System and method for detecting limited traffic zones for automatic driving

Abstract

Systems and methods for detecting restricted traffic zones for automatic driving are disclosed. In one aspect, the method includes receiving an output from a perception system of the autonomous vehicle, and detecting one or more objects indicative of the presence of a restricted traffic zone based on the output from the perception system. The method may also include determining that the autonomous vehicle is entering the restricted traffic zone based on at least one of the previous map, the obstacle map, and the detected one or more objects.

Description

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS

[0002] This application claims the benefit of U.S. Provisional Application No. 63 / 366,395, filed on June 14, 2022, the entire contents of which are incorporated herein by reference. Technical Field

[0003] The present disclosure relates generally to autonomous vehicles and more particularly to systems and methods for detecting restricted traffic zones for autonomous driving. Background Art

[0004] One goal of autonomous vehicle technology is to provide a vehicle that can safely navigate toward a destination with limited or no driver assistance. Safe navigation of an autonomous vehicle (AV) from one location to another may include the ability to signal other vehicles, navigate around other vehicles on the shoulder or emergency lane, change lanes, appropriately veer within a lane, and navigate all sections or types of highway lanes. Summary of the invention

[0005] In one aspect, a method is provided, the method comprising: receiving output from a perception system of an autonomous vehicle; detecting one or more objects indicating the presence of a restricted traffic zone based on the output from the perception system; and determining that the autonomous vehicle is entering the restricted traffic zone based on at least one of a previous map, an obstacle map, and the detected one or more objects.

[0006] In some embodiments, the previous map includes a grid map defining a plurality of drivable lanes, and wherein the obstacle map includes a location of each of a plurality of obstacles in the restricted traffic area.

[0007] In some embodiments, the method further includes generating a drivable area through the restricted traffic zone based on at least one of a previous map, an obstacle map, and the detected one or more objects.

[0008] In some embodiments, the method further includes determining whether the autonomous driving vehicle can drive through a restricted traffic area based on the drivable area.

[0009] In some embodiments, the method further includes: in response to determining that the autonomous vehicle cannot travel through the restricted traffic area, causing the autonomous vehicle to perform a minimum risk condition maneuver.

[0010] In some embodiments, causing the autonomous vehicle to execute the minimum risk condition includes determining whether the autonomous vehicle should stop in the current lane, stop in the emergency lane, or stop in a different part of the drivable area based on the drivable area.

[0011] In some embodiments, the method further includes: connecting the detected one or more objects to form a curve, wherein generating the drivable area includes using the curve as a boundary between the drivable area and the non-drivable area.

[0012] In some embodiments, the method further includes: generating a lane map based on the drivable area, the lane map indicating one or more lanes in the drivable area, the one or more lanes being capable of being used to perform minimum risk condition maneuvers or to enable the autonomous driving vehicle to travel through a traffic restricted area.

[0013] In some embodiments, the method further includes: determining that the curve is parallel to a lane from a previous map and within a predetermined distance of a boundary of the lane; and determining a lane closure based on determining that the curve is parallel to the lane and within a predetermined distance of a boundary of the lane, wherein the lane map indicates the lane closure in response to determining the lane closure.

[0014] In some embodiments, the method further includes: determining that the angle formed by the line and the lane from the previous map is greater than a predetermined angle; and determining that the lane is gradually closing based on determining that the angle formed by the curve and the lane is greater than the predetermined angle, wherein the lane map indicates that the lane is gradually closing in response to determining that the lane is gradually closing.

[0015] In some embodiments, the method further includes: verifying whether the lane map satisfies one or more verification requirements for updating the previous map; and in response to determining that the lane map satisfies the one or more verification requirements, providing the lane map to the previous map updating device.

[0016] In some embodiments, the one or more validation requirements include one or more of the following between the lane map and a previous map: a continuity requirement, a connectivity requirement, and a smoothness requirement.

[0017] In some embodiments, the method also includes: determining that the autonomous driving vehicle is able to drive through a drivable area; and guiding the autonomous driving vehicle through the drivable area based on a lane map.

[0018] In some embodiments, the method further includes: determining that a minimum width of the drivable area is less than a predetermined width; and in response to determining that the minimum width is less than the predetermined width, determining that the autonomous driving vehicle should perform a minimum risk condition maneuver.

[0019] In some embodiments, the method also includes: receiving a requirement for the autonomous driving vehicle to drive through a drivable area from a planning module of the autonomous driving vehicle; and based on the requirement, determining whether the autonomous driving vehicle can drive through the drivable area, or whether a minimum risk condition maneuver should be performed.

