Patent Publication Number: US-2022214181-A1

Title: Systems and methods for translating navigational route into behavioral decision making in autonomous vehicles

Description:
INTRODUCTION 
     The present disclosure generally relates to autonomous vehicles, and more particularly relates to systems and methods for path planning in an autonomous vehicle. 
     An autonomous vehicle is a vehicle that is capable of sensing its environment and navigating with little or no user input. It does so by using sensing devices such as radar, lidar, image sensors, and the like. Autonomous vehicles further use information from global positioning systems (GPS) technology, navigation systems, vehicle-to-vehicle communication, vehicle-to-infrastructure technology, and/or drive-by-wire systems to navigate the vehicle. 
     While recent years have seen significant advancements in autonomous vehicles, such vehicles might still be improved in a number of respects. For example, it is often difficult for an autonomous vehicle to quickly determine a suitable path (along with target accelerations and velocities) to maneuver through a region of interest while avoiding obstacles whose paths might intersect with the region of interest within some predetermined planning horizon. Road-level navigation plans generated by standard infotainment systems do not provide enough detailed information for autonomous driving to path plan. Compressing long-term road-level plans into efficient and actionable conditions for short-term behavioral planning can be computationally intensive and time consuming. 
     Accordingly, it is desirable to provide improved methods and systems for behavioral planning for an autonomous vehicle. Furthermore, other desirable features and characteristics of the present invention will become apparent from the subsequent detailed description and the appended claims, taken in conjunction with the accompanying drawings and the foregoing technical field and background. 
     SUMMARY 
     Methods and systems for behavior planning for an autonomous vehicle are provided. The planning system is effective in translating the navigation route into behavioral decision-making plans for autonomous vehicles. In one embodiment, a method includes: receiving navigation data including a navigation route; converting the navigation route to road segment data including a plurality of road segments; assigning lane attributes to the plurality road segments of the road segment data; computing cost data for each of the road segments; evaluating the cost data of each of the road segments to determine at least one driving behavior; and generating a display signal for displaying the driving behavior to a user of the autonomous vehicle. 
     In various embodiments, the cost data includes a lane occupancy cost. In various embodiments, the computing the lane occupancy cost is based on lane attributes from perception data, lane properties from map data, and lane segments in the current road segment. 
     In various embodiments, the cost data includes a lane end cost. In various embodiments, the computing the lane end cost is based on lane properties from map data, and downstream lane segments. In various embodiments, the computing the lane end cost comprises backpropagating the lane end cost from downstream road segments. 
     In various embodiments, the cost data includes a lane occupancy cost and a lane end cost. In various embodiments, the lane occupancy cost is computed as a binary value. In various embodiments, the lane occupancy cost is zero when the lane is drivable. In various embodiments, the lane end cost is zero when any downstream lanes have a lane occupancy cost of zero. 
     In another embodiment, a computer implemented system includes: a planner module that comprises one or more processors configured by programming instructions encoded in non-transitory computer readable media. The planner module is configured to: receive navigation data including a navigation route; convert the navigation route to road segment data including a plurality of road segments; assign lane attributes to the plurality road segments of the road segment data; compute cost data for each of the road segments; evaluate the cost data of each of the road segments to determine at least one driving behavior; and generate a display signal for displaying the driving behavior to a user of the autonomous vehicle. 
     In various embodiments, the cost data includes a lane occupancy cost. In various embodiments, wherein the planner module computes the lane occupancy cost based on lane attributes from perception data, lane properties from map data, and lane segments in the current road segment. 
     In various embodiments, the cost data includes a lane occupancy cost. In various embodiments, the planner module computes the lane occupancy cost based on lane properties from map data, and downstream lane segments. In various embodiments, the planner module computes the lane occupancy cost by backpropagating the lane end cost from downstream road segments. 
     In various embodiments, the cost data includes a lane occupancy cost and a lane end cost. In various embodiments, the lane occupancy cost is computed as a binary value. In various embodiments, wherein the lane occupancy cost is set to zero when the lane is drivable. In various embodiments, the lane end cost is set to zero when any downstream lanes have a lane occupancy cost of zero. 
    
