type but may be avoidable or surmountable for another vehicle type.

In determining a navigable route, the driveable area of a track is also considered. The driveable area may be understood to refer to the terrain width and height, with respect to the dimensions of a vehicle that will be travelling the navigable route. The driveable area may be determined from the vehicle sensor data, in particular the perception sensor data. The vehicle sensor data may be considered in combination with map data, based on GNSS data associated with the vehicle sensor data readings. Multiple coordinate points may be identified along a track, and then merged with vehicle heading information to generate way points to be used to define a driveable area.

Previously identified objects in the terrain may be considered in relation to the driveable area. The height of the driveable area may be derived from the vehicle sensor data, and may relate to the clearance above the vehicle in relation to hanging obstacles. The determined width and height of the tracks may be compared with the vehicle dimensions, and those tracks having sufficient clearance may be considered navigable routes. Segmentation algorithms may be used on identified tracks to determine if some or all of a track corresponds to a driveable area. The segmentation operation may be applied only to tracks that have been previously analysed for route-suitability, such as considering obstacles, and vehicle attitude data. Merging coordinates with respect to heading information is intended to yield way points for the driveable area.

In an example, the driveable area may be understood to refer a combination of traces that have been defined as a baseline reference, and segmented with respect to identified objects.

Once a driveable area has been identified, it may be combined with a start and end point to form a navigable route. Where a plurality of driveable areas are identified, one or more may be combined with a start and end point to form a navigable route.

When a navigable route has been identified, route context data relating to the navigable route is determined. As described above in relation to Fig. 2, the route context data may comprise one or more of the following classifying characteristics: weather context data, timing context data, journey time context data, energy consumption context data, surface friction context data; surface topology context data, vehicle attitude context data, and other data relating to classifying characteristics of the route or journey. The route context data may be determined per journey and combined to provide a relevant representative value for the route. The route context data may be used to characterise the navigable route. The route context data may be derived from the vehicle sensor data, directly or indirectly. Determining the route context data may comprise determining weather context data by identifying the weather conditions associated with a journey on the navigable route, and tagging the vehicle sensor data from that journey with the weather conditions. Determining the timing context data may comprise determining the time of day at which a journey on the navigable route occurred, and tagging the vehicle sensor data from that journey with the time-of-day information. The time-of-day information may be determined as associated with a particular interval of the day, for example, morning, afternoon or the like.

Determining the journey time context data may comprise determining an average time taken for a journey on the navigable route, where a plurality of journeys have provided vehicle sensor data. Similarly, determining the energy consumption context data may comprise determining an average energy consumption forthe navigable route. Journey time context data may also be determined at a segment level, the navigable route comprises a plurality of segments defined by a plurality of waypoints on the navigable route. Determining the segment average speed context data may comprise determining the average speed for a particular segment.

Determining the route context data may comprise determining surface friction context data, relating to the resistance/friction (e.g. slipperiness) of the driving surface experience during such a journey. This may be derived from the number of ABS interventions that occurred in a particular journey. Vehicle sensor information from one or more of the wheel speed sensors may also be considered.

Determining the route context data may comprise determining surface topology context data relating to the unevenness of the driving surface experience during such a journey. This may be derived from the number of ruts, and the like encountered on a journey.

Determining the route context data may comprise determining vehicle attitude context data relating to attitude of the vehicle during the journey. This may be determined by counting the number of times the yaw, pitch, and roll values exceeded respective predetermined thresholds on a journey. Vehicle attitude context data may also relate to the slope characteristics of the route.

Additionally, route context data may be determined by combining categories of context data, for example, the average journey time for a rainy day may be determined, as well as the overall average journey time.

The methods described herein may comprise determining a label for the navigable route in dependence on the classifying characteristics of the route context data of the navigable route. Labels may relate to difficulty, duration, comfort, energy consumption and the like. In this way, a navigable route will have a useful classifying label to allow a user to assess the route quickly. Determining a label forthe navigable route may comprises calculating a score for at least one of the classifying characteristics of the route context data. The label may be determined on whether a score for a characteristic is above or below a predefined threshold. Routes may be labelled according to energy consumption, time take, difficulty, and so on. In an example, a route having an average energy consumption below a predefined level may be labelled as an “eco” route. In another example, a route having vehicle attitude context data below a certain threshold may be labelled as a “comfort” route, while a route which has more extreme terrain may be labelled as “difficult”.

