Patent Description:
Autonomous vehicles (AVs) in confined areas typically have a simpler sensor setup than those used on public roads, to reduce the expenditure on product cost as well as development cost. Since the environment is known to a further extent in a confined area, the foreseen obstacles that need detection can be narrowed down significantly, as opposed to public road applications where all types of physical obstacles, other traffic participants etc. need to be detected by the sensors. Thanks to the narrowing-down, sensors can be more adapted to the particular use case. Also, the applications can often be implemented with simpler algorithms for object tracking, reducing the need for more complex sensor setups. For example, in some applications it is enough to determine that an object exists in the path of the AV and to stop, whereas for a public-road use case, the object needs to be detected, classified and its intentions need to be predicted, in order to keep the productivity high. With the simpler sensor setup, a simpler representation of the data can be used, for example an occupancy grid can be used as a representation of the world surrounding the AV.

<CIT>, which targets a generic AV use case, discloses a method for generating a non-uniform occupancy grid map, in which the cell size depends on a distance from an object to an ego autonomous vehicle. It is argued that the precision requirements decrease further away from the autonomous vehicle, which justifies using a lower grid-cell density there. The grid size may further be adjusted based on the speed of the ego vehicle.

Further, <CIT> discloses a method for operating an AV operating in a space, such as an indoor space such as a room, facility, terminal, warehouse of similar. The method assigns a weight to an object detected by an AV onboard sensor relating to how static the detected object is. When the AV has been operated in the space multiple times and the same static object has been detected, the weight can be updated (increased). It is disclosed in <CIT> that the method may use an occupancy grid, by which the space is divided into a grid of map cells, where each cell contains an estimate of the probability that that cell is occupied by a landmark or feature. If an occupancy probability is high, that position is more trustworthy.

When operation of the AV is started in this or a similar technical context, all cells in the occupancy grid typically have an uncertain occupancy probability, since the sensors have not registered any hits at all. Once the sensors are up and running, the occupancy probability will be lowered for free cells and raised for occupied cells. However, this is only for cells where the sensors have coverage. In some sensor installations, e.g., the area closest to the AV (cf. area <NUM> in <FIG>) may not be covered at all by the FOV of the sensors. This means that those cells will never be registered as free, since they are not visible for the sensors and the vehicle will not start moving until the cells are reported as free. Other scenarios where a vehiclecontrol system might have issues include when a tractor reverses with a trailer: if a sensor's FOV is obstructed rearwards, the cells just behind the vehicle combination might never be seen and may incorrectly be reported as free (cf. area <NUM> in <FIG>). If the cells are never reported as free, the vehicle will stop reversing and the vehicle combination might block the sensors so that the cells are never able to be seen and reported as free. Further still, there may be situations where the sensors do not have FOV sufficient to continuously report the necessary cells as free, and in some cases this might lead to a deadlock where the AV is not allowed to move; and since the AV does not move, the sensors never see an unobstructed FOV.

A frequently used, though not always cost-efficient, solution may be to add sensors, so that the total FOV is extended. This may well solve the problem (except the startup deadlock issue) but adds complexity and cost. Not all confined applications are foreseen to require full sensor coverage since other mechanisms can ensure the path to be clear of obstacles, such as manual checks, site-mounted sensors, fenced-off areas, etc..

<CIT> discloses a method of controlling an autonomous vehicle according to the preamble of claim <NUM>. In relation to <CIT>, the present disclosure addresses the problem of improving the productivity of the controlled AV without sacrificing safety.

One objective of the present disclosure is to make available a method and vehicle controller suitable for more efficiently controlling an AV on the basis of a world model having an occupancy score for each area of a drivable surface on which the AV can move. It is a further objective to improve the productivity of the AV thus controlled without sacrificing safety. It is a further objective to make such improvements in a case where the world model is obtained or updated based on measurement data from one or more sensors carried by the AV. It is a further objective to present measures for counteracting the deadlock scenario outlined above. It is a still further objective to provide accurate speed control for the AV and reliable estimation of the time needed to complete a driving mission.

At least some of these objectives are achieved by the present invention as defined by the independent claims. The dependent claims relate to advantageous embodiments of the invention.

In a first aspect of the invention, there is provided a method of controlling an AV with the technical features according to claim <NUM>.