    
     
       DESCRIPTION OF THE DRAWINGS 
       The exemplary embodiments will hereinafter be described in conjunction with the following drawing figures, wherein like numerals denote like elements, and wherein: 
         FIG. 1  is a functional block diagram illustrating an autonomous vehicle including a path planning system, in accordance with various embodiments; 
         FIG. 2  is a functional block diagram illustrating a transportation system having one or more autonomous vehicles as shown in  FIG. 1 , in accordance with various embodiments; 
         FIG. 3  is functional block diagram illustrating an autonomous driving system (ADS) associated with an autonomous vehicle, in accordance with various embodiments; 
         FIG. 4  is a dataflow diagram illustrating a path planning system of an autonomous vehicle, in accordance with various embodiments; 
         FIG. 5  is an illustration of a route, and road segments of the path planning system in accordance with various embodiments; and 
         FIGS. 6 and 7  are flowcharts illustrating control methods for controlling the autonomous vehicle, in accordance with various embodiments. 
     
    
    
     DETAILED DESCRIPTION 
     The following detailed description is merely exemplary in nature and is not intended to limit the application and uses. Furthermore, there is no intention to be bound by any expressed or implied theory presented in the preceding technical field, background, brief summary, or the following detailed description. As used herein, the term “module” refers to any hardware, software, firmware, electronic control component, processing logic, and/or processor device, individually or in any combination, including without limitation: application specific integrated circuit (ASIC), a field-programmable gate-array (FPGA), an electronic circuit, a processor (shared, dedicated, or group) and memory that executes one or more software or firmware programs, a combinational logic circuit, and/or other suitable components that provide the described functionality. 
     Embodiments of the present disclosure may be described herein in terms of functional and/or logical block components and various processing steps. It should be appreciated that such block components may be realized by any number of hardware, software, and/or firmware components configured to perform the specified functions. For example, an embodiment of the present disclosure may employ various integrated circuit components, e.g., memory elements, digital signal processing elements, logic elements, look-up tables, or the like, which may carry out a variety of functions under the control of one or more microprocessors or other control devices. In addition, those skilled in the art will appreciate that embodiments of the present disclosure may be practiced in conjunction with any number of systems, and that the systems described herein is merely exemplary embodiments of the present disclosure. 
     For the sake of brevity, conventional techniques related to signal processing, data transmission, signaling, control, machine learning models, radar, lidar, image analysis, and other functional aspects of the systems (and the individual operating components of the systems) may not be described in detail herein. Furthermore, the connecting lines shown in the various figures contained herein are intended to represent example functional relationships and/or physical couplings between the various elements. It should be noted that many alternative or additional functional relationships or physical connections may be present in an embodiment of the present disclosure. 
     With reference to  FIG. 1 , a path planning system shown generally as  100  is associated with a vehicle (or “AV”)  10  in accordance with various embodiments. In general, path planning system (or simply “system”)  100  allows for selecting a path for AV  10  by using a cost-based route planner which provides lane-level information. The resulting route plans are efficient and actionable plans that enable short-term path planning. In various embodiments, the route plans extract the relevant long-term map/navigation information in a way that eases the optimization of decisions based on dynamic information, as will be discussed in more detail below. 
     As depicted in  FIG. 1 , the vehicle  10  generally includes a chassis  12 , a body  14 , front wheels  16 , and rear wheels  18 . The body  14  is arranged on the chassis  12  and substantially encloses components of the vehicle  10 . The body  14  and the chassis  12  may jointly form a frame. The wheels  16 - 18  are each rotationally coupled to the chassis  12  near a respective corner of the body  14 . 
     In various embodiments, the vehicle  10  is an autonomous vehicle and the path planning system  100  is incorporated into the autonomous vehicle  10  (hereinafter referred to as the autonomous vehicle  10 ). The autonomous vehicle  10  is, for example, a vehicle that is automatically controlled to carry passengers from one location to another. The vehicle  10  is depicted in the illustrated embodiment as a passenger car, but it should be appreciated that any other vehicle, including motorcycles, trucks, sport utility vehicles (SUVs), recreational vehicles (RVs), marine vessels, aircraft, etc., can also be used. 
