Patent Publication Number: US-11048260-B2

Title: Adaptive scaling in trajectory generation

Description:
BACKGROUND 
     Various methods, apparatuses, and systems are utilized by autonomous vehicles to guide such autonomous vehicles through environments including various static and dynamic objects. For instance, autonomous vehicles utilize route planning methods, apparatuses, and systems to guide autonomous vehicles through congested areas with other moving vehicles (autonomous or otherwise), moving people, stationary buildings, etc. In some examples, generating a route may be computationally intensive and/or may not provide a safe or comfortable route for passengers. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       The detailed description is described with reference to the accompanying figures. In the figures, the left-most digit(s) of a reference number identifies the figure in which the reference number first appears. The use of the same reference numbers in different figures indicates similar or identical components or features. 
         FIG. 1  is a schematic diagram illustrating an example implementation of a system for generating a trajectory, generating a drivable area, and evaluating costs, in accordance with embodiments of the disclosure. 
         FIG. 2  is an illustration of a reference trajectory comprising regions of differing point densities, in accordance with embodiments of the disclosure. 
         FIG. 3  is an illustration of determining weight(s) for a cost associated with generating a target trajectory based on a reference trajectory when navigating around an obstacle, in accordance with embodiments of the disclosure. 
         FIG. 4  is an illustration of determining weight(s) for a cost associated with generating a target trajectory based on a reference trajectory when changing lanes, in accordance with embodiments of the disclosure. 
         FIG. 5A  is an illustration of a dilated region, a collision region, and a safety region associated with a drivable area, in accordance with embodiments of the disclosure. 
         FIG. 5B  is an illustration of regions associated with a drivable area based at least in part on a classification of an object in an environment and/or on a velocity of a vehicle in the environment, in accordance with embodiments of the disclosure. 
         FIG. 6  is an illustration of updating a region based on identifying an object such as a bicyclist proximate to the vehicle, in accordance with embodiments of the disclosure. 
         FIG. 7  depicts a block diagram of an example system for implementing the techniques described herein. 
         FIG. 8  depicts an example process for determining a point density for points associated with a reference trajectory and evaluating costs at the points to generate a target trajectory, in accordance with embodiments of the disclosure. 
         FIG. 9  depicts an example process for determining weights for points associated with a reference trajectory and evaluating, based on the weights, costs at the points to determine a target trajectory, in accordance with embodiments of the disclosure. 
     
    
    
     DETAILED DESCRIPTION 
     As discussed above, generating a route for an autonomous vehicle through an environment may be computationally intensive and/or may not provide a safe or comfortable route for passengers. This application describes techniques for reducing a computational burden of planning a route through an environment, may improve an accuracy and precision of trajectory generation, and/or may improve safety and comfort of routes for passengers. For instance, the techniques described herein may reduce the computational burden by adaptively scaling a density of trajectory points (e.g., points along the trajectory used for determining associated costs and controls) based on the level of activity (e.g., cost(s) associated with curvature(s) and/or object(s)) along the route. By using a lower density of trajectory points for portions of the route that have lower activity (e.g., lower cost(s) associated with curvature(s) and/or fewer nearby object(s)), the computational intensity of trajectory generation can be reduced. Such costs as referred to herein may be, as non-limiting examples, proportional with respect to the curvature or distance, an L1, L2, quadratic, Huber, polynomial, exponential, function or the like, including any combination thereof. As another example, the techniques described herein may increase accuracy and/or precision of trajectory generation by using a relatively higher density of trajectory points for portions of the route that have higher activity (e.g., higher cost(s) associated with curvature(s) and/or more nearby object(s)). By way of another example, cost functions quantitatively balance goals of comfort, vehicle dynamics, safety, and the like. Techniques discussed herein include adaptively scaling weights associated with one or more costs to enhance safety and/or comfort when determining the contours of a trajectory through an environment. Further, regions establishing buffers around objects in an environment can be increased or decreased in size depending on a classification of an object (e.g., pedestrians, vehicles, etc.) and/or depending on a velocity of the autonomous vehicle in the environment. 
     This disclosure is directed to techniques for generating trajectories and drivable areas for navigating a vehicle in an environment. For example, an autonomous vehicle can receive a reference trajectory representing an initial path or trajectory for the autonomous vehicle to follow in an environment. A point density of points on the reference trajectory can be based at least in part on a cost associated with a curvature of the reference trajectory and/or on a cost associated with a distance between the reference trajectory and an obstacle in the environment. A weight associated with various costs can be based on triggers such as jumps or discontinuities in a reference trajectory or obstacle costs meeting or exceeding a threshold. Further, a drivable area can represent a region in the environment where the autonomous vehicle can travel. Boundaries of the drivable region can include information about object(s) in the environment and probabilistic distances between the boundaries and the object(s). In some examples, regions associated with the drivable area can be based on a classification of objects in the environment and/or on a velocity of the autonomous vehicle in the environment. A target trajectory can be generated with respect to the reference trajectory based at least in part on evaluating one or more costs at points on the reference trajectory relative to the drivable area, whereby a density of points on the reference trajectory and weights associated with cost(s) can be determined as discussed herein. 
     In some examples, a planning system of an autonomous vehicle can include one or more layers for generating and optimizing one or more trajectories for the autonomous vehicle to traverse an environment. For example, a first layer of the planning system can receive or determine a lane reference (also referred to as a reference trajectory), which may correspond to or be associated with a center of a road segment. Costs associated with points on the lane reference can be evaluated and optimized to generate a first target trajectory. For example, a state of the vehicle can be evaluated along each point on the lane reference (or reference trajectory) to evaluate changing states of the vehicle over time (e.g., sometimes referred to as a “rollout”). In some examples, the first target trajectory can be provided to a second layer of the planning system, whereby the first target trajectory is used as a reference trajectory. Costs associated with points on the reference trajectory can be evaluated and optimized to generate a second target trajectory. In some instances, the second target trajectory can be optimized further or can be used to control the autonomous vehicle. In some examples, the first layer can optimize the reference trajectory with respect to a distance between points and/or the second layer can optimize the reference trajectory with respect to a time between points, although other combinations are contemplated here. 
     In some examples, a reference trajectory representing an initial path or trajectory for an autonomous vehicle to follow can be generated or received by a computing device of the autonomous vehicle. In some examples, the reference trajectory can correspond to a centerline of a road segment, although the reference trajectory can represent any path in an environment. In some examples, points on the reference trajectory can be established, selected, sampled, or otherwise determined based at least in part on a cost associated with a curvature value associated with the reference trajectory and/or based at least in part on a cost associated with a distance between a point associated with a region of the reference trajectory and one or more obstacles in the environment. For example, a region associated with a relatively high curvature value may represent a maneuver around an obstacle in the environment or may represent a maneuver such as turning a corner, parking, and the like. By way of another example, a region of the reference trajectory can be relatively close to an obstacle in the environment. Accordingly, in the case of a high cost associated with a curvature and/or of a high cost associated with a distance to an obstacle, the region can be associated with a relatively higher point density of points on the reference trajectory. In some cases, one or more costs can be evaluated at the points on the reference trajectory to generate a target trajectory, whereby the autonomous vehicle can be controlled to follow the target trajectory. Accordingly, dynamically varying the density of points on the reference trajectory based on a cost associated with a curvature of the reference trajectory and/or based on a cost associated with a distance between the point on the reference trajectory and a point associated with an obstacle can affect an amount of downstream processing in connection with generating a target trajectory. 
     In some examples, a target trajectory can be generated with respect to the reference trajectory by evaluating one or more costs associated with points on the reference trajectory. In general, the one or more costs may include, but is not limited to a reference cost, an obstacle cost, a lateral cost, a longitudinal cost, and the like. Additional details of the one or more costs are provided below. Further, a cost can be associated with a weight that can increase or decrease a cost associated with a trajectory or a cost associated with a point of the trajectory. In some examples, the operations discussed herein can include decreasing a weight associated with a cost based on triggers such as discontinuities in a reference trajectory or one or more costs meeting or exceeding a threshold. By way of example, and without limitation, a weight associated with a reference cost (discussed below) can be decreased when the vehicle is navigating around a double-parked vehicle or while changing lanes. In some examples, a reference cost can comprise a cost associated with a difference between a point (also referred to as a reference point) on the reference trajectory and a corresponding point (also referred to as a point or a target point) on the target trajectory, whereby the difference represents one or more difference in a yaw, lateral offset, velocity, acceleration, curvature, curvature rate, and the like. In some examples, decreasing a weight associated with a reference cost can reduce a penalty associated with the target trajectory being located a distance away from the reference trajectory, which can provide smoother transitions leading towards safer and/or more comfortable vehicle operations. For example, decreasing the reference costs associated with a portion of the reference trajectory can lead to increasing a buffer between the vehicle and an obstacle in the environment. 
     In other examples, such as while changing lanes, selecting a weight for a reference cost can be combined with other checks to provide safe and comfortable vehicle dynamics. For example, during a lane change action a target trajectory can be checked to maintain a lateral displacement less than a threshold distance given a distance traveled forward by the vehicle and/or given a period of travel time. By way of example, and without limitation, a vehicle may be constrained to traveling 1 meter in a lateral direction for every 2 meters the vehicle travels in a forward direction, thereby reducing a sideways force during the lane change. By way of another example, and without limitation, the vehicle may further be constrained to traveling 1 meter in a lateral direction for a sliding window of time, such as two seconds. Of course, other distances and times are contemplated herein. 
     In some examples, an obstacle cost can comprise a cost associated with a distance between a point on the reference trajectory or the target trajectory and a point associated with an obstacle in the environment. By way of example, the point associated with the obstacle can correspond to a point on a boundary of a drivable area or can correspond to a point associated with the obstacle in the environment. In some examples, an obstacle in the environment can include, but is not limited to a static object (e.g., building, curb, sidewalk, lane marking, sign post, traffic light, tree, etc.) or a dynamic object (e.g., a vehicle, bicyclist, pedestrian, animal, etc.). In some examples, a dynamic object can also be referred to as an agent. In some examples, a static object or a dynamic object can be referred to generally as an object or an obstacle. 
     In some examples, a lateral cost can refer to a cost associated with steering inputs to the vehicle, such as maximum steering inputs relative to a velocity of the vehicle. 
     In some examples, a longitudinal cost can refer to a cost associated with a velocity and/or acceleration of the vehicle (e.g., maximum braking and/or acceleration). 
     As introduced above, the vehicle can determine a drivable area that represents a region in the environment where the vehicle can travel. In some examples, a computing device of an autonomous vehicle can receive sensor data captured by one or more sensors of the autonomous vehicle and can determine one or more objects in the environment and/or attributes of the one or more objects in the environment. In some examples, the autonomous vehicle can utilize the object(s) and/or the attributes of the object(s) to determine which object(s) should be included in determining extents of the drivable area. Accordingly, the autonomous vehicle can plan a trajectory (e.g., a reference trajectory and/or the target trajectory) within the extents of the drivable area. 
