Patent Publication Number: US-2023161047-A1

Title: Sensor Steering for Multi-Directional Long-Range Perception

Description:
CROSS-REFERENCE TO RELATED APPLICATIONS 
     This application is a continuation of U.S. patent application Ser. No. 16/723,693, filed Dec. 20, 2019, which is incorporated herein by reference. 
    
    
     BACKGROUND 
     Active sensors include devices that emit energy, which can reflect off environmental surroundings and can be measured upon return to the device. Active sensors include radar and lidar, among others. Such active sensors may be utilized in areas such as autonomous or semi-autonomous vehicles, robotics, mapping, and security applications. 
     SUMMARY 
     The present disclosure relates to systems, vehicles, and methods that involve adjustment of a steerable lidar unit based on points of interest within an environment. 
     In a first aspect, a system is provided. The system includes a planner unit having a planner controller operable to carry out operations. The operations include determining a plurality of points of interest within an environment of the system and assigning, to each point of interest of the plurality of points of interest, a respective priority score. The system also includes a perception unit with a perception controller operable to carry out operations. The operations include partitioning at least a portion of the environment of the system into a plurality of sectors. Each sector of the plurality of sectors includes at least one point of interest. The system also includes a lidar unit operable to adjust a scanning region to correspond with a respective sector of the plurality of sectors. 
     In a second aspect, a vehicle is provided. The vehicle includes a planner unit with a planner controller operable to carry out operations. The operations include determining a plurality of points of interest within an environment of the vehicle and assigning, to each point of interest of the plurality of points of interest, a respective priority score. The vehicle also includes a perception unit that has a perception controller operable to carry out operations. The operations include partitioning at least a portion of the environment of the vehicle into a plurality of sectors. Each sector of the plurality of sectors includes at least one point of interest. The vehicle also includes a lidar unit operable to adjust a scanning region to correspond with a respective sector of the plurality of sectors. 
     In a third aspect, a method is provided. The method includes determining a plurality of points of interest within an environment of a vehicle and assigning, to each point of interest of the plurality of points of interest, a respective priority score. The method also includes partitioning at least a portion of the environment of the vehicle into a plurality of sectors. Each sector of the plurality of sectors includes at least one point of interest. The method includes, for each sector of the plurality of sectors, adjusting a scanning region of a lidar unit corresponding with the respective sector. The method also includes, for each sector of the plurality of sectors, causing the lidar unit to scan the respective sector. 
     Other aspects, embodiments, and implementations will become apparent to those of ordinary skill in the art by reading the following detailed description, with reference where appropriate to the accompanying drawings. 
    
    
     
       BRIEF DESCRIPTION OF THE FIGURES 
         FIG.  1    illustrates a system, according to an example embodiment. 
         FIG.  2    illustrates various operations involving the system of  FIG.  1   , according to an example embodiment. 
         FIG.  3 A  illustrates a scenario involving the system of  FIG.  1   , according to an example embodiment. 
         FIG.  3 B  illustrates a scenario involving the system of  FIG.  1   , according to an example embodiment. 
         FIG.  3 C  illustrates a scenario involving the system of  FIG.  1   , according to an example embodiment. 
         FIG.  3 D  illustrates a scenario involving the system of  FIG.  1   , according to an example embodiment. 
         FIG.  3 E  illustrates a scenario involving the system of  FIG.  1   , according to an example embodiment. 
         FIG.  3 F  illustrates a scenario involving the system of  FIG.  1   , according to an example embodiment. 
         FIG.  4 A  illustrates a scenario involving the system of  FIG.  1   , according to an example embodiment. 
         FIG.  4 B  illustrates a scenario involving the system of  FIG.  1   , according to an example embodiment. 
         FIG.  4 C  illustrates a scenario involving the system of  FIG.  1   , according to an example embodiment. 
         FIG.  4 D  illustrates a scenario involving the system of  FIG.  1   , according to an example embodiment. 
         FIG.  4 E  illustrates a scenario involving the system of  FIG.  1   , according to an example embodiment. 
         FIG.  5 A  illustrates a vehicle, according to an example embodiment. 
         FIG.  5 B  illustrates a vehicle, according to an example embodiment. 
         FIG.  5 C  illustrates a vehicle, according to an example embodiment. 
         FIG.  5 D  illustrates a vehicle, according to an example embodiment. 
         FIG.  5 E  illustrates a vehicle, according to an example embodiment. 
         FIG.  6    illustrates a method, according to an example embodiment. 
     
    
    
     DETAILED DESCRIPTION 
     Example methods, devices, and systems are described herein. It should be understood that the words “example” and “exemplary” are used herein to mean “serving as an example, instance, or illustration.” Any embodiment or feature described herein as being an “example” or “exemplary” is not necessarily to be construed as preferred or advantageous over other embodiments or features. Other embodiments can be utilized, and other changes can be made, without departing from the scope of the subject matter presented herein. 