     In an exemplary embodiment, the autonomous vehicle  10  corresponds to a level four or level five automation system under the Society of Automotive Engineers (SAE) “J3016” standard taxonomy of automated driving levels. Using this terminology, a level four system indicates “high automation,” referring to a driving mode in which the automated driving system performs all aspects of the dynamic driving task, even if a human driver does not respond appropriately to a request to intervene. A level five system, on the other hand, indicates “full automation,” referring to a driving mode in which the automated driving system performs all aspects of the dynamic driving task under all roadway and environmental conditions that can be managed by a human driver. It will be appreciated, however, the embodiments in accordance with the present subject matter are not limited to any particular taxonomy or rubric of automation categories. Furthermore, systems in accordance with the present embodiment may be used in conjunction with any vehicle in which the present subject matter may be implemented, regardless of its level of autonomy. 
     As shown, the autonomous vehicle  10  generally includes a propulsion system  20 , a transmission system  22 , a steering system  24 , a brake system  26 , a sensor system  28 , an actuator system  30 , at least one data storage device  32 , at least one controller  34 , and a communication system  36 . The propulsion system  20  may, in various embodiments, include an internal combustion engine, an electric machine such as a traction motor, and/or a fuel cell propulsion system. The transmission system  22  is configured to transmit power from the propulsion system  20  to the vehicle wheels  16  and  18  according to selectable speed ratios. According to various embodiments, the transmission system  22  may include a step-ratio automatic transmission, a continuously-variable transmission, or other appropriate transmission. 
     The brake system  26  is configured to provide braking torque to the vehicle wheels  16  and  18 . Brake system  26  may, in various embodiments, include friction brakes, brake by wire, a regenerative braking system such as an electric machine, and/or other appropriate braking systems. 
     The steering system  24  influences a position of the vehicle wheels  16  and/or  18 . While depicted as including a steering wheel  25  for illustrative purposes, in some embodiments contemplated within the scope of the present disclosure, the steering system  24  may not include a steering wheel. 
     The sensor system  28  includes one or more sensing devices  40   a - 40   n  that sense observable conditions of the exterior environment and/or the interior environment of the autonomous vehicle  10  (such as the state of one or more occupants). Sensing devices  40   a - 40   n  might include, but are not limited to, radars (e.g., long-range, medium-range-short range), lidars, global positioning systems, optical cameras (e.g., forward facing, 360-degree, rear-facing, side-facing, stereo, etc.), thermal (e.g., infrared) cameras, ultrasonic sensors, odometry sensors (e.g., encoders) and/or other sensors that might be utilized in connection with systems and methods in accordance with the present subject matter. 
     The actuator system  30  includes one or more actuator devices  42   a - 42   n  that control one or more vehicle features such as, but not limited to, the propulsion system  20 , the transmission system  22 , the steering system  24 , and the brake system  26 . In various embodiments, autonomous vehicle  10  may also include interior and/or exterior vehicle features not illustrated in  FIG. 1 , such as various doors, a trunk, and cabin features such as air, music, lighting, touch-screen display components (such as those used in connection with navigation systems), and the like. 
     The data storage device  32  stores data for use in automatically controlling the autonomous vehicle  10 . In various embodiments, the data storage device  32  stores defined maps of the navigable environment. In various embodiments, the defined maps may be predefined by and obtained from a remote system (described in further detail with regard to  FIG. 2 ). For example, the defined maps may be assembled by the remote system and communicated to the autonomous vehicle  10  (wirelessly and/or in a wired manner) and stored in the data storage device  32 . Route information may also be stored within data storage device  32 —i.e., a set of road segments (associated geographically with one or more of the defined maps) that together define a route that the user may take to travel from a start location (e.g., the user&#39;s current location) to a target location. As will be appreciated, the data storage device  32  may be part of the controller  34 , separate from the controller  34 , or part of the controller  34  and part of a separate system. 
     The controller  34  includes at least one processor  44  and a computer-readable storage device or media  46 . The processor  44  may be any custom-made or commercially available processor, a central processing unit (CPU), a graphics processing unit (GPU), an application specific integrated circuit (ASIC) (e.g., a custom ASIC implementing a neural network), a field programmable gate array (FPGA), an auxiliary processor among several processors associated with the controller  34 , a semiconductor-based microprocessor (in the form of a microchip or chip set), any combination thereof, or generally any device for executing instructions. The computer readable storage device or media  46  may include volatile and nonvolatile storage in read-only memory (ROM), random-access memory (RAM), and keep-alive memory (KAM), for example. KAM is a persistent or non-volatile memory that may be used to store various operating variables while the processor  44  is powered down. The computer-readable storage device or media  46  may be implemented using any of a number of known memory devices such as PROMs (programmable read-only memory), EPROMs (electrically PROM), EEPROMs (electrically erasable PROM), flash memory, or any other electric, magnetic, optical, or combination memory devices capable of storing data, some of which represent executable instructions, used by the controller  34  in controlling the autonomous vehicle  10 . In various embodiments, controller  34  is configured to implement the path planning systems and methods discussed in more detail below. 