     In some examples, the drivable area can comprise a dilated region, a collision region, and/or a safety region. For example, the dilated region can be statically or dynamically generated with respect to a lane boundary to represent the largest extent of the drivable area, and can comprise information about object(s) in the environment and probabilistic distances between the boundaries and the object(s) and the reference and/or target trajectories. For example, the dilated region can represent a buffer associated with a boundary based at least in part on a distance (e.g., half of a width of a vehicle) plus some distance based on an uncertainty of sensor noise, which may be based at least in part on an object classification. Further, and in some examples, the collision region can represent a smaller drivable area than the dilated region (e.g., representing a greater distance between an obstacle and a boundary of the collision region) representing a region for the autonomous vehicle to avoid to further reduce a likelihood that the autonomous vehicle will collide with an object in the environment. In some examples, a cost associated with entering the collision region can be relatively high (relative to the safety region). In some examples, the safety region can represent a region smaller than the collision region and the dilated region to provide a buffer between the autonomous vehicle and the object in the environment. In some examples, a cost associated with entering the safety region can be lower than a cost associated with the collision region. In some examples, the collision region and/or the safety region can also be associated with information about object(s) in the environment and probabilistic distances between the boundaries and the object(s). In some examples, the autonomous vehicle can evaluate costs based at least in part on distance(s) between points on the reference trajectory and/or the target trajectory and one or more points associated with the regions, as discussed herein. In some examples, the cost(s) associated with the region(s) may vary. For example, a cost and/or weight associated with the safety region may be relatively less than a cost and/or weight associated with the collision region. 
     In some examples, a size of the region(s) can be based at least in part on a classification type of objects that the regions represent or are otherwise associated with in the environment. For example, a safety region associated with a pedestrian may be associated with a first distance or size and a safety region associated with a vehicle may be associated with a second distance or size that is different than the first distance or size. By way of example, and without limitation, a safety region associated with the pedestrian can be larger than a safety region associated with a vehicle, which may result in the autonomous vehicle giving a larger buffer to the pedestrian than to the vehicle, assuming other factors are the same. 
     In some examples, a size of the region(s) can be based at least in part on a velocity of the autonomous vehicle. For example, a safety region associated with a pedestrian may be associated with a first distance or size while the autonomous vehicle is traveling at a first velocity (or is predicted to travel at a reference point corresponding to the region) and may be associated with a second distance or size while the autonomous vehicle is traveling at a second velocity (or is predicted to travel at a reference point corresponding to the region) that is different than the first velocity. By way of example, and without limitation, a size of the safety region can be increased as the velocity of the autonomous vehicle increases, which may result in the autonomous vehicle giving a larger buffer to the pedestrian at a higher velocity, assuming other factors are the same. 
     The techniques discussed herein can improve a functioning of a computing device in a number of additional ways. In some cases, varying a density of points associated with the reference trajectory can reduce an amount of processing by increasing a density of points in regions of high interest or in regions with high activity and by decreasing a density of points in regions of low interest or low activity. Further, because additional processing steps, such as evaluating costs, are based on the points on the reference trajectory, reducing a number of points to consider can represent a large reduction in overall processing without sacrificing accuracy or precision. In some examples, selecting weights associated with a cost can result in safer trajectories (e.g., by increasing a distance between an object and a vehicle in the environment) and/or can result in more comfortable trajectories (e.g., by reducing lateral acceleration during a lane change action). In some examples, varying a size of regions associated with a drivable area can improve safety by generating and/or modifying the regions based on a classification type and/or a velocity of the autonomous vehicle. Further, the techniques discussed herein can be used alone or in combination to improve safety and/or comfort in a variety of systems that generate trajectories. These and other improvements to the functioning of the computer are discussed herein. 
     The techniques described herein can be implemented in a number of ways. Example implementations are provided below with reference to the following figures. Although discussed in the context of an autonomous vehicle, the methods, apparatuses, and systems described herein can be applied to a variety of systems (e.g., a sensor system or a robotic platform), and are not limited to autonomous vehicles. In another example, the techniques can be utilized in an aviation or nautical context. Additionally, the techniques described herein can be used with real data (e.g., captured using sensor(s)), simulated data (e.g., generated by a simulator), or any combination of the two. 
       FIG. 1  is a schematic diagram illustrating an example implementation of a system for generating a trajectory, generating a drivable area, and evaluating costs, in accordance with embodiments of the disclosure. More specifically,  FIG. 1  illustrates an example environment  100  in which a vehicle  102  is positioned. In the illustrated example, the vehicle  102  is driving in the environment  100 , although in other examples the vehicle  102  may be stationary and/or parked in the environment  100 . One or more objects, or agents, also are in the environment  100 . For instance,  FIG. 1  illustrates additional vehicles  104   a  and  104   b  and pedestrians  106   a  and  106   b  in the environment  100 . Of course, any number and/or type of objects can additionally or alternatively be present in the environment  100 . 
     For the purpose of illustration, the vehicle  102  can be an autonomous vehicle configured to operate according to a Level 5 classification issued by the U.S. National Highway Traffic Safety Administration, which describes a vehicle capable of performing all safety-critical functions for the entire trip, with the driver (or occupant) not being expected to control the vehicle at any time. In such an example, since the vehicle  102  can be configured to control all functions from start to stop, including all parking functions, it can be unoccupied. This is merely an example, and the systems and methods described herein can be incorporated into any ground-borne, airborne, or waterborne vehicle, including those ranging from vehicles that need to be manually controlled by a driver at all times, to those that are partially or fully autonomously controlled. Additional details associated with the vehicle  102  are described below. 
     In the example of  FIG. 1 , the vehicle  102  can be associated with one or more sensor systems  108 . The sensor system(s)  108  can generate sensor data  110 , which can be utilized by vehicle computing device(s)  112  associated with the vehicle  102  to recognize the one or more objects, e.g., the vehicles  104  and the pedestrians  106 . The sensor system(s)  108  can include, but is/are not limited to, light detection and ranging (LIDAR) sensors, radio detection and ranging (RADAR) sensors, Time of Flight sensors, ultrasonic transducers, sound navigation and ranging (SONAR) sensors, location sensors (e.g., global positioning system (GPS), compass, etc.), inertial sensors (e.g., inertial measurement units, accelerometers, magnetometers, gyroscopes, etc.), cameras (e.g., RGB, IR, intensity, depth, etc.), wheel encoders, microphones, environment sensors (e.g., temperature sensors, humidity sensors, light sensors, pressure sensors, etc.), etc. 
     In at least one example, the vehicle computing device(s)  112  can include a perception system, which can perform object detection, segmentation, and/or classification based at least in part on the sensor data  110  received from the sensor system(s)  108 . For instance, the perception system can detect the vehicles  104   a  and  104   b  and/or the pedestrians  106   a  and  106   b  in the environment  100  based on the sensor data  110  generated by the sensor system(s)  108 . Additionally, the perception system can determine an extent of the vehicles  104   a  and  104   b  and/or the pedestrians  106   a  and  106   b  (e.g., height, weight, length, etc.), a pose of the vehicles  104   a  and  104   b  and/or the pedestrians  106   a  and  106   b  (e.g., x-coordinate, y-coordinate, z-coordinate, pitch, roll, yaw), etc. The sensor system(s)  108  can continuously generate the sensor data  110  (e.g., in near-real time), which can be utilized by the perception system (and other systems of the vehicle computing device(s)  112 ). 
     The vehicle computing device(s)  112  can also include a drivable area component  114 , a trajectory generation component  116 , and a cost(s) component  118 . In some examples, although discussed separately, the trajectory generation component  116  and the cost(s) component  118  (and/or other components discussed herein) can be integrated into a single component or algorithm. In general, the drivable area component  114  can include functionality to generate and/or determine a drivable area  120 , in accordance with the embodiments discussed herein. In general, the trajectory generation component  116  can include functionality to generate a reference trajectory  122  and/or a target trajectory  124  within the drivable area  120 , in accordance with the embodiments discussed herein. In general, the cost(s) component can include functionality to evaluate one or more costs to generate the target trajectory  124  with respect to the reference trajectory  122 . 
     In some examples, the drivable area  120  may be a virtual area in the environment  100  in which the vehicle  102  can travel safely through the environment  100 , for example, relative to objects including the vehicles  104   a ,  104   b  and the pedestrians  106   a ,  106   b.    
     As illustrated in  FIG. 1  and detailed further herein, the drivable area  120  may be defined by virtual boundaries  126  and  128 . In the example drivable area  120  illustrated in  FIG. 1 , the drivable area  120  may have a variable width  130 , for example, in the width or lateral direction of the vehicle  102 .  FIG. 1  illustrates a first width  130   a  and a second width  130   b , which collectively, and along with other widths at other positions in the drivable area  120 , may be referred to as the width  130 . As described herein, attributes of the virtual boundaries  126  and  128 , including the width  130 , may be determined based at least in part on the sensor data  110  and/or determinations made by the perception system. For example, in some implementations, the virtual boundaries  126  and  128  may be determined based on information about the objects in the environment  100 , which may include information about semantic classifications and/or probabilistic models. In at least some examples, the virtual boundaries  126  and  128  may be encoded with information (such as lateral distance to the nearest object, semantic classification of the nearest object, related probabilities of such distance and classification, etc.). In some examples, the drivable area  120  can be generated in accordance with the techniques discussed in U.S. patent application Ser. No. 15/982,694, filed May 17, 2018, which is hereby incorporated by reference, in its entirety. 
     As also illustrated in  FIG. 1 , upon determining the drivable area  120 , the vehicle computing device(s)  112  also may determine a reference trajectory  122  along which the vehicle  102  may travel through the environment  100 . The planner system can determine routes and/or trajectories to use to control the vehicle  102  based at least in part on the sensor data  110  received from the sensor system(s)  108  and/or any determinations made by the perception system. For instance, the planning system can evaluate one or more costs to generate the target trajectory  124  with respect to the reference trajectory  122  to navigate safely through the environment relative to the vehicle  104   a ,  104   b  and the pedestrians  106   a ,  106   b.    
     More specifically,  FIG. 1  illustrates a scenario in which the vehicle  102  is travelling through the environment  100 , generally in the direction of arrow  132 . The vehicle  102  is travelling on a road  134  having a first lane  136  and a second lane  138 . The vehicle  102  is in the first lane  136 , behind the vehicle  104   a , and, in the example, is travelling relatively faster than the vehicle  104   a . For example, the vehicle  104   a  may be slowing down to turn left, to parallel park on the left side of the road  134 , to drop off a passenger or delivery on the left shoulder of the road, or the like. In examples of this disclosure, the vehicle  102 , for example, by executing the drivable area component  114 , may determine the drivable area  120  as a precursor to determining whether the vehicle  102  can safely navigate around the slowing vehicle  104   a . More specifically, the vehicle computing device(s)  112 , using the sensor data  110 , may determine information about objects in the environment  100 , which may include the vehicle  104   a , the vehicle  104   b , the pedestrian  106   a , the pedestrian  106   b , and/or additional objects. For example, the vehicle computing device(s)  112 , using the drivable area component  114 , may fuse or combine information about each of the objects to configure the drivable area  120 . Such a drivable area  120  may, in turn, be used to determine the reference trajectory  122  and/or the target trajectory  124  (e.g., by providing constraints and/or boundaries) along which the vehicle  102  may travel. In some examples, the reference trajectory  122  can be generated prior to or substantially simultaneously (within technical tolerances) as the drivable area  120 . 