     Thus, the example embodiments described herein are not meant to be limiting. Aspects of the present disclosure, as generally described herein, and illustrated in the figures, can be arranged, substituted, combined, separated, and designed in a wide variety of different configurations, all of which are contemplated herein. 
     Further, unless context suggests otherwise, the features illustrated in each of the figures may be used in combination with one another. Thus, the figures should be generally viewed as component aspects of one or more overall embodiments, with the understanding that not all illustrated features are necessary for each embodiment. 
     I. Overview 
     Systems and methods described in various embodiments herein relate to long-range perception using a steerable light detection and ranging (lidar) device that has a limited angular field of view for a given position (e.g., a given azimuthal position). Such systems and methods could be utilized in semi- or fully-autonomous vehicles, such as with self-driving cars and trucks. In such scenarios, the steerable lidar points in one direction at any particular time. However, vehicles that utilize trajectory and/or route planning may benefit from long range visibility in multiple directions at once, or within a brief period of time (e.g., within 3 seconds or less). For example, when making a left turn onto a major road, it may be beneficial for a vehicle to sense oncoming traffic at long range both from the left and from the right, and possibly also from straight ahead. 
     In some embodiments, a method for obtaining information about several regions of interest within a short period of time could be carried out as follows: 
     1. A planner unit creates a list of points of interest within an environment of a sensing vehicle. Each point of interest describes a single location or region in space to be scanned in order to detect, for example, other vehicles, pedestrians, or other moving or non-moving objects. In some embodiments, each point could be assigned a priority score. The priority score for each point could correspond roughly to an inverse of the amount of time it would take for a moving object (e.g., another vehicle) at that point following the road to intersect with a trajectory of the sensing vehicle. As an example, systems and methods described herein could take into account an actual or predicted (e.g., typical average or maximum) speed of other vehicles. As an example, present systems and methods could have information about posted speed limits and/or average speeds of traffic on nearby roadways. Additionally or alternatively, present system and methods could receive information about current or future weather or road conditions. In such a scenario, the less time the sensing vehicle will have to react to a moving object approaching from the given point of interest, the higher the priority score. That is, a first point of interest 100 meters away along a foggy roadway with a posted speed limit of 60 miles per hour may be assigned a higher priority score than that of a second point of interest 100 meters away along a clear roadway with a posted speed limit of 30 miles per hour. In other terms, higher priority scores may be assigned to points of interest corresponding to scenarios where an amount of reaction time or collision avoidance time is lower, a risk of a collision is higher, a traffic density is higher, etc. 
     2. A perception unit collects all of these points of interest (and corresponding priority scores) and partitions or divides at least a portion of the environment into sectors centered at the self-driving car&#39;s location. Each sector may have an azimuthal angular width that corresponds to the angular field of view of the lidar (e.g., between 5-15 degrees in azimuth). The algorithm that does this grouping may collect or aggregate as many points as possible within each sector, and may seek to maximize overlap between adjacent sectors. For example, if the points of interest cannot fit into a single 8 degree sector, but could fit into a single 12 degree sector, the perception unit may create two different 8 degree sectors that overlap in the center 4 degrees. In such scenarios, some points may be located within both sectors and will therefore be detected more frequently. In some embodiments, the grouping algorithm that collects or aggregates points of interest into sectors could include a clustering or aggregation algorithm such as a K-means algorithm (e.g., Lloyd&#39;s algorithm), affinity propagation clustering algorithm, or another type of algorithm that utilizes the physical distance between points of interest and/or the vehicle position/orientation to partition the environment into sectors. 
     3. The perception unit then schedules the lidar to point to different sectors based on a prediction of how long the lidar would need to scan a given sector in order for a perception object to be created (e.g., the amount of time needed to scan a given sector before the perception unit would be expected to recognize a given object). The perception unit allows the lidar to “dwell” on a particular sector for enough time that an object of interest within the sector will be detected with high likelihood by the lidar before the lidar is steered to a different sector. In some embodiments, the lidar may dwell for about 0.75 seconds on each sector. Other dwell times (e.g., between 500 milliseconds and 2 seconds) are possible and contemplated. Additionally or alternatively, the amount of time that the lidar may dwell on each sector could be variable. Furthermore, the dwell time could be dynamically adjustable, based on, for example, weather conditions, time of day, current or historic traffic patterns, current lidar system performance, sector size/volume, distance to point of interest, availability of backup sensor systems to cover a given point of interest, etc. 
     The algorithm that schedules the steering ensures that “high priority” sectors (e.g., those containing points with expected trajectory intersection times less than about six seconds) will be visited, but may ignore sectors with lower priority points if there are too many sectors to ensure that they can all be visited in a timely fashion. In some embodiments, the scheduling algorithm may include a priority-based scheduling algorithm. For example, the priority-based scheduling algorithm could include an earliest deadline first (EDF) or least time-to-go dynamic scheduling algorithm. For example, from among a plurality of potential sectors to scan, the scheduling algorithm may select the sector with the highest priority (effectively the sector with the least potential intersection time). Additionally or alternatively, the scheduling algorithm could include a first come first serve (FCFS) scheduling algorithm, a shortest job first (SJF) scheduling algorithm, or a round robin (RR) scheduling algorithm, among other possibilities. 