     The instructions may include one or more separate programs, each of which comprises an ordered listing of executable instructions for implementing logical functions. The instructions, when executed by the processor  44 , receive and process signals from the sensor system  28 , perform logic, calculations, methods and/or algorithms for automatically controlling the components of the autonomous vehicle  10 , and generate control signals that are transmitted to the actuator system  30  to automatically control the components of the autonomous vehicle  10  based on the logic, calculations, methods, and/or algorithms. Although only one controller  34  is shown in  FIG. 1 , embodiments of the autonomous vehicle  10  may include any number of controllers  34  that communicate over any suitable communication medium or a combination of communication mediums and that cooperate to process the sensor signals, perform logic, calculations, methods, and/or algorithms, and generate control signals to automatically control features of the autonomous vehicle  10 . 
     The communication system  36  is configured to wirelessly communicate information to and from other entities  48 , such as but not limited to, other vehicles (“V2V” communication), infrastructure (“V2I” communication), networks (“V2N” communication), pedestrian (“V2P” communication), remote transportation systems, and/or user devices (described in more detail with regard to  FIG. 2 ). In an exemplary embodiment, the communication system  36  is a wireless communication system configured to communicate via a wireless local area network (WLAN) using IEEE 802.11 standards or by using cellular data communication. However, additional or alternate communication methods, such as a dedicated short-range communications (DSRC) channel, are also considered within the scope of the present disclosure. DSRC channels refer to one-way or two-way short-range to medium-range wireless communication channels specifically designed for automotive use and a corresponding set of protocols and standards. 
     With reference now to  FIG. 2 , in various embodiments, the autonomous vehicle  10  described with regard to  FIG. 1  may be suitable for use in the context of a taxi or shuttle system in a certain geographical area (e.g., a city, a school or business campus, a shopping center, an amusement park, an event center, or the like) or may simply be managed by a remote system. For example, the autonomous vehicle  10  may be associated with an autonomous-vehicle-based remote transportation system.  FIG. 2  illustrates an exemplary embodiment of an operating environment shown generally at  50  that includes an autonomous-vehicle-based remote transportation system (or simply “remote transportation system”)  52  that is associated with one or more autonomous vehicles  10   a - 10   n  as described with regard to  FIG. 1 . In various embodiments, the operating environment  50  (all or a part of which may correspond to entities  48  shown in  FIG. 1 ) further includes one or more user devices  54  that communicate with the autonomous vehicle  10  and/or the remote transportation system  52  via a communication network  56 . 
     The communication network  56  supports communication as needed between devices, systems, and components supported by the operating environment  50  (e.g., via tangible communication links and/or wireless communication links). For example, the communication network  56  may include a wireless carrier system  60  such as a cellular telephone system that includes a plurality of cell towers (not shown), one or more mobile switching centers (MSCs) (not shown), as well as any other networking components required to connect the wireless carrier system  60  with a land communications system. Each cell tower includes sending and receiving antennas and a base station, with the base stations from different cell towers being connected to the MSC either directly or via intermediary equipment such as a base station controller. The wireless carrier system  60  can implement any suitable communications technology, including for example, digital technologies such as CDMA (e.g., CDMA2000), LTE (e.g., 4G LTE or 5G LTE), GSM/GPRS, or other current or emerging wireless technologies. Other cell tower/base station/MSC arrangements are possible and could be used with the wireless carrier system  60 . For example, the base station and cell tower could be co-located at the same site or they could be remotely located from one another, each base station could be responsible for a single cell tower or a single base station could service various cell towers, or various base stations could be coupled to a single MSC, to name but a few of the possible arrangements. 
     Apart from including the wireless carrier system  60 , a second wireless carrier system in the form of a satellite communication system  64  can be included to provide uni-directional or bi-directional communication with the autonomous vehicles  10   a - 10   n . This can be done using one or more communication satellites (not shown) and an uplink transmitting station (not shown). Uni-directional communication can include, for example, satellite radio services, wherein programming content (news, music, etc.) is received by the transmitting station, packaged for upload, and then sent to the satellite, which broadcasts the programming to subscribers. Bi-directional communication can include, for example, satellite telephony services using the satellite to relay telephone communications between the vehicle  10  and the station. The satellite telephony can be utilized either in addition to or in lieu of the wireless carrier system  60 . 