     With further reference to  FIG. 1 , the drivable area  120  may have varying widths, for example, the first width  130   a  and a second width  130   b , and those widths are determined at least in part based on information about the objects in the environment  100 . For example, at a position immediately in front of the vehicle  102  in the direction illustrated by arrow  132 , the width  130   a  generally spans the entire width of the road  134 . At edges of the first lane  136  and the second lane  138 , a slight offset relative to the shoulder may be provided, although such an offset may be excluded in other embodiments. Further along the road  134  in the direction of the arrow  132 , the drivable area  120  begins to narrow as the boundary  128  gradually moves away from the left shoulder until the drivable area  120  is completely confined in the right lane  138 . This narrowing of the drivable area  120  is a result of the presence and/or the relative deceleration of the vehicle  104   a . Moreover, immediately adjacent the vehicle  104   a , the drivable area  120  may further narrow, for example, to provide a minimum lateral offset relative to the vehicle  104   a . Similarly, on the right side of the drivable area  120 , offsets may be provided proximate the first pedestrian  106   a  and the vehicle  104   b , which is parked on the right shoulder of the road  134 . In some examples, such a drivable area  120  may also be determined based on a predicted trajectory of objects, such as that of the pedestrian  106   a . As a non-limiting example depicted in  FIG. 1 , the drivable area  120  indents slightly near the pedestrian  106   a  in anticipation of the pedestrian  106   a  continuing walking in the direction of travel  132 . The length of such an indent may be based on, for example, the velocities of the vehicle  102  and the pedestrian  106   a.    
     As described further herein, the width  130  of the drivable area  120  varies as a result of information about objects in the environment  100 . Thus, for example, offsets are provided proximate the vehicle  104   a , the vehicle  104   b , and the pedestrian  106   a . These offsets may serve to provide a safety margin or minimum distance of the vehicle  102  from the objects, as the vehicle  102  travels in the drivable area  120 . In some implementations, the offsets and/or the minimum distances may differ among and between objects in the environment  100 . For example, semantic information about the objects may be used to determine appropriate offsets. Thus, in  FIG. 1 , pedestrians may be associated with a first classification and vehicles may be associated with a second classification. The first classification may correspond to a minimum offset larger than a minimum offset to which the second classification corresponds. Moreover, vehicles within the second classification may be further classified as static, as in the case of the vehicle  104   b  or dynamic, as in the case of the vehicle  104   a . An offset relative to the moving vehicle  104   a  may be greater than the offset associated with the parked vehicle  104   b , for example. In further examples, probabilistic information about the objects may also or alternatively be used to determine the offsets. For example, there may be some uncertainty associated with the actions of the moving pedestrian  106   a  and/or the moving vehicle  104   a , for example, because the pedestrian  106   a  or the driver of the vehicle  104   a  may change course. Probabilistic information may also be used to account for sensor inaccuracies, for example, associated with the sensor system(s)  108 . 
     The drivable area  120  may allow for more efficient navigation around the vehicle  104   a  when compared with conventional navigation techniques. In conventional solutions, for example, the vehicle  102  may slow down as it approaches the vehicle  104   a , for example, to maintain a minimum distance between the vehicle  102  and the vehicle  104   a , and only upon stopping or slowing to a threshold speed may the vehicle  102  begin to seek out an alternative route around the vehicle  104   a . Alternatively, the vehicle  102  may idle in the lane  136  until the vehicle  104   a  turns, parks, moves, accelerates, or the like. However, in implementations of this disclosure, the drivable area  120  considers multiple objects as well as information about the objects to provide a more robust understanding of the environment  100 . This understanding may enhance decision making and/or allow for more effective and efficient control of the vehicle  102  as it travels through the environment  100 . With specific regard to  FIG. 1 , such enhanced decision-making may allow the vehicle to traverse along the target trajectory  124 , without the need to slow down behind the vehicle  104   a  and/or await additional decision making. Further, the drivable area  120  provides boundaries for a wide range of possible trajectories which may be employed to safely navigate a portion of a path. By providing such a wide range of safe trajectories, the vehicle  102  is able to quickly overcome any hazards, while incorporating information about all objects in a scene. 
       FIG. 1  illustrates a single example of using a drivable area to navigate in an environment. Myriad other examples also are contemplated. For example, the drivable area component  114  may be used in any environment to better navigate with respect to objects. 
       FIG. 2  is an illustration of a reference trajectory comprising regions of differing point densities, in accordance with embodiments of the disclosure. More specifically,  FIG. 2  illustrates an example environment  200  in which the vehicle  102  is positioned.  FIG. 2  further illustrates an obstacle  202  in the environment  200 , whereby a reference trajectory  204  represents an initial path for the vehicle  102  to navigate around the obstacle  202  in the environment  200 . In some examples, the obstacle  202  corresponds to the vehicle  104   a  of  FIG. 1 , although the obstacle  202  may represent any static object or dynamic object in the environment  200 . Further, the reference trajectory  204  may correspond to the reference trajectory  122  of  FIG. 1 , although the reference trajectory  204  may represent any path or trajectory in the environment  200 . 
     In some examples, the trajectory generation component  116  (or another component of the vehicle computing device(s)  112 ) can determine a density of points associated with various regions of the reference trajectory  204 . By way of example and without limitation, the vehicle computing device(s)  112  can identify region(s) of the reference trajectory  204  corresponding to regions of interest or corresponding to regions of high activity. In some examples, the region(s) can be identified based at least in part on a cost associated with a curvature value associated with the reference trajectory  204  and/or based at least in part on a cost associated with a distance between a point on the reference trajectory  204  and a distance to a point associated with an obstacle in the environment  200 . 
     As illustrated, the reference trajectory  204  includes portions  206 ,  208 ,  210 , and  226 , although the reference trajectory  204  can include any number of portions. 
     The portion  206  can be associated with a point density  212 , the portion  208  can be associated with a point density  214 , the portion  210  can be associated with a point density  216 , and the portion  226  can be associated with a point density  228 . 
     In some examples, the point density  214  can be higher than the point density  212  and/or  216  because a cost associated with a curvature value associated with the portion  208  is higher than a curvature value associated with the portions  206  and/or  210 . In some examples, the point density  228  can be higher than the point density  212  and/or  217  because a cost associated with a distance between the portion  226  and the obstacle  202  is smaller than a distance between the portions  206 ,  210  and the obstacle  202 . That is, the point density  214  associated with the portion  208  can be based at least in part on a cost associated with a curvature value associated with the portion  208  and the point density  228  associated with the portion  226  can be based at least in part on a cost associated with a distance between the portion  226  and the obstacle  202 . 
     In some examples, the point density  214  can correspond to a density of points in space or time. For example, as discussed above, a first layer of a planning system may optimize a trajectory based on a distance (which may be a Euclidian distance, length along an inertial reference, etc.) between points while a second layer of a planning system may optimize the trajectory based on a time period between points, or vice versa. In some examples, the difference  218  between adjacent reference points  220  and  222  can represent a distance in space (e.g., 5 cm, 10 cm, 50 cm, 1 m, and the like) or a distance in time (e.g., 0.05 seconds, 0.1 seconds, 0.2 seconds, 0.5 seconds, 1 second, and the like). In an example where the point density  214  is higher than the point density  212 , the difference  218  between the adjacent points  220  and  222  can be smaller than a difference  224  between adjacent reference points associated with the portion  206 . 
     As can be understood in the context of this disclosure, one or more costs can be evaluated at discrete points on the reference trajectory  204  to generate a target trajectory that the vehicle  102  can follow to traverse through the environment  200 . 
       FIG. 3  is an illustration of determining weight(s) associated with a cost associated with generating a target trajectory based on a reference trajectory when navigating around an obstacle, in accordance with embodiments of the disclosure. More specifically,  FIG. 3  illustrates an example environment  300  in which the vehicle  102  is positioned.  FIG. 3  further illustrates an obstacle  302  in the environment  300 , whereby a reference trajectory  304  represents an initial path for the vehicle  102  to navigate around the obstacle  302  in the environment  200 . In some examples, the reference trajectory  304  can correspond to a maneuver for the vehicle  102  to navigate around a double-parked vehicle in the environment  300 . In some examples, the obstacle  302  may represent any static object or dynamic object in the environment  300 . Further, the reference trajectory  304  may represent any path in the environment  300 . 
     In general, the vehicle computing device(s)  112  can evaluate one or more costs at reference points  306  and  308  to generate a target trajectory  310  having points  312  and  314  corresponding to the reference points  306  and  308 , respectively. A dotted line between points on the reference trajectory  304  and the target trajectory  310  indicates corresponding points. In some cases, each point on the reference trajectory  304  can be associated with a corresponding point on the target trajectory  310 , although in some cases the target trajectory  310  can be associated with points that do not necessarily correspond to point(s) on the reference trajectory. In some cases, a point (e.g., the points  312 ,  314 , and  326 ) on the target trajectory  310  can be orthogonal to a corresponding reference point (e.g., the reference points  306 ,  308 , and  324 , respectively) on the reference trajectory  304 , based at least in part on a curvature value at the reference trajectory  304 . 
     In general, the one or more costs may include, but is not limited to a reference cost, an obstacle cost, a lateral cost, a longitudinal cost, and the like. A cost can be associated with a weight (e.g., weights  316  and  318 ) that can increase or decrease a cost associated with a trajectory or a cost associated with a point of the trajectory. In some examples, the vehicle computing device(s)  112  can perform operations to decrease a weight associated with a cost based on actions or maneuvers to be performed by the vehicle. 
     A start trigger  320  can represent a condition causing weights associated with cost(s) to be varied, as discussed herein. In some examples, a trigger can correspond to obstacle costs meeting or exceeding a threshold (e.g., discussed in context of  FIG. 3 ) and/or jumps or discontinuities in a reference trajectory (e.g., discussed in the context of  FIG. 4 ). For example, during an operation to evaluate initial costs associate with reference points on the reference trajectory  304 , an obstacle cost associated with a reference point  324  may meet or exceed a threshold cost by virtue of the reference point  324  being located within the obstacle  302 . Accordingly, the start trigger can be initiated along the reference trajectory  304  based at least in part on a distance or time in relation to the reference point  324 . An end trigger  322  can represent a period of time or distance associated with the cost(s) (e.g., an obstacle cost) associated with the reference trajectory  304  being below a threshold value. Based at least in part on the start trigger  320  and the end trigger  322 , a weight associated with the points of the reference trajectory  304  and/or the target trajectory  310  can be adjusted to increase or decrease the weights. For example, a weight associated with the reference point  306  can correspond to the weight  316  and a weight associated with the reference point  308  can correspond to the weight  318 . Accordingly, evaluating costs associated with the points  306  and  308  can be based at least in part on the weights  316  and  318 , respectively. By reducing the weight associated with the reference cost when passing the obstacle  302 , the vehicle is better able to explore trajectories which may be further from the reference trajectory  304  to overcome the obstacle  302 . By way of example, a point  326  corresponding to the reference point  324  is allowed to be located further from the reference point  324  to safely overcome the obstacle  302 . 