     In some embodiments, the described systems and methods could represent a way to more safely navigate vehicular situations in which other vehicles may be approaching from multiple directions. In particular, the lidar may move back and forth to point at oncoming traffic from different directions. In some implementations, this behavior could emerge from the described method without such “back and forth” motion being specifically programmed. 
     The perception unit&#39;s aggregation of points into sectors may attempt to minimize the total number of sectors that are steered to. In such scenarios, more time could be spent doing perception on relevant locations of interest and less time may be spent mechanically driving the lidar sensor to a new target area. For example, each time the sensor is moved to a new pointing direction, 0.25 second or more of sensor repointing time could take up time that could otherwise be spent perceiving the scene. 
     Based on some lidar hardware and sampling rates, the perception unit may “know” that there is sufficient time to steer between 3 different sectors before starting to push the limits of what the planner unit can process using the lidar alone. However, if movement between more than three sectors is needed, too much time might be spent looking away from a given sector for the planner unit to be confident enough that the sector is clear to be able to safely proceed. This situation may be taken into account, with the result that some sectors may be dropped from the lidar movement schedule altogether. For example, the two highest priority sectors may be included at all times. However, if more than two sectors are required, then the lowest priority sectors could be dropped and only the highest priority sectors may be retained by in the sector schedule. While two or three sectors are described in examples herein, it will be understood that the perception unit may alternatively schedule a variable number of sectors (e.g., 3, 5, 15, or more sectors). For example, the perception unit may review a specific set of sectors and dwell times and determine whether a particular scan plan is sufficient or not. Based on the sector review, the perception unit may schedule additional sectors to visit, or may otherwise adjust the sector visit order. 
     In some cases, even if the lidar does not have enough time to scan each sector over a given period of time, the overall system may still be able proceed (e.g., move the vehicle) in many cases because radar or other sensors (e.g., other lidar sensors) can be used to scan the areas that are not scanned by the lidar. 
     In various embodiments, the systems and methods described herein could be applied to lidars with or without affecting their steering (e.g., physical orientation). For example, adjusting a scanning region of the lidar unit could include a redistribution of light pulse power to a respective sector. In such scenarios, sectors having higher priority may be illuminated, while the lower priority sectors could be dropped in some cases. In other words, the systems and methods herein could be applied to other ways to dynamically refocus, redirect, and/or reprioritize lidar regions of interest, including dynamic power modulation, dynamically adjustable focal distance/depth of field, among other possibilities. 
     II. Example Systems 
       FIG.  1    illustrates a system  100 , according to an example embodiment. The system  100  includes a planner unit  110 , a perception unit  120 , and a lidar unit  130 . 
     The planner unit  110  includes a planner controller  112 , which may include a planner processor  114  that executes program instructions stored in a planner memory  116 . As such, the planner controller  112  could be operable to carry out planner operations. The planner operations include determining a plurality of points of interest  119  within an environment  10  of the system. In some embodiments, the points of interest  119  could correspond to locations from which one or more other vehicles  12  are likely to approach the system  100 . In other scenarios, the points of interest  119  could correspond to locations that may be associated with the potential or actual presence of pedestrians, motorcyclists, bicyclists, or other objects. 
     The planner operations also include assigning, to each point of interest of the plurality of points of interest, a respective priority score  117 . 
     In some embodiments, the planner operations of the planner controller  112  may additionally include determining, for each point of interest  119 , a respective intersection time  115 . The respective intersection time  115  is based on when another vehicle  12  approaching from the respective point of interest  119  is predicted to intersect a current trajectory or a potential trajectory of the system  100 . 
     In such scenarios, the respective priority scores  117  could be inversely proportional to the respective intersection time  115 . For example, if a given point of interest  119  is associated with vehicles that approach at a high rate of speed, the assigned priority score will be higher than that of a point of interest  119  substantially the same distance away that is associated with vehicles that approach at a lower rate of speed. In such examples, priority scores  117  may be assigned based on other information. For example, priority scores may be assigned based on actual speeds of oncoming vehicles from around the given point of interest  119 . Additionally or alternatively, priority scores could be assigned based on object information from prior images or point cloud information at the same location and/or similar environment scenarios (e.g., similar traffic patterns, roadways, and/or intersection types). 
     The perception unit  120  includes a perception controller  122 , which may include a perception processor  124  that executes program instructions stored in a perception memory  126 . In such scenarios, the perception controller  122  could be operable to carry out perception operations. The perception operations include partitioning the environment  10  of the system  100  into a plurality of sectors  127 . Each sector of the plurality of sectors  127  includes at least one point of interest  119 . 