     A land communication system  62  may further be included that is a conventional land-based telecommunications network connected to one or more landline telephones and connects the wireless carrier system  60  to the remote transportation system  52 . For example, the land communication system  62  may include a public switched telephone network (PSTN) such as that used to provide hardwired telephony, packet-switched data communications, and the Internet infrastructure. One or more segments of the land communication system  62  can be implemented through the use of a standard wired network, a fiber or other optical network, a cable network, power lines, other wireless networks such as wireless local area networks (WLANs), or networks providing broadband wireless access (BWA), or any combination thereof. Furthermore, the remote transportation system  52  need not be connected via the land communication system  62 , but can include wireless telephony equipment so that it can communicate directly with a wireless network, such as the wireless carrier system  60 . 
     Although only one user device  54  is shown in  FIG. 2 , embodiments of the operating environment  50  can support any number of user devices  54 , including multiple user devices  54  owned, operated, or otherwise used by one person. Each user device  54  supported by the operating environment  50  may be implemented using any suitable hardware platform. In this regard, the user device  54  can be realized in any common form factor including, but not limited to: a desktop computer; a mobile computer (e.g., a tablet computer, a laptop computer, or a netbook computer); a smartphone; a video game device; a digital media player; a component of a home entertainment equipment; a digital camera or video camera; a wearable computing device (e.g., smart watch, smart glasses, smart clothing); or the like. Each user device  54  supported by the operating environment  50  is realized as a computer-implemented or computer-based device having the hardware, software, firmware, and/or processing logic needed to carry out the various techniques and methodologies described herein. For example, the user device  54  includes a microprocessor in the form of a programmable device that includes one or more instructions stored in an internal memory structure and applied to receive binary input to create binary output. In some embodiments, the user device  54  includes a GPS module capable of receiving GPS satellite signals and generating GPS coordinates based on those signals. In other embodiments, the user device  54  includes cellular communications functionality such that the device carries out voice and/or data communications over the communication network  56  using one or more cellular communications protocols, as are discussed herein. In various embodiments, the user device  54  includes a visual display, such as a touch-screen graphical display, or other display. 
     The remote transportation system  52  includes one or more backend server systems, not shown), which may be cloud-based, network-based, or resident at the particular campus or geographical location serviced by the remote transportation system  52 . The remote transportation system  52  can be manned by a live advisor, an automated advisor, an artificial intelligence system, or a combination thereof. The remote transportation system  52  can communicate with the user devices  54  and the autonomous vehicles  10   a - 10   n  to schedule rides, dispatch autonomous vehicles  10   a - 10   n , and the like. In various embodiments, the remote transportation system  52  stores store account information such as subscriber authentication information, vehicle identifiers, profile records, biometric data, behavioral patterns, and other pertinent subscriber information. 
     In accordance with a typical use case workflow, a registered user of the remote transportation system  52  can create a ride request via the user device  54 . The ride request will typically indicate the passenger&#39;s desired pickup location (or current GPS location), the desired destination location (which may identify a predefined vehicle stop and/or a user-specified passenger destination), and a pickup time. The remote transportation system  52  receives the ride request, processes the request, and dispatches a selected one of the autonomous vehicles  10   a - 10   n  (when and if one is available) to pick up the passenger at the designated pickup location and at the appropriate time. The transportation system  52  can also generate and send a suitably configured confirmation message or notification to the user device  54 , to let the passenger know that a vehicle is on the way. 
     As can be appreciated, the subject matter disclosed herein provides certain enhanced features and functionality to what may be considered as a standard or baseline autonomous vehicle  10  and/or an autonomous vehicle based remote transportation system  52 . To this end, an autonomous vehicle and autonomous vehicle based remote transportation system can be modified, enhanced, or otherwise supplemented to provide the additional features described in more detail below. 
     In accordance with various embodiments, controller  34  implements an autonomous driving system (ADS)  70  as shown in  FIG. 3 . That is, suitable software and/or hardware components of controller  34  (e.g., processor  44  and computer-readable storage device  46 ) are utilized to provide an autonomous driving system  70  that is used in conjunction with vehicle  10 . 
     In various embodiments, the instructions of the autonomous driving system  70  may be organized by function or system. For example, as shown in  FIG. 3 , the autonomous driving system  70  can include a computer vision system  74 , a positioning system  76 , a guidance system  78 , and a vehicle control system  80 . As can be appreciated, in various embodiments, the instructions may be organized into any number of systems (e.g., combined, further partitioned, etc.) as the disclosure is not limited to the present examples. 