     In some examples, the weights associated with the reference points  306 ,  308 , and  324  can be used to determine the points  312 ,  314 , and  326  respectively, and accordingly, can determine the contours of the target trajectory  310 . By way of example, and without limitation, a weight associated with a reference cost can be decreased when the vehicle is navigating around the obstacle  302 . In some examples, a reference cost can comprise a cost associated with a difference between a point (e.g., the reference point  308 ) on the reference trajectory  304  and a corresponding point (e.g., the point  314 ) on the target trajectory  310 . In some example, the difference can represent one or more difference in a yaw, lateral offset, velocity, acceleration, curvature, curvature rate, and the like. In some examples, decreasing a weight associated with a reference cost can reduce a penalty associated with the target trajectory  310  being located a distance away from the reference trajectory  304 , which can provide smoother transitions leading towards safer and/or more comfortable vehicle operations. For example, decreasing the reference costs associated with a portion of the reference trajectory  304  associated with the start trigger  320  and the end trigger  322  can lead to increasing a buffer between the vehicle  102  and the obstacle  302  in the environment  300  and/or otherwise allowing the vehicle  102  to explore trajectories not previously available. 
       FIG. 4  is an illustration of determining weight(s) associated with a cost associated with generating a target trajectory based on a reference trajectory when changing lanes, in accordance with embodiments of the disclosure. More specifically,  FIG. 4  illustrates an example environment  400  in which the vehicle  102  is positioned.  FIG. 4  further illustrates an obstacle  402  in the environment  400 , whereby a reference trajectory  404  (comprising portions  404 A and  404 B) represents an initial path or trajectory for the vehicle  102  to change lanes to navigate around the obstacle  402  in the environment  400 . In some examples, the obstacle  402  corresponds to the vehicle  104   a  of  FIG. 1 , although the obstacle  402  may represent any static object or dynamic object in the environment  200 . Further, the reference trajectory  404  may represent an alternative implementation compared to the reference trajectory  122  of  FIG. 1  (e.g., representing a lane change action from the lane  136  to the lane  138 ). However, the reference trajectory  404  may represent any path or trajectory in the environment  400 . 
     In general, the vehicle computing device(s)  112  can evaluate one or more costs at a reference point  406  to generate a target trajectory  408  having a point  410  corresponding to the reference point  306 . A dotted line between the reference point  406  and the point  410  indicates a corresponding point. Although only one point on the reference trajectory  404  is shown in  FIG. 4 , it can be understood that a plurality of points on the reference trajectory  404  can correspond to a plurality of corresponding points on the target trajectory  408 . 
     In general, the one or more costs may include, but is not limited to a reference cost, an obstacle cost, a lateral cost, a longitudinal cost, and the like. A cost can be associated with a weight (e.g., weights  412  and  414 ) that can increase or decrease a cost associated with a trajectory or a cost associated with a point of the trajectory. In some examples, the vehicle computing device(s)  112  can perform operations to decrease a weight associated with a cost based on actions or maneuvers to be performed by the vehicle. 
     A start trigger  416  can represent a jump or discontinuity of the reference trajectory  404 , which can represent a lane change action to change from the lane  136  to the lane  138  in the environment  400 . A curve  418  can represent the varying weight(s) of cost(s) over the reference trajectory  404 B, whereby a shape of the curve  418  can be adjusted based on a lateral distance for the vehicle  102  to traverse with respect to a distance and/or time. Based at least in part on the start trigger  416  and the curve  418 , a weight associated with the points of the reference trajectory  404  and/or the target trajectory  408  can be adjusted to increase or decrease the weight. In some examples, the curve  418  can represent a linear function, quadratic function, stepwise function, and the like, associated with the weight. For example, a weight associated with the reference point  406  can correspond to the weight  414 . Accordingly, evaluating cost(s) associated with the point  406  can be based at least in part on the weight  414 . Of course, evaluating the costs can be performed for a plurality of points associated with a plurality of weights, as discussed herein. 
     In some examples, a lane change action can be associated with other constraints to provide a smooth and comfortable behavior of the vehicle  102 . For example, a detail of the target trajectory  408  is illustrated as a detail  420  of a portion of the target trajectory  422 . In an example  424 , the vehicle computing device(s)  112  can determine whether a lateral displacement  426  of the vehicle  102  is less than a threshold amount given a longitudinal displacement  428 . By way of example and without limitation, the lateral displacement  426  may be 1 meter (or N meters) and the longitudinal displacement  428  may be 5 meters (or M meters). In an example  430 , the vehicle computing device(s)  112  can determine whether a lateral displacement  432  of the vehicle  102  is less than a threshold amount given a time period  434 . By way of example and without limitation, the lateral displacement  432  may be 1 meter (or N′ meters) and the longitudinal displacement  428  may be 2 seconds (or S seconds). In some examples, a shape of the curve  418  can be adjusted (e.g., to vary weights more or less rapidly) to ensure that the lateral displacement  426  and  432  of the vehicle  102  is below a threshold. Thus, in some examples, the vehicle computing device(s)  112  can vary weight(s) associated with cost(s) and can ensure that a lateral displacement of the vehicle  102  is less than a threshold amount given a distance to travel and given a time period to determine the target trajectory  408  for the vehicle  102  to follow as the vehicle  102  traverses the environment  400 . 
       FIG. 5A  is an illustration of a dilated region, a collision region, and a safety region associated with a drivable area, in accordance with embodiments of the disclosure. In particular,  FIG. 5  illustrates an example environment  500  in which the vehicle  102  is positioned in a lane  502  of a road  504 .  FIG. 5  further illustrates a pedestrian  106   a  traversing the environment  500  and the vehicle  104   a  parked on the side of the road  504 . 
     In some examples, the vehicle computing device(s)  112  can determine a drivable area  506  comprising a plurality of regions, as illustrated in a detail  508 . In particular, the drivable area  506  comprises a dilated region  510 , a collision region  512 , and a safety region  514 . For example, the dilated region  510  can represent the largest extent of the drivable area  506  and can comprise information about object(s) (e.g.,  106   a  and  104   a ) in the environment  500  and probabilistic distances between the boundaries (e.g., defined by the regions  510 ,  512 , and  514 ) and the object(s). As a non-limiting example, uncertainties from measurement and/or movement of the object(s) may be effectively “encoded” into such boundaries. Further, and in some examples, the collision region  512  can represent a drivable region smaller than the dilated region  510  representing a region for the vehicle  102  to avoid to further reduce a likelihood that the vehicle  102  will collide with the pedestrian  106   a  and the vehicle  104   a  in the environment  500 . In some examples, the safety region  514  can represent a drivable region smaller than the collision region  512  and the dilated region  510  to provide a buffer between the vehicle  102  and the pedestrian  106   a  and the vehicle  104   a  in the environment  500 . In some examples, the collision region  512  and/or the safety region  514  can also be associated with information about object(s) in the environment and probabilistic distances between the boundaries and the object(s). In some examples, the vehicle  102  can evaluate costs based at least in part on distance(s) between points on a reference trajectory  516  (and/or on a target trajectory) and one or more points associated with the regions  510 ,  512 , and/or  516 , as discussed herein. In some examples, the cost(s) associated with the region(s)  510 ,  512 , and  514  may vary. For example, a cost associated with the safety region  514  may be relatively less than a cost associated with the collision region  512 , and vice versa. 
       FIG. 5B  is an illustration of regions associated with a drivable area based at least in part on a classification of an object in an environment and/or on a velocity of a vehicle in the environment, in accordance with embodiments of the disclosure. An example  518  illustrates the detail  508  of  FIG. 5A  with the dilated region  510 , the collision region  512 , and the safety region  514 . 
     For example, the safety region  514  represents a safety region while the vehicle  102  is associated with a velocity V 1 . 
     By way of another example, the safety region  514 * represents a safety region while the vehicle  102  is associated with a velocity V 2 . As the safety regions  514  and  514 * can be associated with information about object(s) in the environment  500  (e.g., classification information) and probabilistic distances between the boundaries and the object(s), a size of different portions of the safety regions  514  and  514 * can vary based on classification information of the different portions and a velocity of the vehicle  102 . For example, a portion  520  can represent a portion of the safety regions  514  and  514 * associated with the pedestrian  106   a , while the portion  522  can represent a portion of the safety regions  514  and  514 * associated with the vehicle  104   a . Portions  524 ,  526 , and  528  may not be associated with the objects  106   a  or  104   a , or may be associated with the extents of the road  504 . 
     In some examples, a size of the safety regions  514  and  514 * can be based at least in part on classification information of the associated object and/or on a velocity of the vehicle  102  in the environment. For example, a distance  530  associated with the portion  520  of the safety region  514 * can represent an increase in a size of the safety region  514  for the portion associated with the pedestrian  106   a  while the vehicle  102  is at a velocity V 2 . A distance  532  associated with the portion  522  of the safety region  514 * can represent an increase in a size of the safety region  514  for the portion associated with the vehicle  104   a  while the vehicle  102  is at a velocity V 2 . In some examples, the distance  530  can be larger than the distance  532 . In some examples, the velocity V 2  can be higher than the velocity V 1 . In some examples, varying a size of the safety regions  514  and  514 * can further include modifying an extent of the drivable area to generate a modified drivable area. 
     In some examples, the portions  524 ,  526 , and/or  528  of the safety region  514  that are not associated with the pedestrian  106   a  or the vehicle  104   a  may or may not change based on a velocity of the vehicle  102 . 
       FIG. 6  is an illustration of updating a region based on identifying an object such as a bicyclist proximate to the vehicle, in accordance with embodiments of the disclosure. In particular,  FIG. 6  illustrates an example environment  600  in which the vehicle  102  is positioned in a lane  602  of a road  604 .  FIG. 6  further illustrates a pedestrian  106   a  traversing the environment  500 , the vehicle  104   a  parked on the side of the road  604 , and a bicyclist  606  (e.g., in a bicycle lane) proximate to the vehicle  102 . 
     A detail  608  illustrates the vehicle  102 , the bicyclist  606  proximate to the vehicle  102 , and a drivable area  610  comprising a plurality of regions. For simplicity of discussion, a lane boundary  612  and a lane bias region  614  are illustrated in  FIG. 6 , although it may be understood that additional regions may comprise a dilated region, a collision region, a safety region, and the like. In some examples, the vehicle computing device(s)  112  can determine the lane bias region  614  based at least in part on the bicyclist  606  (and/or a bicycle lane, parking lane, parked car, and the like) being proximate to the vehicle  102  in the environment  600 . 