By the use of a distance-dependent threshold for determining whether grid cells are occupied, the method according to the first aspect could for instance allow the AV to move into relatively more distant cells under a relatively more lenient condition. Indeed, noise and other spurious contributions to the occupancy score will be disregarded to a greater extent if the occupancy threshold is set higher in distant cells, so that the noise and other spurious contributions to the occupancy score are less likely to exceed the occupancy threshold. The effect is achieved regardless of whether the world model has a uniform or non-uniform spatial resolution throughout the drivable surface; instead, the spatial resolution of the world model can be constant over time and need not be revised when the AV moves to a new position, which seems to be required in the prior art solution described in <CIT>. With the method according to the first aspect, the vehicle control function will typically see a finite free path ahead of the AV, which is shorter in regions with more uncertain cells, so that the AV's speed will be inherently limited. The proposed solution is able to account for the fact that measurements on locations more distant from the AV are typically less accurate and/or less reliable, especially if the measurements are made from the viewpoint of the AV. This effect flows from the use of different thresholds, which will limit the impact of any errors that affect the relatively more distant locations.

In some embodiments, the occupancy threshold is determined to be relatively higher if the area is relatively closer to the AV and relatively lower if the area is relatively farther from the AV. This determination presupposes knowledge of the AV's current position (e.g., relative to the world model), which must be determined unless already available. It is important to note that although the occupancy threshold is determined dynamically in view of the AV position, it is independent from the world model. The occupancy threshold can be understood as a desired safety level at a certain position relative to the AV; it typically does not represent a topographic feature (or local feature) associated with a definite location on the drivable surface.

According to the invention, where the world model is obtained and/or updated based on measurement data from one or more sensors carried by the AV, the occupancy threshold is determined to be relatively higher if the area is outside a field of view (FOV) of the sensors carried by the AV and relatively lower if the area is inside the field of view. The FOV is a simple yet efficient criterion for setting the occupancy threshold. Especially for measuring techniques relying on propagating electromagnetic or acoustic waves, it accurately distinguishes the areas where fresh measurement data is available and the areas where older measurement data has to be used. The use of older measurement data could bring a greater degree of uncertainty.

In some embodiments, the method further comprises generating a route of the AV towards a destination (in particular, up to a destination), said route passing only via such areas into which movement is enabled, and estimating a time of arrival at the destination. This embodiment improves on such prior art approaches where a time of arrival is estimated without sufficient attention given to obstacles in the driving environment and/or without considering spatial limitations of the sensorics used. Such prior art approaches may initially produce vehicle routes with too high speed, and the speed might later have to be reduced on short notice when new obstacles appear, e.g., as a result of a refresh of the world model or when more reliable measurement data becomes available. Such sudden changes could also have negative secondary effects on a vehicle fleet to which the routed vehicle belongs, more precisely, as an original plan for coordinating vehicle movements within the fleet could suddenly become obsolete and have to be made up anew. In the present embodiment, the use of a variable occupancy threshold, which increases away from the vehicle, will produce conservative velocity profiles, which are typically a more realistic basis for vehicle routing and vehicle coordination.

In a second aspect of the invention, there is provided a vehicle controller with the technical features according to claim <NUM>.

The second aspect of the invention generally shares the effects and advantages of the first aspect, and it can be implemented with a corresponding degree of technical variation.

The invention further relates to a computer program containing instructions for causing a computer, or the vehicle controller in particular, to carry out the above method. The computer program may be stored or distributed on a data carrier. As used herein, a "data carrier" may be a transitory data carrier, such as modulated electromagnetic or optical waves, or a non-transitory data carrier. Non-transitory data carriers include volatile and non-volatile memories, such as permanent and non-permanent storage media of magnetic, optical or solid-state type. Still within the scope of "data carrier", such memories may be fixedly mounted or portable.

In the present disclosure, the concept of an "occupancy score" could overlap with the scope of the term occupancy probability in the literature, where it is sometimes defined as an estimate of the probability that that cell is occupied by a landmark or feature, or not drivable for other reasons. An occupancy score should be understood in a somewhat broader sense, to include also indicators that are not necessarily probabilistically true in a strict sense. A higher occupancy score for an area should be understood as a higher probability that the area is occupied. The condition of being occupied can refer to various aspects of the area not being possible to drive, e.g., a physical obstacle is present, the road surface is missing, the road is bumped or recessed (e.g., pothole), or is polluted (e.g., oil, grease, debris, crushed glass).