Referring to Fig. 4, there is shown a flowchart of a further method, indicated generally by the reference numeral 400, according to an embodiment of the disclosure. The method 400 comprises, at block 402 receiving vehicle sensor information corresponding to an off-road area from a plurality of journeys, and combining that vehicle sensor data. At block 404, the method comprises determining, in dependence on the vehicle sensor data, a navigable route through the off-road area. A number of navigable routes may be determined. The method comprises, at block 406, determining, in dependence on the identified navigable route, route context data relating to the navigable route. The route context data comprises classifying characteristics of the navigable route. At block 408, the method comprises storing the navigable route and associated route context data in a data store. At block 412, the method comprises receiving further vehicle sensor information corresponding to the off-road area. The method comprises, at block 414, determining, in dependence on the further vehicle sensor information, an updated navigable route. Information on the updated route may include newly-identified obstacles. If such an obstacle results in a previously navigable route being blocked, that navigable route may be flagged as blocked. The route context data may also be updated based on the further vehicle sensor information. In an example, the surface route context data may be updated for a changed surface friction or the like. At block 416, the method comprises storing the updated navigable route in the data store. Fig. 4 shows four vehicles providing vehicle sensor data initially and two vehicles providing further vehicle sensor data, but it will be understood that this is merely illustrative and vehicle sensor data may be received from any number of journeys, including a plurality of vehicles, or one or more journey from a single vehicle.

The method 400 may comprise outputting, at block 410, at least one of the navigable routes, its associated route context data, the updated navigable route, and the updated route context data. This allows other vehicles wishing to travel in the off-road area, or currently travelling therein, to obtain information about the navigable route.

A navigable route may have a revision number, and when the navigable route has been updated, its revision number will be incremented. Each revision of the navigable route may have a timestamp based on revision release time.

Each time a version of a navigable route is travelled by a vehicle, a route usage counter for that navigable route is incremented. In this way, popular routes may be identified. Additionally, more confidence can be placed in the information for a route that has been travelled more recently. As will be understood, the methods described herein may result in a number of navigable routes through the off-road area having been identified and labelled according to their characteristics. This information is stored for access by vehicles who wish to travel in the off-road area. Referring now to Fig. 5, there is shown a flow chart for a method, indicated generally by the reference numeral 500, of disseminating the data relating to the navigable routes and their characteristics. At block 502, a vehicle requests information on navigable routes in the off-road area. At block 504, the available navigable routes and their related route context data is transmitted to the requesting vehicle. At block 506, the user of the vehicle, selects the characteristics of the route they’d like to travel. The user selects a route from a list of the navigable routes that meet their selected characteristics at block 508. At block 510, any updates to the selected navigable route are transmitted to the vehicle while it travels along it.

When a user is selecting the characteristics of the route they’d like to travel, they may be offered options in relation to one or more of difficulty, duration, energy usage and so on.

The weather context data may be considered in choosing the routes to be offered to the user and/or may change the options the user can choose from when selecting the characteristics of the route they’d like to travel. In this way, the route context data can help to provide weather dependent off-road routing with respect to driver preferences. The methods disclosed herein may be carried out on board a vehicle, or may be carried out remote from the vehicle, for example at a suitable server. If the method is carried out on a server, the navigable route and associated route context data may be stored at that server or another suitable location. In some examples, parts of the method may be carried out on one device and other parts may be carried out at another device. In an example, a vehicle may receive vehicle sensor information from one or more other vehicles, and combine that with its own vehicle sensor information to identify a navigable route, as described herein. Some or all of the details of the navigable route and its associated route context data may be transmitted to relevant vehicles, from a server or another vehicle, at an appropriate time. In an example, the methods described herein are applied in relation to vehicles in a convoy in the off-road area. This is particularly relevant in relevant for updates in relation to the route. Consider an example where a convoy of vehicles are travelling through to an off-road area for which a plurality of labelled navigable routes have been determined. A route is selected and the convoy being to travel along the selected route. As the lead vehicle is travelling, it acquires vehicle sensor information of its route and surroundings. This lead vehicle sensor information may be considered as further vehicle sensor information suitable for use in routing. An updated navigable route may be determined in dependence on the further vehicle sensor information, and updated route context data may also be determined. The updated navigable route may then be transmitted to other vehicles in the vicinity, including those in the convoy. In a particular example, the lead vehicle determined the navigable route from vehicle sensor data for the convoy.