     For example, the vehicle computing device(s)  112  can determine a predicted trajectory for the bicyclist  606  to determine that the bicyclist  606  may pass the vehicle  102 , and accordingly, can expand a size of the lane boundary  612  to generate the lane bias region  614 . Accordingly, for a given reference trajectory  616 , the vehicle computing device(s)  112  can generate a target trajectory  618  that biases a location of the vehicle  102  away from the bicyclist  606  (and/or bicycle lane) to allow the bicyclist to pass the vehicle  102  with more comfortable space. In some examples, a determination of whether to generate the lane bias region  614  can be based at least in part on one or more of a velocity of the bicyclist  606 , a velocity of the bicyclist  606  relative to a velocity of the vehicle  102 , a velocity of the vehicle  102  being below a threshold, a position of the bicyclist  606  in the lane  602  of the road  604 , a position of the vehicle  102  and/or information from a map, and the like. In this manner, the vehicle computing device(s)  112  can evaluate cost(s) based on the lane bias region  614  to generate the target trajectory  620  that provides additional space for the bicyclist  606  to pass the vehicle  102 . 
       FIG. 7  depicts a block diagram of an example system  700  for implementing the techniques described herein. In at least one example, the system  700  can include a vehicle  702 . 
     The vehicle  702  can include a vehicle computing device  704 , one or more sensor systems  706 , one or more emitters  708 , one or more communication connections  710 , at least one direct connection  712 , and one or more drive systems  714 . 
     The vehicle computing device  704  can include one or more processors  716  and memory  718  communicatively coupled with the one or more processors  716 . In the illustrated example, the vehicle  702  is an autonomous vehicle; however, the vehicle  702  could be any other type of vehicle. In the illustrated example, the memory  718  of the vehicle computing device  704  stores a localization component  720 , a perception component  722 , one or more maps  724 , one or more system controllers  726 , and a planning component  728  comprising the drivable area component  114 , the trajectory generation component  116 , the cost(s) component  118 , a safety region component  730 , a trajectory sampling component  732 , and a weight(s) component  734 . Though depicted in  FIG. 7  as residing in memory  718  for illustrative purposes, it is contemplated that the localization component  720 , the perception component  722 , the one or more maps  724 , the one or more system controllers  726 , the planning component  728 , the drivable area component  114 , the trajectory generation component  116 , the cost(s) component  118 , the safety region component  730 , the trajectory sampling component  732 , and the weight(s) component  734  may additionally, or alternatively, be accessible to the vehicle  702  (e.g., stored remotely). 
     In at least one example, the localization component  720  can include functionality to receive data from the sensor system(s)  706  to determine a position and/or orientation of the vehicle  702  (e.g., one or more of an x-, y-, z-position, roll, pitch, or yaw). For example, the localization component  720  can include and/or request/receive a map of an environment and can continuously determine a location and/or orientation of the autonomous vehicle within the map. In some instances, the localization component  720  can utilize SLAM (simultaneous localization and mapping), CLAMS (calibration, localization and mapping, simultaneously), relative SLAM, bundle adjustment, non-linear least squares optimization, or the like to receive image data, LIDAR data, RADAR data, IMU data, GPS data, wheel encoder data, and the like to accurately determine a location of the autonomous vehicle. In some instances, the localization component  720  can provide data to various components of the vehicle  702  to determine an initial position of an autonomous vehicle for generating a trajectory and/or for generating map data, as discussed herein. 
     In some instances, the perception component  722  can include functionality to perform object detection, segmentation, and/or classification. In some examples, the perception component  722  can provide processed sensor data that indicates a presence of an entity that is proximate to the vehicle  702  and/or a classification of the entity as an entity type (e.g., car, pedestrian, cyclist, animal, building, tree, road surface, curb, sidewalk, unknown, etc.). In additional or alternative examples, the perception component  722  can provide processed sensor data that indicates one or more characteristics associated with a detected entity (e.g., a tracked object) and/or the environment in which the entity is positioned. In some examples, characteristics associated with an entity can include, but are not limited to, an x-position (global and/or local position), a y-position (global and/or local position), a z-position (global and/or local position), an orientation (e.g., a roll, pitch, yaw), an entity type (e.g., a classification), a velocity of the entity, an acceleration of the entity, an extent of the entity (size), etc. Characteristics associated with the environment can include, but are not limited to, a presence of another entity in the environment, a state of another entity in the environment, a time of day, a day of a week, a season, a weather condition, an indication of darkness/light, etc. 
     The memory  718  can further include one or more maps  724  that can be used by the vehicle  702  to navigate within the environment. For the purpose of this discussion, a map can be any number of data structures modeled in two dimensions, three dimensions, or N-dimensions that are capable of providing information about an environment, such as, but not limited to, topologies (such as intersections), streets, mountain ranges, roads, terrain, and the environment in general. In some instances, a map can include, but is not limited to: texture information (e.g., color information (e.g., RGB color information, Lab color information, HSV/HSL color information), and the like), intensity information (e.g., LIDAR information, RADAR information, and the like); spatial information (e.g., image data projected onto a mesh, individual “surfels” (e.g., polygons associated with individual color and/or intensity)), reflectivity information (e.g., specularity information, retroreflectivity information, BRDF information, BSSRDF information, and the like). In one example, a map can include a three-dimensional mesh of the environment. In some instances, the map can be stored in a tiled format, such that individual tiles of the map represent a discrete portion of an environment, and can be loaded into working memory as needed, as discussed herein. In at least one example, the one or more maps  724  can include at least one map (e.g., images and/or a mesh). In some examples, the vehicle  702  can be controlled based at least in part on the maps  724 . That is, the maps  724  can be used in connection with the localization component  720 , the perception component  722 , and/or the planning component  728  to determine a location of the vehicle  702 , identify objects in an environment, and/or generate routes and/or trajectories to navigate within an environment. 
     In some examples, the one or more maps  724  can be stored on a remote computing device(s) (such as the computing device(s)  738 ) accessible via network(s)  736 . In some examples, multiple maps  724  can be stored based on, for example, a characteristic (e.g., type of entity, time of day, day of week, season of the year, etc.). Storing multiple maps  724  can have similar memory requirements, but increase the speed at which data in a map can be accessed. 
     In at least one example, the vehicle computing device  704  can include one or more system controllers  726 , which can be configured to control steering, propulsion, braking, safety, emitters, communication, and other systems of the vehicle  702 . These system controller(s)  726  can communicate with and/or control corresponding systems of the drive system(s)  714  and/or other components of the vehicle  702 . 
     In general, the planning component  728  can determine a path for the vehicle  702  to follow to traverse through an environment. For example, the planning component  728  can determine various routes and trajectories and various levels of detail. For example, the planning component  728  can determine a route to travel from a first location (e.g., a current location) to a second location (e.g., a target location). For the purpose of this discussion, a route can be a sequence of waypoints for travelling between two locations. As non-limiting examples, waypoints include streets, intersections, global positioning system (GPS) coordinates, distributed points along a line connecting the first location to the second location constrained by a map and/or map information, etc. Further, the planning component  728  can generate an instruction for guiding the autonomous vehicle along at least a portion of the route from the first location to the second location. In at least one example, the planning component  728  can determine how to guide the autonomous vehicle from a first waypoint in the sequence of waypoints to a second waypoint in the sequence of waypoints. In some examples, the instruction can be a trajectory, or a portion of a trajectory. In some examples, multiple trajectories can be substantially simultaneously generated (e.g., within technical tolerances) in accordance with a receding horizon technique, wherein one of the multiple trajectories is selected for the vehicle  702  to navigate. 
     In some instances, the planning component  728  can include a prediction component to generate predicted trajectories of objects in an environment. For example, a prediction component can generate one or more predicted trajectories for vehicles, pedestrians, animals, and the like within a threshold distance from the vehicle  702 . In some instances, a prediction component can measure a trace of an object and generate a trajectory for the object based on observed and predicted behavior. Examples of generating predicted trajectories are discussed in U.S. patent application Ser. No. 16/151,607, filed Oct. 4, 2018 and Ser. No. 15/982,658, filed May 17, 2018. Application Ser. Nos. 16/151,607 and 15/982,658 are herein incorporated by reference, in their entirety. 
     In some instances, the drivable area component  114  can include functionality to generate and/or determine a drivable area in an environment. For example, the drivable area component  114  can receive sensor data from the sensor system(s)  706  and/or can receive information about obstacles and/or objects in the environment from the perception component  722 . Based at least in part on the sensor data and/or on information associated with the objects (e.g., location, pose, extent, classification, velocity, predicted trajectories, etc.) the drivable area component  114  can determine a drivable area comprising one or more regions, including but not limited to a dilated region, a collision region, and a safety region, as discussed herein. In some instances, the drivable area component  114  can determine an extent of the region(s) based on a classification type associated with objects proximate to the region(s) and/or based on a velocity of the vehicle  702 . Additional details of the drivable area component  114  are discussed throughout this disclosure. 
     In some instances, the trajectory generation component  116  can include functionality to generate a reference trajectory and/or a target trajectory within the drivable area. For example, the trajectory generation component  116  can receive or determine a reference trajectory, which can correspond to a centerline of a road segment or other path through an environment. In some instances, the trajectory generation component  116  can generate segments that can correspond to a motion primitive generated in accordance with the techniques discussed in U.S. patent application Ser. No. 15/843,596, filed Dec. 15, 2017. Further, a segment defined by a desired curvature can be generated in accordance with the techniques discussed in U.S. patent application Ser. No. 15/843,512, filed Dec. 15, 2017. Application Ser. Nos. 15/843,596 and 15/843,512 are herein incorporated by reference, in their entirety. Additional details of the trajectory generation component  116  are discussed throughout this disclosure. 
     In some instances, the cost(s) component  118  can include functionality to evaluate one or more costs to generate a target trajectory with respect to the reference trajectory. As discussed above, the one or more costs may include, but is not limited to, a reference cost, an obstacle cost, a lateral cost, and longitudinal cost, and the like. In some examples, one or more costs can be evaluated in accordance with the techniques discussed in U.S. patent application Ser. No. 16/147,492, filed Sep. 28, 2018, which is hereby incorporated by reference, in its entirety. Additional details of the cost(s) component  118  are discussed throughout this disclosure. 
     In some instances, the safety region component  730  can include functionality to generate an updated safety region based at least in part on a classification of objects in an environment and/or on a velocity of the vehicle  702  traversing the environment. As illustrated in  FIGS. 5A, 5B, and 6 , the safety region component  730  can determine a size of a safety region (or other region), which can be used by the cost(s) component  118  in generating a target trajectory. Additional details of the safety region component  730  are discussed throughout this disclosure. 