The aspects of the present disclosure will now be described more fully hereinafter with reference to the accompanying drawings, on which certain embodiments of the invention are shown. These aspects may, however, be embodied in many different forms and should not be construed as limiting; rather, these embodiments are provided by way of example so that this disclosure will be thorough and complete, and to fully convey the scope of all aspects of the invention to those skilled in the art.

<FIG> shows a surface <NUM> on which an autonomous vehicle (AV) <NUM> is configured to move. In typical use cases envisaged for the present disclosure, the surface <NUM> is a confined area, such as a mining site, logistics center or port. The AV <NUM> can be a car or a heavy commercial vehicle, such as a construction equipment vehicle, a one or multi-unit truck or a bus, as illustrated in <FIG>. The example AV <NUM> in <FIG> is equipped with a sensor <NUM> with a limited field of view (FOV) as well as a wireless communication interface <NUM>.

The AV <NUM> is assumed to be under the control of a vehicle controller <NUM>. The vehicle controller <NUM> may be any programmable general-purpose computer with suitable input/output interfaces. It can be mounted in the vehicle AV <NUM> or be external to the AV <NUM>. As shown in detail <FIG>, the vehicle controller <NUM> may comprise a sensor interface <NUM>, a control interface <NUM>, an operator interface <NUM> as well as processing circuitry <NUM> including runtime memory and one or multiple processor cores.

The sensor interface <NUM> is adapted for at least receiving data from sensors <NUM>, such as a camera, a lidar and a radio receiver. The sensors <NUM> may use one or more of the following measuring principles: optical, electromagnetic reflection, electromagnetic scattering, electromagnetic diffraction, lidar, color-depth sensing, millimeter-wave radar, ultra-wideband radar. For these measuring principles, which rely on rectilinear propagation of energy, a sensor's FOV can usually be accurately estimated by tracing straight lines to or from the sensor. The sensors <NUM> may be carried by (e.g., installed in) the AV <NUM> or they may be external to the AV <NUM>. The external sensors <NUM> may be fixedly mounted on a building or another structure, or they may be mobile, in particular carried by other vehicles.

The control interface <NUM> is compatible with the communication interface <NUM> of the AV <NUM>. It may be used for conveying instructions suitable for execution by actuators in the AV <NUM>, either directly (machine-level commands) or after conversion into machine-level commands by a processor carried in the AV <NUM>.

The operator interface <NUM> is configured to receive higher-level or longterm commands, such as transport missions to be carried out by the AV <NUM> or strategic instructions, from a human operator or from an executing software application authorized to control the AV <NUM>.

It is seen in <FIG> that the surface <NUM>, for purposes of vehicle control, is overlaid with an imaginary occupancy grid <NUM>, by which the surface <NUM> is partitioned into cells <NUM>. The cells <NUM> visible in <FIG> constitute a tiling (or tessellation) of the surface <NUM>, and it is theoretically known that this can be achieved in several different ways. The cells <NUM> may have a single geometric shape or multiple different geometric shapes/sizes can be combined. To mention a few examples, the cells <NUM> can be polygons, such as hexagons or quadrilaterals, in particular rectangles or squares. The occupancy grid <NUM> can have a uniform or nonuniform spatial resolution, e.g., as measured in terms of the number of cells <NUM> per unit area of the surface <NUM>. The occupancy grid <NUM> should cover a portion of the surface <NUM> where the AV <NUM> is expected to be moving (so as not to limit the vehicle controller's freedom to perform efficient routing of vehicles), but the occupancy grid <NUM> does not necessarily cover the full extent of the surface <NUM>, e.g., it need not extend all the way to the outer limits of a logistics site or the like. The occupancy grid <NUM> provides an occupancy score for each cell <NUM>. As explained above in more detail, an occupancy score can be understood as an occupancy probability - an estimate of the probability that that cell is occupied by a landmark or feature, or not drivable for other reasons - or as a generalized version of this concept. As illustrated in <FIG>, the condition of being occupied can refer to various aspects of the area not being possible to drive, e.g., a physical obstacle <NUM> is present, the road surface is missing or has a bump or recess <NUM>, or the area contains a pollutant <NUM>.