     In some embodiments, partitioning the environment  10  of the system  100  into the plurality of sectors  127  could be based on the assigned priority score  117  of at least one point of interest  119 . In some embodiments, each sector of the plurality of sectors  127  could include a predetermined azimuth angle range. As an example, the predetermined azimuth angle range could be between five degrees and fifteen degrees. 
     In various embodiments, the perception operations of the perception controller  122  could additionally include determining a visit order  128  of the plurality of sectors  127 . In such scenarios, determining the visit order  128  could be based on a variety of different factors. For example, the visit order  128  could be determined based on a number of points of interest in a given sector. In such cases, multiple points of interest could be grouped into a single sector to more efficiently scan the particular sector of the environment  10 . Additionally or alternatively, the visit order  128  may be determined based on the respective priority scores  115  for the points of interest  119  in a given sector. For example, the visit order  128  could be based on the estimated or actual intersection time  115 . In such scenarios, the visit order  128  could be determined based on how fast vehicles are predicted to approach from a particular location of roadway. 
     In other embodiments, the visit order  128  could be determined based on an angular slew rate of the adjustable mount  132  of the lidar unit  130 . That is, an amount of time needed to rotate the adjustable mount  132  from an initial pointing direction to a desired pointing direction could be taken into account when assigning the visit order  128 . In such scenarios, a sector could be ignored or scanned by another sensor in cases where the amount of time needed to slew the lidar unit  130  to the desired scanning region (corresponding to a desired pointing direction) would be greater than a respective predicted intersection time  115  for objects approaching from the given sector. 
     Additionally or alternatively, the visit order  128  could be determined based on an actual azimuthal angle of respective sectors of the plurality of sectors and/or an azimuthal angle difference between respective sectors of the plurality of sectors. For example, the visit order  128  could be assigned so as to sweep the pointing direction  136  of the lidar unit  130  through multiple sectors (e.g., instead of dithering between short clockwise and counterclockwise azimuthal movements). 
     The lidar unit  130  includes an adjustable mount  132 . The adjustable mount  132  is operable to rotate the lidar unit  130  toward a respective sector of the plurality of sectors  127 . 
     In some embodiments, the system  100  could include an actuator  134  operable to rotate the lidar unit  130  to an azimuthal angle corresponding to the respective sector of the plurality of sectors  127 . 
     In various embodiments, the lidar unit  130  could also include a transmitter  140  having at least one light-emitter device  142 . The lidar unit  130  may also include a receiver  144  having at least one light-detector device  146 . Additionally or alternatively, the lidar unit  130  may include a lidar controller  150 , which may include a lidar processor  152  that executes program instructions stored in a lidar memory  154 . 
     The lidar controller  150  could be operable to carry out certain lidar operations. For example, the lidar operations could include scanning each respective sector of the plurality of sectors  127  by emitting at least one light pulse into the respective sector. 
     In some embodiments, the lidar operations may also include receiving at least one reflected light pulse from the environment  10 . 
     In such scenarios, the lidar operations could include determining, based on an emission time of the at least one light pulse, a time of flight of the reflected light pulse. Based on the determined time of flight, the lidar operations could include determining a distance to an object (e.g., other vehicles  12 ) in the respective sector based on the time of flight. 
       FIG.  2    is a “swimlane”-type diagram that illustrates various operations  200  involving elements of system  100  of  FIG.  1   , according to an example embodiment. While the various operations  200  or blocks are illustrated as being carried out by specific elements of the system  100  (e.g., planner unit  110 , perception unit  120 , lidar unit  130 , or other computing devices), it will be understood that some operations or blocks could be carried out by other elements of system  100 . Additionally, it will be understood that, in some embodiments, the planner unit  110 , the perception unit  120 , and/or the lidar unit  130  could be physically and/or communicatively combined into one or more units. 
     Operation  210  includes the planner unit  110  determining a plurality of points of interest within the environment  10  of the system  100 . 
     Operation  212  includes the planner unit  110  determining an intersection time from each point of interest. 
     Operation  214  includes the planner unit  110  assigning, based on at least the respective intersection time from operation  212 , a respective priority score to each point of interest. 
     In example embodiments, operation  216  could include transmitting information indicative of the points of interest, intersection times, and/or priority scores to the perception unit  120 . Additionally or alternatively, operation  218  could include repeating operations  210 ,  212 , and  214  according to a periodic or aperiodic planner schedule. 
     Operation  220  includes the perception unit  120  partitioning the environment into a plurality of sectors. 
     Operation  222  includes the perception unit  120  determining a visit order of the sectors based on the priority score of respective points of interest within the given sector. 
     Operation  224  includes the perception unit  120  transmitting information indicative of the visit order and/or the sectors to the lidar unit  130 . 
     Operation  228  includes repeating operations  220  and  222  according to a periodic or aperiodic perception schedule. 
     Operation  230  includes the lidar unit  130  rotating to the first visit order sector. In such scenarios, a rotatable housing could rotate a pointing direction of the lidar unit  130  toward an azimuthal direction associated with the first visit order sector. 