A vehicle 600 in accordance with an embodiment of the present invention is described herein with reference to the accompanying Fig.7. The invention disclosed herein aims to provide improved off-road routing, which may allow routes to be tailored to driver preferences, vehicle capabilities, and so on. It will be appreciated that various changes and modifications can be made to the present invention without departing from the scope of the present application.

Claims

1 . A computer-implemented method for a routing system for a vehicle, the method comprising: receiving vehicle sensor information corresponding to an off-road area; determining, in dependence on the vehicle sensor information, a navigable route through the off-road area; determining, in dependence on the identified navigable route, route context data relating to the navigable route, wherein the route context data comprises classifying characteristics of the navigable route; and storing the navigable route and associated route context data in a data store.

2. A computer-implemented method as claimed in claim 1 comprising receiving further vehicle sensor information corresponding to the off-road area; determining, in dependence on the further vehicle sensor information, an updated navigable route; and storing the updated navigable route in the data store.

3. A computer-implemented method as claimed in claim 1 or 2 comprising receiving further vehicle sensor information corresponding to the off-road area; determining, in dependence on the further vehicle sensor information, updated route context data; and storing the updated route context data in the data store.

4. A computer-implemented method as claimed in claim 3 comprising outputting at least one of the navigable route, associated route context data, updated navigable route, and the updated route context data, to a vehicle.

5. A computer-implemented method as claimed in claim 4 comprising receiving a notification when a vehicle travels the identified route and updating a route usage counter for the navigable route in dependence on the notification.

6. A computer-implemented method as claimed in claim 5 comprising obtaining a timestamp for the notification.

7. A computer-implemented method as claimed in claim 6 comprising prioritising outputting the updated navigable route, and the updated route context data in dependence on the route usage counter and timestamp.

8. A computer-implemented method as claimed in any previous claim wherein receiving vehicle sensor information corresponding to an off-road area comprises receiving vehicle sensor information corresponding to an off-road area from a plurality of journeys through the off-road area.

9. A computer-implemented method as claimed in any previous claim wherein the route context data comprises one or more of the following classifying characteristics: weather context data relating to weather conditions, timing context data relating to time of day, journey time context data relating to time taken, energy consumption context data relating to energy consumed, surface friction context data relating to slipperiness of a driving surface; surface topology context data relating to unevenness of the driving surface, vehicle attitude context data relating to the pitch, roll and yaw of a vehicle, average journey time context data relating to average time taken for the route, average energy consumption context data relating to average energy consumed on the route, and segment average speed context data, wherein the navigable route comprises a plurality of segments, and the segment average speed relates to the average speed for a segment.

10. A computer-implemented method as claimed in any previous claim comprising determining a label for the navigable route in dependence on the classifying characteristics of the route context data of the navigable route.

11. A computer-implemented method as claimed in claim 10 wherein determining a label for the navigable route comprises calculating a score for at least one of the classifying characteristics of the route context data.

12. Computer readable instructions which, when executed by one or more processors, cause the one or more processors to perform the method according to any of claims 1 to 11 .

13. A control system for controlling a routing system for a vehicle, the control system comprising one or more processors collectively configured to implement the method of any of claims 1 to 11 .

14. A system comprising the control system of any preceding claim and a vehicle.

15. A vehicle comprising the control system of claim 13.