Functionally, the occupancy grid is a "world model" in the sense of the present disclosure, i.e., a data structure to be used as guidance for vehicle control, including routing and vehicle coordination. Specifically, the cells of the occupancy grid constitute "areas" each associated with an occupancy score. As an alternative to an occupancy grid, sufficient information for supporting the vehicle control could be provided by a map or a map-like structure in computer-readable form, an object list, or any other suitable data structure which associates areas of the surface <NUM> with locally valid occupancy score values.

Two example embodiments of a method <NUM> suitable for controlling an AV of the type illustrated in <FIG> will now be described with reference to the flowchart in <FIG>. The method <NUM> can be executed by any processor with access to a suitable world model of the surface <NUM> and with authority to enable and disable movement of the AV. A distributed or hybrid task allocation is also imaginable, wherein a first processor performs the enabling and disabling of movement into certain areas, and a second processor is responsible for decision-making about vehicle routing and vehicle coordination subject to these movement restrictions. The second processor may be entrusted with the feeding of the instructions to the AV <NUM>. For purposes of illustration, it will be assumed hereinafter that the method <NUM> is executed by the vehicle controller <NUM> to control the AV <NUM>.

In a first embodiment of the method <NUM>, an initial step <NUM> includes obtaining a world model. The world model associates an occupancy score, denoted p(mi), with each area mi of the surface <NUM>. The vehicle controller <NUM> can obtain the world model can be obtained by receiving a data structure representing the world model, or retrieving it from a shared memory. Alternatively, the vehicle controller <NUM> can obtain the world model, or update it, based on measurement data from the sensors <NUM>. In particular, measurement data from sensors <NUM> in the AV <NUM> itself can be used to generate the world model.

In a next step <NUM>, for at least one area mi of the surface <NUM>, an occupancy threshold t(mi) is determined. The occupancy threshold t(mi) is determined on the basis of the position of the area mi where the occupancy threshold is to be applied. The position dependence of the occupancy threshold can be defined according to one of the following options.

In such implementations of the method <NUM> where the position of the AV <NUM> is not determined, a static division into zones of the surface <NUM> maybe used. One option is to define, with reference to the AV's <NUM> normal area of operation, one 'near' zone and one 'far' zone, wherein a relatively lower occupancy threshold is applied in the 'far' zone. It is recalled that an area with a lower occupancy threshold represents a stricter condition to fulfil in order to enable movement into the area. Another option is to define one 'well-known' and one `not yet explored' zone, wherein a relatively lower occupancy threshold is applied in the `not yet explored' zone. Yet another option is to define one 'low-risk' and one 'high-risk' zone, e.g. based on the presence of vulnerable road users, sensitive equipment or the like, wherein a relatively lower occupancy threshold is applied in the 'high-risk' zone.

In such implementations of the method <NUM>, where a position or orientation of one of the sensors <NUM> is determined, a dynamic division into zones of the surface <NUM> dependent on the sensor position/orientation may be used. For example, a FOV of said one of the sensors <NUM> can be determined in relation to the surface <NUM>. The FOV may correspond to those areas on the surface <NUM> from which straight lines can impinge on an aperture of the sensor <NUM>. Geometrically, said areas can be determined by an inverse approach, namely, by projecting a solid angle corresponding to the sensor's <NUM> aperture and collocated with the sensor <NUM> onto the surface <NUM>. The projection thus obtained can then be converted into a set of areas based on a conversion rule, e.g., that an area mi shall be included in the set of areas if at least <NUM>% or at least <NUM>% or some other percentage overlaps with the projection. Regardless of the method by which the FOV is determined, the FOV can be defined to be one zone of the surface <NUM> where the occupancy threshold is relatively lower (corresponding to a more lenient condition on movement), whereas the complement, outside the FOV, is defined as a zone where the occupancy threshold is relatively higher (corresponding to a stricter condition on movement).

<FIG> illustrates a further example FOV configuration, namely, where a forward-facing sensor, e.g., a video camera, is frontally mounted on the AV <NUM>, a nonzero distance above ground level. The FOV <NUM> can be described as an approximate horizontal cone, which has a semi-annular projection on the surface <NUM>. A semicircular area <NUM> immediately in front of the AV <NUM> is hidden from the sensor. This illustrates that the property of being inside or outside the FOV does not necessarily correspond to being near or far from the AV <NUM>. If it is of primary importance to avoid the deadlocking scenario outlined initially, the occupancy threshold based on nearness criterion rather than a FOV criterion. Manual checks, data site-mounted sensors, the presence of fenced-off areas can be relied upon to ensure a desired safety level.