     In some instances, the trajectory sampling component  732  can include functionality to determine a point density for portions of the reference trajectory based at least in part on a cost associated with a curvature value associated with a portion of the reference trajectory and/or based at least in part on a cost associated with a distance between a point on the reference trajectory and a point associated with an obstacle in the environment. As illustrated in  FIG. 2 , the trajectory sampling component  732  can determine a density of points for a reference trajectory when a cost associated with a curvature value is relatively high and/or when cost associated with a distance between a point on the reference trajectory and a point associated with an obstacle in the environment is relatively high (e.g., indicating that an object in the environment is located relatively close to a reference trajectory). Increasing the number of points around a region of high activity or a region of interest allows for cost(s) to be evaluated at more points, thereby ensuring a safe and comfortable ride for the vehicle  702 . Additional details of the trajectory sampling component  732  are discussed throughout this disclosure. 
     In some instances, the weight(s) component  734  can include functionality to select or determine weight(s) associated with evaluating one or more cost functions when generating a target trajectory. For example, and as illustrated in  FIGS. 3 and 4 , an action start (such as the start of a double-parked vehicle action or a lane change action) can vary a weight of a cost in order to allow the planning component  728  generate a target trajectory with more flexibility for the vehicle  702  to traverse the environment. Further, weight(s) can be adjusted in combination with other operations (e.g., when expanding a lane boundary based on a proximity of a bicyclist, bike lane, parking lane, parked car, and the like). Additional details of the weight(s) component  734  are discussed throughout this disclosure. 
     In some instances, aspects of some or all of the components discussed herein can include any models, algorithms, and/or machine learning algorithms. For example, in some instances, the components in the memory  718  (and the memory  742 , discussed below) can be implemented as a neural network. 
     As described herein, an exemplary neural network is a biologically inspired algorithm which passes input data through a series of connected layers to produce an output. Each layer in a neural network can also comprise another neural network, or can comprise any number of layers (whether convolutional or not). As can be understood in the context of this disclosure, a neural network can utilize machine learning, which can refer to a broad class of such algorithms in which an output is generated based on learned parameters. 
     Although discussed in the context of neural networks, any type of machine learning can be used consistent with this disclosure. For example, machine learning algorithms can include, but are not limited to, regression algorithms (e.g., ordinary least squares regression (OLSR), linear regression, logistic regression, stepwise regression, multivariate adaptive regression splines (MARS), locally estimated scatterplot smoothing (LOESS)), instance-based algorithms (e.g., ridge regression, least absolute shrinkage and selection operator (LASSO), elastic net, least-angle regression (LARS)), decisions tree algorithms (e.g., classification and regression tree (CART), iterative dichotomiser 3 (ID3), Chi-squared automatic interaction detection (CHAID), decision stump, conditional decision trees), Bayesian algorithms (e.g., naïve Bayes, Gaussian naïve Bayes, multinomial naïve Bayes, average one-dependence estimators (AODE), Bayesian belief network (BNN), Bayesian networks), clustering algorithms (e.g., k-means, k-medians, expectation maximization (EM), hierarchical clustering), association rule learning algorithms (e.g., perceptron, back-propagation, hopfield network, Radial Basis Function Network (RBFN)), deep learning algorithms (e.g., Deep Boltzmann Machine (DBM), Deep Belief Networks (DBN), Convolutional Neural Network (CNN), Stacked Auto-Encoders), Dimensionality Reduction Algorithms (e.g., Principal Component Analysis (PCA), Principal Component Regression (PCR), Partial Least Squares Regression (PLSR), Sammon Mapping, Multidimensional Scaling (MDS), Projection Pursuit, Linear Discriminant Analysis (LDA), Mixture Discriminant Analysis (MDA), Quadratic Discriminant Analysis (QDA), Flexible Discriminant Analysis (FDA)), Ensemble Algorithms (e.g., Boosting, Bootstrapped Aggregation (Bagging), AdaBoost, Stacked Generalization (blending), Gradient Boosting Machines (GBM), Gradient Boosted Regression Trees (GBRT), Random Forest), SVM (support vector machine), supervised learning, unsupervised learning, semi-supervised learning, etc. 
     Additional examples of architectures include neural networks such as ResNet70, ResNet101, VGG, DenseNet, PointNet, and the like. 
     In at least one example, the sensor system(s)  706  can include LIDAR sensors, RADAR sensors, ultrasonic transducers, sonar sensors, location sensors (e.g., GPS, compass, etc.), inertial sensors (e.g., inertial measurement units (IMUs), accelerometers, magnetometers, gyroscopes, etc.), cameras (e.g., RGB, IR, intensity, depth, etc.), time of flight sensors, microphones, wheel encoders, environment sensors (e.g., temperature sensors, humidity sensors, light sensors, pressure sensors, etc.), etc. The sensor system(s)  706  can include multiple instances of each of these or other types of sensors. For instance, the LIDAR sensors can include individual LIDAR sensors located at the corners, front, back, sides, and/or top of the vehicle  702 . As another example, the camera sensors can include multiple cameras disposed at various locations about the exterior and/or interior of the vehicle  702 . The sensor system(s)  706  can provide input to the vehicle computing device  704 . Additionally or alternatively, the sensor system(s)  706  can send sensor data, via the one or more networks  736 , to the one or more computing device(s)  738  at a particular frequency, after a lapse of a predetermined period of time, in near real-time, etc. 
     The vehicle  702  can also include one or more emitters  708  for emitting light and/or sound, as described above. The emitters  708  in this example include interior audio and visual emitters to communicate with passengers of the vehicle  702 . By way of example and not limitation, interior emitters can include speakers, lights, signs, display screens, touch screens, haptic emitters (e.g., vibration and/or force feedback), mechanical actuators (e.g., seatbelt tensioners, seat positioners, headrest positioners, etc.), and the like. The emitters  708  in this example also include exterior emitters. By way of example and not limitation, the exterior emitters in this example include lights to signal a direction of travel or other indicator of vehicle action (e.g., indicator lights, signs, light arrays, etc.), and one or more audio emitters (e.g., speakers, speaker arrays, horns, etc.) to audibly communicate with pedestrians or other nearby vehicles, one or more of which comprising acoustic beam steering technology. 
     The vehicle  702  can also include one or more communication connection(s)  710  that enable communication between the vehicle  702  and one or more other local or remote computing device(s). For instance, the communication connection(s)  710  can facilitate communication with other local computing device(s) on the vehicle  702  and/or the drive system(s)  714 . Also, the communication connection(s)  710  can allow the vehicle to communicate with other nearby computing device(s) (e.g., other nearby vehicles, traffic signals, etc.). The communications connection(s)  710  also enable the vehicle  702  to communicate with a remote teleoperations computing device or other remote services. 
     The communications connection(s)  710  can include physical and/or logical interfaces for connecting the vehicle computing device  704  to another computing device or a network, such as network(s)  736 . For example, the communications connection(s)  710  can enable Wi-Fi-based communication such as via frequencies defined by the IEEE 802.11 standards, short range wireless frequencies such as Bluetooth, cellular communication (e.g., 2G, 3G, 4G, 4G LTE, 5G, etc.) or any suitable wired or wireless communications protocol that enables the respective computing device to interface with the other computing device(s). 
     In at least one example, the vehicle  702  can include one or more drive systems  714 . In some examples, the vehicle  702  can have a single drive system  714 . In at least one example, if the vehicle  702  has multiple drive systems  714 , individual drive systems  714  can be positioned on opposite ends of the vehicle  702  (e.g., the front and the rear, etc.). In at least one example, the drive system(s)  714  can include one or more sensor systems to detect conditions of the drive system(s)  714  and/or the surroundings of the vehicle  702 . By way of example and not limitation, the sensor system(s) can include one or more wheel encoders (e.g., rotary encoders) to sense rotation of the wheels of the drive systems, inertial sensors (e.g., inertial measurement units, accelerometers, gyroscopes, magnetometers, etc.) to measure orientation and acceleration of the drive system, cameras or other image sensors, ultrasonic sensors to acoustically detect objects in the surroundings of the drive system, LIDAR sensors, RADAR sensors, etc. Some sensors, such as the wheel encoders can be unique to the drive system(s)  714 . In some cases, the sensor system(s) on the drive system(s)  714  can overlap or supplement corresponding systems of the vehicle  702  (e.g., sensor system(s)  706 ). 
     The drive system(s)  714  can include many of the vehicle systems, including a high voltage battery, a motor to propel the vehicle, an inverter to convert direct current from the battery into alternating current for use by other vehicle systems, a steering system including a steering motor and steering rack (which can be electric), a braking system including hydraulic or electric actuators, a suspension system including hydraulic and/or pneumatic components, a stability control system for distributing brake forces to mitigate loss of traction and maintain control, an HVAC system, lighting (e.g., lighting such as head/tail lights to illuminate an exterior surrounding of the vehicle), and one or more other systems (e.g., cooling system, safety systems, onboard charging system, other electrical components such as a DC/DC converter, a high voltage j unction, a high voltage cable, charging system, charge port, etc.). Additionally, the drive system(s)  714  can include a drive system controller which can receive and preprocess data from the sensor system(s) and to control operation of the various vehicle systems. In some examples, the drive system controller can include one or more processors and memory communicatively coupled with the one or more processors. The memory can store one or more components to perform various functionalities of the drive system(s)  714 . Furthermore, the drive system(s)  714  also include one or more communication connection(s) that enable communication by the respective drive system with one or more other local or remote computing device(s). 
     In at least one example, the direct connection  712  can provide a physical interface to couple the one or more drive system(s)  714  with the body of the vehicle  702 . For example, the direct connection  712  can allow the transfer of energy, fluids, air, data, etc. between the drive system(s)  714  and the vehicle. In some instances, the direct connection  712  can further releasably secure the drive system(s)  714  to the body of the vehicle  702 . 
     In some examples, the vehicle  702  can send sensor data to one or more computing device(s)  738  via the network(s)  736 . In some examples, the vehicle  702  can send raw sensor data to the computing device(s)  738 . In other examples, the vehicle  702  can send processed sensor data and/or representations of sensor data to the computing device(s)  738 . In some examples, the vehicle  702  can send sensor data to the computing device(s)  738  at a particular frequency, after a lapse of a predetermined period of time, in near real-time, etc. In some cases, the vehicle  702  can send sensor data (raw or processed) to the computing device(s)  738  as one or more log files. 
     The computing device(s)  738  can include processor(s)  740  and a memory  742  storing a planning component  744 . 
     In some instances, the planning component  744  can substantially correspond to the planning component  728  and can include functionality to generate trajectories for the vehicle  702  in an environment. 