     Operation  232  includes the lidar unit  130  scanning the first visit order sector. In some embodiments, scanning a given sector could include emitting a plurality of light pulses toward various locations within the sector, and receiving a plurality of return pulses. In some embodiments, scanning the given sector could include measuring a time of flight between emission of the light pulses and the time at which the corresponding return pulse is received. 
     Operation  234  may include rotating the lidar unit  130  in azimuthal angle toward the next sector in visit order. 
     Operation  236  could include scanning the next sector. Operation  238  could include repeating operations  230 ,  232 ,  234  and/or  236  according to a period or aperiodic lidar scanning schedule. Operation  240  could include repeating some or all of the various operations  200 . 
       FIG.  3 A  illustrates a scenario  300  involving the system  100  of  FIG.  1   , according to an example embodiment. As an example, the planner unit  110  could generate a list of points of interest (e.g., points of interest  306   a ,  306   b , and  306   c ) within an environment  10  of a vehicle  500 . In such scenarios, each point of interest  306   a ,  306   b , and  306   c  could relate to a single location or region in space for which the planner unit  110  seeks further information. For example, points of interest  306   a ,  306   b , and  306   c  could relate to another vehicle, a pedestrian, a moving object, a stationary object, or another type of object. 
     In some embodiments, each point of interest could be assigned a priority score that may correspond roughly to an inverse of the amount of time it would take for an object, such as another vehicle or another type of moving object at that location following a predicted trajectory, to intersect with the trajectory of vehicle  500 . For example, point of interest  306   a  could be assigned a priority score of 10 (e.g., corresponding to a fast-approaching vehicle), point of interest  306   b  could be assigned a priority of 2 (e.g., corresponding to a slow-moving pedestrian), and point of interest  306   c  could be assigned a priority score of 6 (e.g., corresponding to another vehicle overtaking from a left-hand lane). 
     Subsequently, the perception unit  120  may receive the points of interest (and the corresponding priority scores) and divide or partition at least a portion of the environment  10  into a plurality of sectors centered at the location of vehicle  500  (and/or centered at the location of lidar unit  130 . In such a scenario, each sector could have an azimuthal angular width that is the same as the angular field of view of the lidar (e.g., between 5-15 degrees in azimuth). In other embodiments, the sectors could have an azimuthal angular width based on size of the points of interest and/or an angular extent of several points of interest. In such scenarios, the perception unit  120  may attempt to aggregate as many points as possible within each sector, and maximize overlap between adjacent sectors, when relevant. For example, if the points of interest cannot fit into a single 8 degree sector (e.g., point of interest  306   c ), but could fit into a single 12 degree sector, the perception unit  120  may create two different 8 degree sectors that overlap in the center 4 degrees. In such scenarios, some points may be located within both sectors and will therefore be detected more frequently. Other ways to partition the environment  10  around the vehicle  500  are contemplated and possible. 
     In the illustrated scenario  300 , the partitioned sectors could include: 1) sector  304   a , which corresponds to point of interest  306   a;  2) sector  304   b , which corresponds to point of interest  306   b ; and 3) sectors  304   c  and  304   d , which correspond to point of interest  306   c . Other portions of the environment  10  could also be partitioned into sectors or could remain unpartitioned. 
     Although not shown, scenario  300  could include the lidar unit  130  scanning sector  304   a  (highest priority score), then sectors  304   c  and  304   d  (next highest priority score), followed by sector  304   b  (lowest priority score). It will be understood that while scenario  300  includes three different points of interest, some scenarios may include greater or lesser numbers of points of interest and corresponding sectors. The following scenarios illustrate other potential real-world examples. 
       FIG.  3 B  illustrates a scenario  320  involving the system  100  of  FIG.  1   , according to an example embodiment. Scenario  320  could be based on an unprotected left hand turn where a vehicle  500  in roadway  321  is waiting at a stop sign  323  with the intention of proceeding along trajectory  324 . While roadway  336  has a stop sign  337 , the other roadways do not have a stop. Such a scenario could be similar or identical to an intersection with a two-lane highway. 
     In such an example, three main roadway portions to check are roadway  328  (oncoming traffic from the left), roadway  330  (oncoming traffic from the right), and roadway  336  (oncoming traffic from the front). Other roadways portions (e.g., roadway  334 ,  332 ,  326 , and  338  are less important because vehicles in those roadway portions are not likely to intersect (e.g., potentially collide) with the vehicle  500  or the intended trajectory  324 . 
     Accordingly, the planner unit  110  may assign three points of interest  322   a ,  322   b , and  322   c . Furthermore, the planner unit  110  may assign respective priority scores of 10, 9, and 7, which could be substantially inversely proportional to the speed limit or average speed of hypothetical vehicles approaching vehicle  500  or intended trajectory  324  from the respective points of interest. For example, other vehicles approaching from points of interest  322   a  and  322   b  could be traveling at approximately 60 miles per hour, while other vehicles approaching from point of interest  322   c  may approach at 30 miles per hour. 