<FIG> is a top view of a combination vehicle composed of a tractor <NUM> and a trailer <NUM>. The tractor <NUM> is equipped with backward-facing sensors. Because of the relative yaw movability of the trailer <NUM>, the FOV of the backward-facing sensors is here dependent on the articulation angle. More precisely, the FOV will have one left-hand component <NUM> and one right-hand component 602R, whereas the hashed area <NUM> behind the tractor <NUM> is outside the FOV. The large sector to the right of the dashed lines is always outside the FOV. Vehicle-carried devices for sensing or estimating the articulation angle while the vehicle is operating are known in the art, and they can provide sufficient input to compute the FOV when necessary.

It is important to note, for each of these static and dynamic options, that the determination <NUM> of the occupancy threshold t(mi) in no way interferes - let alone modifies - the data of the world model data. The occupancy threshold is independent from the world model, and independent from the occupancy scores p(mi) in particular.

With reference to the flowchart in <FIG>, next, it is assessed <NUM> for at least one area mi whether the occupancy score is less than the determined occupancy threshold, that is, whether <MAT> If the inequality is true, then movement of the AV <NUM> into the area mi is enabled 210a. Otherwise, if it is found that <MAT> then movement of the AV <NUM> into the area mi is disabled 210b.

The occupancy threshold determination <NUM> and assessment <NUM> maybe repeated as desired for sufficiently many areas mi to cover a region of interest of the surface <NUM>. For example, the region of interest may be the region currently under consideration for route planning. Alternatively, the region of interest may be a corridor extending from the AV <NUM> to a next destination assigned by a transport mission that is in progress; the width of the corridor can be chosen based on earlier runs, e.g., the smallest width that has been found to allow efficient route planning in normal circumstances.

The decision to enable 210a or disable 210b movement may remain in force until the assessing step <NUM> is repeated. The assessing step <NUM> can be repeated in response to an updating of the world model and/or an updating of the occupancy thresholds. To repeat the assessing step <NUM> in these cases, it may or may not be necessary to execute all of the preceding steps anew.

In a second embodiment, the method <NUM> comprises a further step <NUM> in which a current position of the AV <NUM> is determined. The position may be determined based on data from a global navigation satellite system (GNSS) receiver, cellular-network positioning, inertial measurements, an optical or radio-frequency (RF) fiducial deployed at a known reference location, or the like. Using the current position of the AV <NUM>, it is possible to define a dynamic division of the surface <NUM> into two or more zones.

A simple option is to split the surface <NUM> into two zones Mfar, Mnear and to define the occupancy threshold as follows: <MAT> where the constants t<NUM>, t<NUM> satisfy t<NUM> < t<NUM>. The near zone Mnear may be defined as all points on the surface <NUM> which are closer than a predefined distance to a reference point on the AV <NUM>, such as an approximate center, or a driver position. Alternatively, the near zone Mnear may be defined as all points on the surface <NUM> which are closer than a predefined distance to a nearest point on an approximate horizontal contour of the AV <NUM>. The horizontal contour may alternatively be described as the ground projection of the AV <NUM>. Ways of converting such point sets into equivalent sets of areas mi in the world model have been suggested above in connection with sensor FOVs.

A somewhat more complex option is to divide the surface <NUM> into three or more zones M<NUM>, M<NUM>, M<NUM>,. The zones may be concentric on a reference point on the AV <NUM>, and they may have a circular-annular shape or a shape approximating the horizontal contour of the AV <NUM>. In the case of three zones, the occupancy threshold can be defined as follows: <MAT> where t<NUM> < t<NUM> < t<NUM> and M<NUM> represents the farthest, M<NUM> the intermediate and M<NUM> the nearest zone. A variation is illustrated in <FIG>, where three zones are delimited by two contours <NUM>, <NUM> following (i.e., moving with) the vehicle <NUM>. Here, zone M<NUM> is defined as the points outside the outer contour <NUM>, zone M<NUM> is defined as the points between the inner and outer contours <NUM>, <NUM>, and zone M<NUM> is defined as the points inside the inner contour <NUM>. The contours <NUM>, <NUM> are approximately rectangular. It is noted that the shapes of the contours <NUM>, <NUM> do not coincide with the horizontal contour of the AV <NUM> itself, but has been purposefully elongated in the main direction of movement, i.e., the longitudinal direction. In a further variation, the contours <NUM>, <NUM> can be made asymmetric in the longitudinal direction, in the sense that they project further from the AV <NUM> in the forward direction (+x) than in the reverse direction (-x). It can be expected in many use cases that AVs are more maneuverable in the forward direction, including avoidance maneuvers, and have more refined sensorics towards that direction, so that a less restrictive movementenable criterion is acceptable; this is reflected by the shape of each contour <NUM>, <NUM>.