     In various embodiments, the computer vision system  74  synthesizes and processes sensor data and predicts the presence, location, classification, and/or path of objects and features of the environment of the vehicle  10 . In various embodiments, the computer vision system  74  can incorporate information from multiple sensors (e.g., sensor system  28 ), including but not limited to cameras, lidars, radars, and/or any number of other types of sensors. 
     The positioning system  76  processes sensor data along with other data to determine a position (e.g., a local position relative to a map, an exact position relative to a lane of a road, a vehicle heading, etc.) of the vehicle  10  relative to the environment. As can be appreciated, a variety of techniques may be employed to accomplish this localization, including, for example, simultaneous localization and mapping (SLAM), particle filters, Kalman filters, Bayesian filters, and the like. 
     The guidance system  78  processes sensor data along with other data to determine a path for the vehicle  10  to follow. The vehicle control system  80  generates control signals for controlling the vehicle  10  according to the determined path. 
     In various embodiments, the controller  34  implements machine learning techniques to assist the functionality of the controller  34 , such as feature detection/classification, obstruction mitigation, route traversal, mapping, sensor integration, ground-truth determination, and the like. 
     In various embodiments, all or parts of the path planning system  100  may be included within the computer vision system  74 , the positioning system  76 , the guidance system  78 , and/or the vehicle control system  80 . As mentioned briefly above, the path planning system  100  of  FIG. 1  is configured to determine paths using a road-level route with map-based lane connectivity and attributes to generate lane-level information to enable autonomous vehicle path planning. In various embodiments, the lane-level information is represented in terms of two cost components: lane occupancy cost and lane end cost. Using this approach, downstream behavioral planning logic is only required to optimize for driving decisions within the perceived horizon (a single to few road segments ahead). 
     With reference now to  FIG. 4 , a dataflow diagram illustrates all or parts of the path planning system  100  in accordance with various embodiments. It will be understood that various embodiments of the path planning system  100  according to the present disclosure may include any number of sub-modules embedded within the controller  34  which may be combined and/or further partitioned to similarly implement systems and methods described herein. Furthermore, inputs to the path planning system  100  may be received from the sensor system  28 , received from other control modules (not shown) associated with the autonomous vehicle  10 , received from the communication system  36 , and/or determined/modeled by other sub-modules (not shown) within the controller  34  of  FIG. 1 . Furthermore, the inputs might also be subjected to preprocessing, such as sub-sampling, noise-reduction, normalization, feature-extraction, missing data reduction, and the like. In various embodiments, the path planning system  100  includes a navigation planner module  102 , a scene provider module  104 , a route planner module  106 , a behavior planner module  108 , a trajectory planner module  109 , and a visualizer module  110 . 
     In various embodiments, the navigation planner module  102  receives as input navigation data  112 . The navigation data  112  may be generated by a navigation system and includes a desired route of the vehicle  10 . The navigation planner module  102  converts the navigation route to road-level route information based on map data from a map datastore  114  of the vehicle  10  and generates road properties data  116  based thereon. As shown in  FIG. 5 , the navigation planner module  102  generates ordered sequences of road segment IDs (RS 1 , RS 2 , RS 3 , RS 5 ) along the planned route within a horizon ahead of the vehicle  10  and associates properties of the road (e.g., the type of road, the number of lanes, the type of lanes, the speed limit, planned lane closures or construction zones, lanes in direction or in opposite direction to route, etc. identified by the map datastore  114 ) with each of the segments. 
     In various embodiments, the scene provider module  104  receives the road properties data  116 , and perception data  118 . The perception data  118  can include static and/or dynamic information perceived (e.g., from the sensor system) about the lanes such as objects within or near the lane, lane markings, lane types, construction status, lanes in direction of route, congestion level, lanes in opposite direction to route, etc. The scene provider module  104  updates the road properties data  116  based on the perception data  118  to produce lane attributes data  120 . 
     In various embodiments, the route planner module  106  receives the lane attributes data  120 . The route planner module  106  computes cost data  122  for each lane segment based on the lane attributes data  120 . For example, in various embodiments, the route planner module  106  computes a lane occupancy cost (LOC) as a cost of occupying a lane segment and computes a lane end cost (LEC) as a cost of reaching the end of the lane segment. 
     In various embodiments, the LOC is computed based on the lane attributes (LA) and the lane end cost (LEC) for all lanes within the current road segment. For example, lane occupancy cost for i-th lane segment, LOC i  can be computed as: 
       LOC i   =f   1 (LA i )+ f   2 (LEC j=1:n )γ f     3     (LP     i     ) ,  (1)
 