     Although not illustrated, the perception unit  120  could partition the environment into three different sectors corresponding to the three different points of interest  322   a ,  322   b , and  322   c . In some embodiments, the sector visit order could be assigned based on the priority score of the respective points of interest in the sector. 
       FIG.  3 C  illustrates a scenario  340  involving the system  100  of  FIG.  1   , according to an example embodiment. In such a scenario, the lidar unit  130  of system  100  may rotate an adjustable mount in azimuthal angle from initial sector  342  to sector  344 , which includes the point of interest  322   a  with the highest priority score of 10. Scenario  340  may include the lidar unit  130  scanning within the sector  344  so as to obtain information about potential objects (or absence thereof). 
       FIG.  3 D  illustrates a scenario  350  involving the system  100  of  FIG.  1   , according to an example embodiment. In such a scenario, the lidar unit  130  of system  100  may rotate or slew an adjustable mount in azimuthal angle from sector  344  to sector  352 , which includes the point of interest  322   b  with the second-highest priority score of 9. Scenario  350  may include the lidar unit  130  scanning within the sector  352  so as to obtain information about potential objects (or absence thereof). 
       FIG.  3 E  illustrates a scenario  360  involving the system  100  of  FIG.  1   , according to an example embodiment. In such a scenario, the lidar unit  130  of system  100  may rotate or slew an adjustable mount in azimuthal angle from sector  352  to sector  362 , which includes the point of interest  322   c  with the lowest priority score of 7. Scenario  360  may include the lidar unit  130  scanning within the sector  362  so as to obtain information about potential objects (or absence thereof). 
       FIG.  3 F  illustrates a scenario  370  involving the system  100  of  FIG.  1   , according to an example embodiment. In such a scenario, the lidar unit  130  of system  100  may rotate or slew an adjustable mount in azimuthal angle from sector  362  back to highest-priority sector  344 , which includes the point of interest  322   a . That is, in some embodiments, the lidar unit  130  may be configured to repeat the same scanning cycle, jumping from one sector to the next, based on priority score and/or sector visit order. Scenario  370  may include the lidar unit  130  re-scanning the sector  344  so as to obtain the latest possible information about potential objects (or absence thereof). 
       FIG.  4 A  illustrates a scenario  400  involving the system  100  of  FIG.  1   , according to an example embodiment. Scenario  400  could be based on a highway merging scenario where a vehicle  500  in roadway  402  is merging onto a highway  404  with the intention of proceeding along trajectory  403 . In such a scenario, the planner unit  110  could identify point of interest  412   a , which could correspond to potential vehicles approaching from closest lane  406  and farthest lane  408 . The planner unit  110  could also identify point of interest  412   b , which may correspond to a slow-moving or stopped vehicle in forward lane  410 . 
     As illustrated, the planner unit  110  could assign priority scores to the points of interest based on, for example, the approach speed of vehicles present in the given locations among other factors. For example, point of interest  412   a  could be assigned a priority score of 9 while point of interest  412   b  could be assigned a priority score of 6. 
     Subsequent to priority score assignment, the perception unit  120  could partition the environment into sectors that each include at least one point of interest. In scenario  400 , point of interest  412   a  could be larger than a single sector azimuth angle range. Accordingly, in some examples, as described below, two sectors may be assigned to a single point of interest. 
       FIG.  4 B  illustrates a scenario  420  involving the system  100  of  FIG.  1   , according to an example embodiment. Scenario  420  could include slewing or rotating the lidar unit  130  counterclockwise (when viewed overhead) from an initial sector  422  to sector  424 , which may correspond to one of the two sectors assigned to highest-priority score point of interest  412   a . Once oriented along the desired pointing direction, the lidar unit  130  may be configured to scan the sector  424 . 
       FIG.  4 C  illustrates a scenario  430  involving the system  100  of  FIG.  1   , according to an example embodiment. Scenario  430  could include slewing or rotating the lidar unit  130  clockwise (when viewed overhead) from sector  424  to sector  432 , which may correspond to the second of the two sectors assigned to highest-priority score point of interest  412   a . Once oriented along the desired pointing direction, the lidar unit  130  may be configured to scan the sector  432 . 
       FIG.  4 D  illustrates a scenario  440  involving the system  100  of  FIG.  1   , according to an example embodiment. Scenario  440  could include slewing or rotating the lidar unit  130  clockwise (when viewed overhead) from sector  432  to sector  442 , which may correspond to the lower-priority score point of interest  412   b . Once oriented along the desired pointing direction, the lidar unit  130  may be configured to scan the sector  442 . 
       FIG.  4 E  illustrates a scenario  450  involving the system  100  of  FIG.  1   , according to an example embodiment. Scenario  450  could include slewing or rotating the lidar unit  130  counterclockwise (when viewed overhead) from sector  442  to sector  424 , which may correspond to the first of two sectors given the highest priority score. Once oriented along the desired pointing direction, the lidar unit  130  may be configured to scan the sector  424 . 