A still further option is to define the occupancy threshold continuously, as per: <MAT> where d is a distance function, X<NUM> denotes the position of the AV <NUM>, and φ is a nonincreasing function. For example, one may use φ(s) = min{t<NUM>, <NUM>/s}, where t<NUM> is a preset maximum occupancy threshold to be applied.

In each of the first and second embodiments, the method <NUM> may include one or more of the following optional steps after the enabling 210a or disabling 210b of movement into areas mi of the surface <NUM>.

In one optional step <NUM> of the method <NUM>, the speed of the AV <NUM> is controlled such that the AV <NUM> is at least one stopping distance away from any areas mi into which movement is disabled. It is known that the stopping distance is a (generally superlinear) function of speed, which may have a further dependence on road conditions. The criterion of having at least one stopping distance away from any areas mi into which movement is disabled may be relaxed so that it applies only for areas mi ahead of the AV <NUM>, e.g., areas mi belonging to a frontal halfplane relative to the AV <NUM>.

In another optional step <NUM>, a route of the AV <NUM> towards a destination is generated, wherein the route passes only via such areas into which movement has been enabled 210a. Further a time of arrival at the destination is estimated. The estimation may take into account a velocity profile of the route; the velocity profile may observe one of the criteria stated in step <NUM>.

In a further optional step <NUM>, the current position of the AV <NUM> is determined and the occupancy threshold t) is updated. The execution of step <NUM> may end after the current position of the AV <NUM> has been determined and found to differ by less than a predefined threshold distance from the previous position of the AV <NUM>; in this case the new occupancy threshold t) may be set equal to the previous occupancy threshold t).

In a further development of step <NUM>, the updating of the occupancy threshold t) takes into account a current orientation of the AV <NUM>. The orientation may for example affect the FOV of a vehicle-carried sensor <NUM> relative to the surface <NUM>. Further, a change of AV orientation may further lead to a corresponding change in the orientation of a contour that moves with the AV <NUM>, such as one of the contours <NUM>, <NUM> illustrated in <FIG>.

The present disclosure has proposed various ways of safeguarding a desired deterministic behavior of an AV that could also circumvents issues with possible blind spots of the sensor setup. It does so without strict requirements as to how a world model shall be represented, but rather uses an interpretation of the uncertainties inherent to the representation so as to combine a pessimistic behavior (which, in isolation, could lead to low productivity) with an optimistic behavior (which, in isolation, might produce an unsafe system). The invention reaps the benefits of these two behaviors into a balanced model where the interpretation of the world model - and thus the behavior of the ego vehicle - is be made dependent on the closeness to the ego vehicle. As seen in some embodiments, a pessimistic behavior is applied close to the ego vehicle, whereas further away a more optimistic behavior is configured. This leads to higher productivity while maintaining a safety-oriented pessimism in respect of areas close to the ego vehicle.

Claim 1:
A method (<NUM>) of controlling an autonomous vehicle, AV (<NUM>), which is movable on a surface (<NUM>), the method comprising:
obtaining (<NUM>) a world model (<NUM>) of the surface, by which each area (<NUM>) of the surface is associated with a probabilistic occupancy score, wherein the world model is obtained and/or updated based on measurement data from one or more sensors (<NUM>) carried by the AV;
determining (<NUM>) an occupancy threshold to be applied to an area (<NUM>) of the surface;
enabling (210a) movement of the AV into the area if the associated occupancy score is less than the determined occupancy threshold; and
otherwise disabling (210b) movement into the area,
characterized by determining the occupancy threshold on the basis of the area's position, wherein the occupancy threshold is determined to be relatively higher if the area is outside a field of view of the one or more sensors carried by the AV and relatively lower if the area is inside the field of view.