     where LA i  represents lane attributes for the i-th lane segment, LP i  represents the lane property for i-th lane segment, j=1:n represents the lane segments in the current road segment, γ represents a discount factor, and f 1 , f 2  and f 3  represent the cost functions. 
     In various embodiments, the LEC is computed based on the LOC and LEC of the downstream lane segments. For example, the lane end cost for i-th lane segment, LEC i  can be computed as: 
     
       
         
           
             
               
                 
                   
                     
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     where LP i  represents the lane property for i-th lane segment, k=1:m represents the downstream lanes to the i-th lane segment, γ 1  and γ 2  represent discount factors, and g 2 , g 3  and g 4  represent the cost functions. 
     Since the LEC calculation uses the cost data from the downstream road segments, the route planner module starts the calculation of the LEC and LOC from the last road segment in the road segment sequence provided by the navigation planner, e.g., RS 5  in  FIG. 5 . Once done, it will continue backward in the route segment sequence to find LEC and LOC for next road segment, e.g., RS 3  in  FIG. 5 . 
     In various embodiments, in addition to LOC consideration, downstream LEC is backpropagated as a function of estimated time to arrival. For example, the LECi can be computed as: 
     
       
         
           
             
               
                 
                   
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                                       ⁢ 
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                             ⁢ 
                             
                               ( 
                               
                                 
                                   LEC 
                                   j 
                                 
                                 * 
                                 
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                                         ⁢ 
                                         _ 
                                         ⁢ 
                                         length 
                                       
                                       j 
                                     
                                     
                                       