     The processor(s)  716  of the vehicle  702  and the processor(s)  740  of the computing device(s)  738  can be any suitable processor capable of executing instructions to process data and perform operations as described herein. By way of example and not limitation, the processor(s)  716  and  740  can comprise one or more Central Processing Units (CPUs), Graphics Processing Units (GPUs), or any other device or portion of a device that processes electronic data to transform that electronic data into other electronic data that can be stored in registers and/or memory. In some examples, integrated circuits (e.g., ASICs, etc.), gate arrays (e.g., FPGAs, etc.), and other hardware devices can also be considered processors in so far as they are configured to implement encoded instructions. 
     Memory  718  and  742  are examples of non-transitory computer-readable media. The memory  718  and  742  can store an operating system and one or more software applications, instructions, programs, and/or data to implement the methods described herein and the functions attributed to the various systems. In various implementations, the memory can be implemented using any suitable memory technology, such as static random access memory (SRAM), synchronous dynamic RAM (SDRAM), nonvolatile/Flash-type memory, or any other type of memory capable of storing information. The architectures, systems, and individual elements described herein can include many other logical, programmatic, and physical components, of which those shown in the accompanying figures are merely examples that are related to the discussion herein. 
     In some instances, the memory  718  and  742  can include at least a working memory and a storage memory. For example, the working memory may be a high-speed memory of limited capacity (e.g., cache memory) that is used for storing data to be operated on by the processor(s)  716  and  740 . In some instances, the memory  718  and  742  can include a storage memory that may be a lower-speed memory of relatively large capacity that is used for long-term storage of data. In some cases, the processor(s)  716  and  740  cannot operate directly on data that is stored in the storage memory, and data may need to be loaded into a working memory for performing operations based on the data, as discussed herein. 
     It should be noted that while  FIG. 7  is illustrated as a distributed system, in alternative examples, components of the vehicle  702  can be associated with the computing device(s)  738  and/or components of the computing device(s)  738  can be associated with the vehicle  702 . That is, the vehicle  702  can perform one or more of the functions associated with the computing device(s)  738 , and vice versa. 
       FIGS. 8 and 9  illustrate example processes in accordance with embodiments of the disclosure. These processes are illustrated as logical flow graphs, each operation of which represents a sequence of operations that can be implemented in hardware, software, or a combination thereof. In the context of software, the operations represent computer-executable instructions stored on one or more computer-readable storage media that, when executed by one or more processors, perform the recited operations. Generally, computer-executable instructions include routines, programs, objects, components, data structures, and the like that perform particular functions or implement particular abstract data types. The order in which the operations are described is not intended to be construed as a limitation, and any number of the described operations can be combined in any order and/or in parallel to implement the processes. 
       FIG. 8  depicts an example process  800  for determining a point density for points associated with a reference trajectory and evaluating costs at the points to generate a target trajectory, in accordance with embodiments of the disclosure. For example, some or all of the process  800  can be performed by one or more components in  FIG. 1 or 7 , as described herein. For example, some or all of the process  800  can be performed by the vehicle computing device(s)  112  and/or the computing device(s)  704 . 
     At operation  802 , the process can include receiving a reference trajectory representing an initial trajectory for an autonomous vehicle to traverse an environment. In some instances, the reference trajectory can comprise a segment of a continuous trajectory or a piecewise trajectory, depending on an action to be performed and a path to be followed in an environment. In some examples, a reference trajectory can be based at least in part on static and/or dynamic obstacles in an environment, a minimum or maximum longitudinal acceleration or velocity, a maximum steering angle, vehicle dynamics (e.g., a notion that the wheels do not slip, etc.). 
     At operation  804 , the process can include determining a first point density of first points associated with a first portion of the reference trajectory. For example, the operation  804  can include determining a cost associated with a curvature value associated with a portion of the reference trajectory and/or a cost associated with a distance to a point associated with an obstacle that is below a threshold value. In some examples, the operation  804  can include accessing a lookup table to determine a point density based on the cost and/or to access a function to determine a point density. For example, a first cost below a first threshold (e.g., 0.1 or x) a first point density can be selected as the point density for a region. Further, where a second cost is above the first threshold (e.g., 0.1 or y) but below a second threshold (e.g., 0.5 or y), a second point density can be based on linearly interpolating between density values. Further, where a third cost is above the second threshold (e.g., 0.5 or y), a third point density can be selected as the point density for a region. Of course, costs can be selected in a variety of manners. 
     Further, in some examples, costs associated with a curvature and/or distance can be performed by a second layer of a planner system, as discussed above. For example, the second layer can receive a first target trajectory from the first layer of the planner system, whereby the first target trajectory can include curvature values. Further, the operation  804  can include evaluating the first target trajectory over time with respect to objects in an environment to determine an initial cost associated with a distance between a point on the first target trajectory (e.g., a reference trajectory) and a point associated with an obstacle in the environment and can subsequently determine the point density based at least in part on the cost. 
     At operation  806 , the process can include determining a second point density of second points associated with a second portion of the reference trajectory. For example, the operation  806  can include determining a region associated with a low curvature value (e.g., a curvature value below a threshold) and/or associated with a distance to a point associated with an obstacle that meets or exceeds a threshold value. 
     At operation  808 , the process can include evaluating cost function(s) at a first point of the first points and at a second point of the second points to generate a target trajectory. For example, the operation  808  can include evaluating one or more of a reference cost, an obstacle cost, a lateral cost, a longitudinal cost, and the like. 
     At operation  810 , the process can include determining whether cost(s) are below a threshold. For example, the operation  810  can include using technique(s) involving a gradient descent to optimize the cost function(s). In some examples, the operation  810  can include determining whether a cost is below a threshold value, determining whether a change in cost between optimization operations is below a threshold value, and/or determining whether a number of optimization iterations has been performed. If the costs meet or exceed a threshold (e.g., “no” in the operation  810 ) the process returns to the operation  808  where another trajectory can be generated (e.g., by varying locations of the points and/or controls (e.g., steering angles, acceleration, etc.)) and updated costs can be determined. If the costs are below a threshold (e.g., “yes” in the operation  810 ), the process can continue to operation  812 . 
     At operation  812 , the process can include controlling the autonomous vehicle to traverse the environment based at least in part on the target trajectory. In some instances, the target trajectory can be provided to a trajectory smoother component and/or the trajectory tracker component to refine the target trajectory and/or to generate control signals for the various motors and steering actuators of the autonomous vehicle. 
       FIG. 9  depicts an example process  900  for determining weights associated with points along a reference trajectory (e.g., those points determined in accordance with  FIG. 8 ) and evaluating, based on the weights, costs at the points to determine a target trajectory, in accordance with embodiments of the disclosure. For example, some or all of the process  900  can be performed by one or more components in  FIG. 1 or 7 , as described herein. For example, some or all of the process  900  can be performed by the vehicle computing device(s)  112  and/or the computing device(s)  704 . 
     At operation  902 , the process can include receiving a reference trajectory representing an initial path for an autonomous vehicle to traverse an environment. In some examples, this operation  902  can substantially correspond to the operation  802 . 
     At operation  904 , the process can include determining portion(s) of the reference trajectory. For example, the operation  904  can include determining a first portion  906 , a second portion  908 , and an N-th portion  910 . In some examples, the portions  906 ,  908 , and  910  can be discrete portions of (and/or points along) the reference trajectory. In some examples, the operation  904  can include determining the portions based at least in part on actions or maneuvers to be performed by the autonomous vehicle. For example, an action or maneuver may include, but is not limited to, a double-parked vehicle action or a lane change action. In some examples, the operation  904  can include determining that the autonomous vehicle is proximate to a bicyclist or another object in the environment and determining a portion of the reference trajectory based at least in part on a predicted trajectory of the bicyclist. In some examples, the operation  904  can include determining a location associated with the vehicle based on map data that may indicate weight(s) or a trigger to vary weights, as discussed herein. 
     At operation  912 , the process can include determining weight(s) associated with reference cost(s) to be evaluated at point(s) associated with the portion(s) of the reference trajectory. In some cases, a reference cost is associated with a difference between a point on the reference trajectory and a corresponding point. For example, the operation  912  can include determining a first weight  914 , a second weight  916 , and an M-th weight  918 . In some examples, the operation  912  can include determining a weight that corresponds to individual portions as determined in the operation  904 . In some examples, the operation  912  can include determining a weight that corresponds to individual points (e.g., reference points) associated with individual portions. That is, if the portion  906  comprises 10 points, the operation  912  can include determining a unique weight for cost(s) associated with each of the 10 points. Of course, other configurations are contemplated herein. 
     At operation  920 , the process can include evaluating a reference cost function at the point(s) to generate a target trajectory comprising the corresponding point. In some examples, the operation  920  can include scaling a cost by a weight as determined in the operation  912 . For example, the operation  920  can include scaling a cost up or down based on the weight associated with the cost, which, in turn, can lead to changes in the contour of the target trajectory based on the weights associated with the various costs, as discussed herein. In some instances, the operation  920  can include evaluating other cost functions, and is not limited to evaluating a reference costs function. Thus, the operations provide a flexible framework to generate trajectories 
     At operation  922 , the process can include controlling the autonomous vehicle to traverse the environment based at least in part on the target trajectory. In some examples, this operation  922  can substantially correspond to the operation  812 . 
     Example Clauses 
     A: A system comprising: one or more processors; and one or more computer-readable media storing instructions executable by the one or more processors, wherein the instructions, when executed, cause the system to perform operations comprising: receiving a reference trajectory representing an initial trajectory for an autonomous vehicle to traverse an environment; determining a first point density of first points associated with a first portion of the reference trajectory; determining a second point density of second points associated with a second portion of the reference trajectory, wherein the second point density is less than the first point density; evaluating a cost function at a first point of the first points and at a second point of the second points to generate a target trajectory comprising first corresponding points and second corresponding points, wherein a first corresponding point of the first corresponding points corresponds to the first point of the first points, and wherein a second corresponding point of the second corresponding points corresponds to a second point of the second points; and controlling the autonomous vehicle to traverse the environment based at least in part on the target trajectory. 
     B: The system of paragraph A, wherein the first point density is based, at least in part, on one or more of: a time period between adjacent points of the first points; or a distance between the adjacent points of the first points. 
     C: The system of paragraph A or B, wherein the cost function comprises a cost based at least in part on a distance between the first corresponding point of the target trajectory and a point associated with an obstacle in the environment. 
     D: The system of any of paragraphs A-C, wherein the first point density is based at least in part on determining that at least one of: a first cost associated with a curvature value associated with the first point of the reference trajectory; or a second cost associated with a distance between the first point and a point associated with an obstacle in the environment. 
     E: The system of paragraph D, wherein determining the first point density comprises accessing a lookup table or a function to determine the first point density based at least in part on the first cost or the second cost. 
     F: The system of any of paragraphs A-E, wherein a first time period associated with the first point and an adjacent first point of the first points is less than a second time period associated with the second point and an adjacent second point of the second points. 
     G: A method comprising: receiving a first trajectory representing an initial trajectory for a vehicle to traverse an environment; determining a point density for points associated with a portion of the first trajectory based at least in part on a first cost associated with a curvature value of the portion or a second cost associated with a distance between a first point of the portion and a second point associated with an obstacle in the environment; determining, based at least on a cost associated with the first point, a second trajectory; and controlling the vehicle to traverse the environment based at least in part on the second trajectory. 