     III. Example Vehicles 
       FIGS.  5 A,  5 B,  5 C,  5 D, and  5 E  illustrate a vehicle  500 , according to an example embodiment. In some embodiments, the vehicle  500  could be a semi- or fully-autonomous vehicle. While  FIGS.  5 A,  5 B,  5 C,  5 D, and  5 E  illustrates vehicle  500  as being an automobile (e.g., a passenger van), it will be understood that vehicle  500  could include another type of autonomous vehicle, robot, or drone that can navigate within its environment using sensors and other information about its environment. 
     The vehicle  500  could include a planner unit (e.g., planner unit  110 ). The planner unit could include a planner controller (e.g., planner controller  112 ) operable to carry out planner operations. The planner operations could include determining a plurality of points of interest within an environment (e.g., environment  10 ) of the vehicle  500 . 
     The planner operations could include assigning, to each point of interest of the plurality of points of interest, a respective priority score (e.g., priority score(s)  117 ). 
     The vehicle  500  includes a perception unit (e.g., perception unit  120 ), which may include a perception controller (e.g., perception controller  122 ) operable to carry out perception operations. The perception operations could include partitioning the environment of the vehicle  500  into a plurality of sectors (e.g., plurality of sectors  127 ). Each sector of the plurality of sectors includes at least one point of interest (e.g., points of interest  119 ). 
     The vehicle  500  includes a lidar unit (e.g., lidar unit  130 ). The lidar unit includes an adjustable mount (e.g., adjustable mount  132 ). The adjustable mount is operable to rotate the lidar unit toward a respective sector of the plurality of sectors. 
     Additionally or alternatively, the vehicle  500  may include one or more sensor systems  502 ,  504 ,  506 ,  508 , and  510 . In some embodiments, sensor systems  502 ,  504 ,  506 ,  508 , and  510  could include system  100  as illustrated and described in relation to  FIG.  1   . In other words, the systems described elsewhere herein could be coupled to the vehicle  500  and/or could be utilized in conjunction with various operations of the vehicle  500 . As an example, the system  100  could be utilized in self-driving or other types of navigation, planning, perception, and/or mapping operations of the vehicle  500 . 
     While the one or more sensor systems  502 ,  504 ,  506 ,  508 , and  510  are illustrated on certain locations on vehicle  500 , it will be understood that more or fewer sensor systems could be utilized with vehicle  500 . Furthermore, the locations of such sensor systems could be adjusted, modified, or otherwise changed as compared to the locations of the sensor systems illustrated in  FIGS.  5 A,  5 B,  5 C,  5 D, and  5 E . 
     In some embodiments, the one or more sensor systems  502 ,  504 ,  506 ,  508 , and  510  could include image sensors. Additionally or alternatively the one or more sensor systems  502 ,  504 ,  506 ,  508 , and  510  could include lidar sensors. For example, the lidar sensors could include a plurality of light-emitter devices arranged over a range of angles with respect to a given plane (e.g., the x-y plane). For example, one or more of the sensor systems  502 ,  504 ,  506 ,  508 , and  510  may be configured to rotate about an axis (e.g., the z-axis) perpendicular to the given plane so as to illuminate an environment around the vehicle  500  with light pulses. Based on detecting various aspects of reflected light pulses (e.g., the elapsed time of flight, polarization, intensity, etc.), information about the environment may be determined. 
     In an example embodiment, sensor systems  502 ,  504 ,  506 ,  508 , and  510  may be configured to provide respective point cloud information that may relate to physical objects within the environment of the vehicle  500 . While vehicle  500  and sensor systems  502 ,  504 ,  506 ,  508 , and  510  are illustrated as including certain features, it will be understood that other types of sensor systems are contemplated within the scope of the present disclosure. 
     While lidar systems with single light-emitter devices are described and illustrated herein, lidar systems with multiple light-emitter devices (e.g., a light-emitter device with multiple laser bars on a single laser die) are also contemplated. For example, light pulses emitted by one or more laser diodes may be controllably directed about an environment of the system. The angle of emission of the light pulses may be adjusted by a scanning device such as, for instance, a mechanical scanning mirror and/or a rotational motor. For example, the scanning devices could rotate in a reciprocating motion about a given axis and/or rotate about a vertical axis. In another embodiment, the light-emitter device may emit light pulses towards a spinning prism mirror, which may cause the light pulses to be emitted into the environment based on an angle of the prism mirror angle when interacting with each light pulse. Additionally or alternatively, scanning optics and/or other types of electro-opto-mechanical devices are possible to scan the light pulses about the environment. While  FIGS.  5 A- 5 E  illustrate various lidar sensors attached to the vehicle  500 , it will be understood that the vehicle  500  could incorporate other types of sensors. 