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                                         ⁢ 
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     where j=1: n represents all downstream lanes to the current lane i, and γ represents the discount factor in backpropagation. 
     In various embodiments, the route planner module  106  computes the LOC and the LEC as binary values. For example, the LOC can be set equal to zero if the lane is drivable, otherwise the LOC can be set to one. In another example, the LEC can be set equal to zero when any downstream lanes have a LOC of zero, otherwise the LEC can be set equal to one. 
     In various embodiments, the behavior planner module  108  receives as input the cost data  122  including LOC data and LEC data. The behavior planner module  108  uses the cost data  122  and the other dynamic data from the perception system to generate a behavioral plan and updates behavior plan data  124  based thereon. For example, when all lane end costs are equal to one, a takeover flag may be set to TRUE. Otherwise the takeover flag may set to FLASE. As can be appreciated, other behavior planning can be implemented by the behavior planner module  108  in various embodiments. 
     In various embodiments, the trajectory planner module  109  receives as input the behavior plan data  124 . The trajectory planner module  109  generates trajectory data  125  for controlling future motion of the vehicle  10  based on the behavior plan data  124  and/or other data. 
     In various embodiments, the visualizer module  110  receives as input the cost data, the behavior data  124 , and/or the trajectory data  125 . The visualizer module  110  generates display data  126  based on the received input data to display the plan including the lane level route on a display of the vehicle  10 , for example, to be viewed by a user of the vehicle  10 . 
     With reference now to  FIG. 6 , and with continued reference to  FIGS. 1-5 , a flowchart illustrates a method  400  that may be performed by the path planning system  100  in accordance with various embodiments. As can be appreciated in light of the disclosure, the order of operation within the method is not limited to the sequential execution as illustrated in  FIG. 6 , but may be performed in one or more varying orders as applicable and in accordance with the present disclosure. As can further be appreciated, one or more steps of the methods may be added or removed without altering the spirit of the method. 
     In one example, the method  400  may begin at  405 . A navigation route is received and converted to road-level route information including road segments based on the internal map database at  410 . The lane level attributes are determined and assigned to the road segments at  420 . 
     Thereafter, the LEC and LOC are computed for each of the road segments using back propagation at  430 . The LEC and LOC are evaluated to determine driving behaviors a t 440 . The event planning outputs are converted to trajectory data to control the vehicle and display signals to display to a user of the vehicle  10  at  450 . Thereafter, the method may end at  460 . 
     With reference now to  FIG. 7 , and with continued reference to  FIGS. 1-5 , a flowchart illustrates a method  500  that may be performed by the path planning system  100  in accordance with various embodiments. As can be appreciated in light of the disclosure, the order of operation within the method is not limited to the sequential execution as illustrated in  FIG. 7  but may be performed in one or more varying orders as applicable and in accordance with the present disclosure. As can further be appreciated, one or more steps of the methods may be added or removed without altering the spirit of the method. The method illustrates a more detailed embodiment of step  430  of computing the LEC and LOC. 
     In one example, the method  500  may begin at  505 . The last road segment in the navigation route sequence is selected at  510 . The lane properties and attributes data are determined for the last road segment at  520 . The LEC is computed for all lane segments in the selected road segment using, for example, equation (2) at  530 . The LOC is computed for all lane segments in the selected road segment using, for example, equation (1) at  540 . The next previous road segment in the navigation planner&#39;s road segment sequence is selected at  550 . The backpropagation is performed to compute the cost data from the downstream road segment to the first road segment is performed for all road segments in the sequence at  520 - 540 . Once all road segments have been processed at  550 , the method may end at  560 . 
     While at least one exemplary embodiment has been presented in the foregoing detailed description, it should be appreciated that a vast number of variations exist. It should also be appreciated that the exemplary embodiment or exemplary embodiments are only examples, and are not intended to limit the scope, applicability, or configuration of the disclosure in any way. Rather, the foregoing detailed description will provide those skilled in the art with a convenient road map for implementing the exemplary embodiment or exemplary embodiments. It should be understood that various changes can be made in the function and arrangement of elements without departing from the scope of the disclosure as set forth in the appended claims and the legal equivalents thereof.