     H: The method of paragraph G, wherein at least one of: the point density increases as the first cost associated with the curvature value increases; or the point density increases as the second cost associated with the distance between a first point and the second point increases. 
     I: The method of paragraph G or H, wherein: the point density is a first point density; the portion is a first portion; the points are first points; the curvature value is a first curvature value; the distance is a first distance; the obstacle is a first obstacle; and the method further comprises: determining a second point density for second points associated with a second portion the first trajectory based at least in part on a third cost associated with a second curvature value of the second portion or a fourth cost associated with a second distance between a third point of the second portion and a fourth point associated with a second obstacle in the environment, wherein the first point density is different than the second point density. 
     J: The method of any of paragraphs G-I, wherein the distance is a first distance, and wherein the point density is based at least in part on one of: a second distance between the first point on the portion and an adjacent first point on the portion; or a time period associated with a difference in a first time associated with the first point and a second time associated with the adjacent first point. 
     K: The method of any of paragraphs G-J, wherein the cost is based at least in part on the distance between the first point of the portion and the second point associated with the obstacle in the environment. 
     L: The method of any of paragraphs G-K, wherein determining the second trajectory further comprises: evaluating the cost for a plurality of points associated with the first trajectory to generate the second trajectory. 
     M: The method of any of paragraphs G-L, further comprising: receiving the first trajectory from a first layer of a planning system of the vehicle, wherein the first trajectory is generated with respect to a distance between points on the first trajectory and the second trajectory is generated, by a second layer of the planning system, with respect to a time period between points on the second trajectory. 
     N: The method of paragraph M, wherein the point density is non-uniform with respect to the portion of the first trajectory. 
     O: A non-transitory computer-readable medium storing instructions that, when executed, cause one or more processors to perform operations comprising: receiving a first trajectory representing an initial trajectory for a vehicle to traverse an environment; determining a point density for points associated with a portion of the first trajectory based at least in part on a first cost associated with a curvature value of the portion or a second cost associated with a distance between a first point of the portion and a second point associated with an obstacle in the environment; generating, based at least in part on a cost associated with the first point, a second trajectory, the second trajectory comprising a target point; and controlling the vehicle to traverse the environment based at least in part on the second trajectory. 
     P: The non-transitory computer-readable medium of paragraph O, wherein at least one of: the point density increases as the first cost associated with the curvature value increases; or the point density increases as the second cost associated with the distance between a first point and the second point increases. 
     Q: The non-transitory computer-readable medium of paragraph O or P, wherein: the point density is a first point density; the portion is a first portion; the points are first points; the curvature value is a first curvature value; the distance is a first distance; the obstacle is a first obstacle; and the operations further comprise: determining a second point density for second points associated with a second portion the first trajectory based at least in part on a third cost associated with a second curvature value of the second portion or a fourth cost associated with a second distance between a third point of the second portion and a fourth point associated with a second obstacle in the environment, wherein the first point density is different than the second point density. 
     R: The non-transitory computer-readable medium of any of paragraphs O-Q, wherein the distance is a first distance, and wherein the cost is based at least in part on a second distance between the target point of the second trajectory and the second point associated with the obstacle in the environment. 
     S: The non-transitory computer-readable medium of any of paragraphs O-R, wherein a first time period associated with the first point and an adjacent first point of the first points is less than a second time period associated with the second point and an adjacent second point of the second points. 
     T: The non-transitory computer-readable medium of paragraph S, the operations further comprising: receiving the first trajectory from a first layer of a planning system of the vehicle, wherein the first trajectory is generated with respect to a Euclidian distance between points on the first trajectory and the second trajectory is generated, by a second layer of the planning system, with respect to a time period between points on the second trajectory. 
     U: A system comprising: one or more processors; and one or more computer-readable media storing instructions executable by the one or more processors, wherein the instructions, when executed, cause the system to perform operations comprising: receiving a reference trajectory representing an initial path for an autonomous vehicle to traverse an environment; determining a first portion of the reference trajectory; determining a second portion of the reference trajectory that is separate from the first portion; determining a first weight associated with a first reference cost to be evaluated at a first point associated with the first portion of the reference trajectory, wherein the first reference cost is associated with a first difference between the first point on the reference trajectory and a corresponding first point; determining a second weight associated with a second reference cost to be evaluated at a second point associated with the second portion of the reference trajectory, wherein the second reference cost is associated with a second difference between the second point on the reference trajectory and a corresponding second point, wherein the second weight is larger than the first weight; evaluating a reference cost function at the first point and the second point to generate a target trajectory comprising the corresponding first point and the corresponding second point; and controlling the autonomous vehicle to traverse the environment based at least in part on the target trajectory. 
     V: The system of paragraph U, wherein the first portion of the reference trajectory is associated with at least one of a discontinuity in the reference trajectory or an obstacle cost that meets or exceeds a threshold obstacle cost. 
     W. The system of paragraph U or V, wherein the first difference comprises one or more of a difference in a yaw, a lateral offset, a velocity, an acceleration, a curvature, or a curvature rate. 
     X: The system of any of paragraphs U-W, wherein the first weight is based at least in part on one or more of: determining that a lateral displacement associated with a portion of the target trajectory corresponding to the first portion of the reference trajectory is below a first threshold distance for a given a time period; or determining that the lateral displacement associated with the portion of the target trajectory corresponding to the first portion of the reference trajectory is below a second threshold distance for a given distance. 
     Y: The system of any of paragraphs U-X, wherein the first reference cost comprises a penalty associated with generating the target trajectory that deviates, at the first point and represented as the first difference, from the reference trajectory. 
     Z: A method comprising: receiving a first trajectory for an autonomous vehicle to traverse an environment; determining a first portion of the first trajectory; determining a second portion of the first trajectory that is separate from the first portion; determining a first weight associated with the first portion of the first trajectory; determining a second weight associated with second portion of the first trajectory, wherein the first weight is different than the second weight; evaluating, with respect to the first trajectory and based at least in part on the first weight and the second weight, a reference cost to generate a second trajectory; and controlling the autonomous vehicle to traverse the environment based at least in part on the second trajectory. 
     AA: The method of paragraph Z, wherein evaluating the reference cost comprises: evaluating a first reference cost associated with the first portion, the first reference cost proportional to the first weight; and evaluating a second reference cost associated with the second portion, the second reference cost proportional to the second weight. 
     AB: The method of paragraph Z or AA, wherein the first portion of the first trajectory is associated with points, and wherein the point is a first point, the method further comprising: determining a point density for the points associated with the first portion of the first trajectory based at least in part on a first cost associated with a curvature value of the first portion or a second cost associated with a distance between the first point of the first portion and a second point associated with an obstacle in the environment. 
     AC: The method of any of paragraphs Z-AB, wherein the reference cost is associated with a difference between a first point on the first trajectory and a corresponding first point on the second trajectory. 
     AD: The method of paragraph AC, wherein the difference comprises one or more of a difference in a yaw, a lateral offset, a velocity, an acceleration, a curvature, or a curvature rate. 
     AE: The method of any of paragraphs Z-AD, wherein the first portion of the first trajectory is associated with at least one of a discontinuity in the first trajectory or an obstacle cost that meets or exceeds a threshold obstacle cost. 
     AF. The method of paragraph AE, wherein: the discontinuity in the first trajectory represents a lane change action; or the obstacle cost meeting or exceeding the threshold obstacle cost represents a least a portion of the first trajectory passing within an area representing an obstacle in the environment. 
     AG: The method of any of paragraphs Z-AF, further comprising: determining a drivable area through the environment for the autonomous vehicle, wherein the drivable area represents a region in the environment on which the autonomous vehicle is configured to traverse and an object in the environment; and evaluating an obstacle cost based at least in part on the drivable area; wherein the second trajectory is based at least in part on the obstacle cost. 
     AH: The method of paragraph AG, wherein determining the drivable area is based at least in part on a velocity associated with the autonomous vehicle or a classification of the object. 
     AI: A non-transitory computer-readable medium storing instructions that, when executed, cause one or more processors to perform operations comprising: receiving a first trajectory for an autonomous vehicle to traverse an environment; determining a first portion of the first trajectory; determining a second portion of the first trajectory that is separate from the first portion; determining a first weight associated with the first portion of the first trajectory; determining a second weight associated with second portion of the first trajectory, wherein the first weight is different than the second weight; evaluating, with respect to the first trajectory and based at least in part on the first weight and the second weight, a reference cost to generate a second trajectory; and controlling the autonomous vehicle to traverse the environment based at least in part on the second trajectory. 
     AJ: The non-transitory computer-readable medium of paragraph AI, wherein: evaluating the reference cost comprises: evaluating a first reference cost associated with the first portion, the first reference cost proportional to the first weight; and evaluating a second reference cost associated with the second portion, the second reference cost proportional to the second weight. 
     AK: The non-transitory computer-readable medium of paragraph AI or AJ, wherein the reference cost is associated with a difference between a first point on the first trajectory and a corresponding first point on the second trajectory. 
     AL: The non-transitory computer-readable medium of any of paragraphs AI-AK, wherein the first portion of the first trajectory is associated with at least one of a discontinuity in the first trajectory or an obstacle cost that meets or exceeds a threshold obstacle cost. 
     AM: The non-transitory computer-readable medium of any of paragraphs AI-AL, wherein the operations further comprise: determining a drivable area through the environment for the autonomous vehicle, wherein the drivable area represents a region in the environment on which the autonomous vehicle is configured to traverse and an object in the environment; and evaluating an obstacle cost based at least in part on the drivable area; wherein the second trajectory is based at least in part on the obstacle cost. 
     AN: The non-transitory computer-readable medium of paragraph AM, wherein determining the drivable area is based at least in part on a velocity associated with the autonomous vehicle or a classification of the object. 
     While the example clauses described above are described with respect to one particular implementation, it should be understood that, in the context of this document, the content of the example clauses can also be implemented via a method, device, system, a computer-readable medium, and/or another implementation. 
     CONCLUSION 
     While one or more examples of the techniques described herein have been described, various alterations, additions, permutations and equivalents thereof are included within the scope of the techniques described herein. 
     In the description of examples, reference is made to the accompanying drawings that form a part hereof, which show by way of illustration specific examples of the claimed subject matter. It is to be understood that other examples can be used and that changes or alterations, such as structural changes, can be made. Such examples, changes or alterations are not necessarily departures from the scope with respect to the intended claimed subject matter. While the steps herein can be presented in a certain order, in some cases the ordering can be changed so that certain inputs are provided at different times or in a different order without changing the function of the systems and methods described. The disclosed procedures could also be executed in different orders. Additionally, various computations that are herein need not be performed in the order disclosed, and other examples using alternative orderings of the computations could be readily implemented. In addition to being reordered, the computations could also be decomposed into sub-computations with the same results.