     IV. Example Methods 
       FIG.  6    illustrates a method  600 , according to an example embodiment. It will be understood that the method  600  may include fewer or more steps or blocks than those expressly illustrated or otherwise disclosed herein. Furthermore, respective steps or blocks of method  600  may be performed in any order and each step or block may be performed one or more times. In some embodiments, some or all of the blocks or steps of method  600  may relate to elements of system  100  and/or vehicle  500  as illustrated and described in relation to  FIG.  1    and  FIG.  5   , respectively. Furthermore, some or all of the block or steps of method  600  may relate to various operations  200  of the system  100  as illustrated and described in relation to  FIG.  2   . Additionally or alternatively, steps or blocks of method  600  may relate to any of scenarios  300 ,  320 ,  340 ,  350 ,  360 ,  370 ,  400 ,  420 ,  430 ,  440 , or  450 , as illustrated and described in relation to  FIGS.  3 A- 3 F and  4 A- 4 E , respectively. 
     Block  602  includes determining a plurality of points of interest (e.g., points of interest  306   a ,  306   b , and/or  306   c , etc.) within an environment (e.g., environment  10 ) of a vehicle (e.g., vehicle  500 ). In some embodiments, the points of interest could correspond to locations from which one or more other vehicles (e.g., other vehicles  12 ) are likely to approach. 
     Block  604  includes assigning, to each point of interest of the plurality of points of interest, a respective priority score. 
     Block  606  includes partitioning at least a portion of the environment of the vehicle into a plurality of sectors (e.g., sectors  304   a ,  304   b ,  304   c , and  304   d , etc.). Each sector of the plurality of sectors includes at least one point of interest. 
     Block  608  includes, for each sector of the plurality of sectors, adjusting a pointing direction (e.g., pointing direction  136 ) or a scanning region (e.g., scanning region  137 ) of a lidar unit (e.g., lidar unit  130 ) corresponding with the respective sector. 
     Block  610  includes, for each sector of the plurality of sectors, causing the lidar unit to scan the respective sector. 
     In some embodiments, method  600  may include determining, for each point of interest, a respective intersection time (e.g., intersection times  115 ). The respective intersection time could be based on a future time when another (actual or potential) vehicle approaching from the respective point of interest is predicted to intersect a current trajectory or a potential trajectory of the vehicle. In such scenarios, the respective priority scores could be substantially inversely proportional to the respective intersection time. 
     In some embodiments, partitioning at least a portion of the environment of the vehicle into the plurality of sectors could be based on the assigned priority score of at least one point of interest. 
     Furthermore, in various examples, each sector of the plurality of sectors could include a predetermined azimuth angle range. For example, the predetermined azimuth angle range could be between five degrees and fifteen degrees. 
     In some embodiments, adjusting the pointing direction of the lidar unit could include causing an actuator to rotate the lidar unit to a pointing direction or an azimuthal angle corresponding to the respective sector. 
     In example embodiments, causing the lidar unit to scan the respective sector could include emitting at least one light pulse into the respective sector and receiving at least one reflected light pulse from the environment. In such scenarios, causing the lidar unit to scan the respective sector could also include determining, based on an emission time of the at least one light pulse, a time of flight of the reflected light pulse. Additionally, causing the lidar unit to scan the respective sector may additionally include determining a distance to an object in the respective sector based on the time of flight. 
     In some examples, method  600  could additionally include determining a visit order (e.g., visit order  128 ) of the plurality of sectors. In such scenarios, determining the visit order could be based on at least one of: a number of points of interest in a given sector, the respective priority scores for the points of interest in a given sector, an angular slew rate of the lidar unit, or an azimuthal angle difference between respective sectors of the plurality of sectors. 
     In various embodiments, the adjusting and causing steps for each sector of the plurality of sectors could be performed according to the determined visit order. 
     The particular arrangements shown in the Figures should not be viewed as limiting. It should be understood that other embodiments may include more or less of each element shown in a given Figure. Further, some of the illustrated elements may be combined or omitted. Yet further, an illustrative embodiment may include elements that are not illustrated in the Figures. 
     A step or block that represents a processing of information can correspond to circuitry that can be configured to perform the specific logical functions of a herein-described method or technique. Alternatively or additionally, a step or block that represents a processing of information can correspond to a module, a segment, or a portion of program code (including related data). The program code can include one or more instructions executable by a processor for implementing specific logical functions or actions in the method or technique. The program code and/or related data can be stored on any type of computer readable medium such as a storage device including a disk, hard drive, or other storage medium. 
     The computer readable medium can also include non-transitory computer readable media such as computer-readable media that store data for short periods of time like register memory, processor cache, and random access memory (RAM). The computer readable media can also include non-transitory computer readable media that store program code and/or data for longer periods of time. Thus, the computer readable media may include secondary or persistent long term storage, like read only memory (ROM), optical or magnetic disks, compact-disc read only memory (CD-ROM), for example. The computer readable media can also be any other volatile or non-volatile storage systems. A computer readable medium can be considered a computer readable storage medium, for example, or a tangible storage device. 
     While various examples and embodiments have been disclosed, other examples and embodiments will be apparent to those skilled in the art. The various disclosed examples and embodiments are for purposes of illustration and are not intended to be limiting, with the true scope being indicated by the following claims.