[0020] In some embodiments, the requirement includes one or more of: current traffic conditions, a current speed of the autonomous vehicle, and a current perception range of the perception system.

[0021] In some embodiments, the detected one or more objects include one or more of: cones, barricades, temporary barricades, obstacles, barrels, emergency lights, emergency signs, emergency vehicles, vehicles, pedestrians, first responders, and construction zone signs.

[0022] In some embodiments, the restricted traffic area includes one or more of the following: an emergency area, a construction area, an accident site, a work zone, and a traffic control area.

[0023] In another aspect, a device is provided, comprising: at least one processor; and at least one memory comprising computer program code, the at least one memory and the computer program code being configured to, together with the at least one processor, enable the device to: receive output from a perception system of an autonomous vehicle; detect the presence of one or more emergency area warning devices based on the output from the perception system; receive a previous map indicating an environment in which the autonomous vehicle is capable of traveling; and determine that the autonomous vehicle is entering an emergency area based on the detected presence of the one or more emergency area warning devices and the previous map.

[0024] In another aspect, a non-transitory computer-readable medium is provided that stores computer program instructions that, when executed by at least one processor, cause the at least one processor to: receive output from a perception system of an autonomous vehicle; detect the presence of one or more emergency area warning devices based on the output from the perception system; receive a previous map indicating an environment in which the autonomous vehicle is capable of traveling; and determine that the autonomous vehicle is entering an emergency area based on the detected presence of one or more emergency area warning devices and the previous map. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] For a more complete understanding of the present disclosure, reference is now made to the following brief description taken in conjunction with the accompanying drawings and detailed description, wherein like reference numerals represent like parts.

[0026] Figure 1 A schematic diagram of a system including an autonomous vehicle is illustrated.

[0027] Figure 2 A flow chart for safely operating an autonomous vehicle (AV) based on the AV's health and surrounding environment is shown.

[0028] Figure 3A system including one or more autonomous vehicles, a control center or supervisory system with a human operator (e.g., a remote center operator (RCO)), and an interface for third-party interaction is illustrated.

[0029] Figure 4 is a flow chart illustrating a method for detecting a restricted traffic zone for autonomous driving according to aspects of the present disclosure.

[0030] Figure 5 is a block diagram illustrating a method for detecting a restricted traffic zone for autonomous driving according to aspects of the present disclosure. DETAILED DESCRIPTION

[0031] Vehicles traversing highways and roads are legally required to comply with laws and regulations in the vehicle's safe operating scenario. For autonomous vehicles (AVs), particularly autonomous tractor trailers, the ability to identify a system failure and safely stop the vehicle is necessary for the vehicle to operate legally and safely. Systems and methods for safe and legal operation of an autonomous vehicle on a roadway are described in detail below, including performing maneuvers to cause the autonomous vehicle to comply with the law while signaling surrounding vehicles of its condition.

[0032] Aspects of the present disclosure relate to systems and techniques that can detect restricted traffic areas for autonomous driving. In particular, it is desirable for an autonomous vehicle to be able to detect restricted traffic areas that include objects that may not be present in a navigation map to determine whether the autonomous vehicle can safely continue to travel through the detected area. Examples of such areas include: emergency areas, construction areas, accident sites, work areas, and traffic control areas. By detecting restricted traffic areas, the autonomous vehicle can determine whether it is safe to continue traveling through the detected restricted traffic area, or whether the autonomous vehicle should stop driving to avoid entering an area where the autonomous vehicle cannot safely travel.

[0033] Figure 1A system 100 is shown including a tractor 105 of an autonomous truck. The tractor 105 includes a plurality of vehicle subsystems 140 and an onboard control computer 150. The plurality of vehicle subsystems 140 include a vehicle drive subsystem 142, a vehicle sensor subsystem 144, and a vehicle control subsystem. An engine or motor, wheels and tires, a transmission, an electrical subsystem, and a power subsystem may be included in the vehicle drive subsystem. The engine of the autonomous vehicle may be an internal combustion engine, a fuel cell-driven electric engine, a battery-driven electric engine, a hybrid engine, or any other type of engine capable of moving the wheels on which the tractor 105 moves. The tractor 105 has a plurality of motors or actuators to drive the wheels of the vehicle, so that the vehicle drive subsystem 142 includes two or more electric drive motors. The transmission may include a continuously variable transmission or a set of gears that convert the power generated by the engine into a force that drives the wheels of the vehicle. The vehicle drive subsystem may include an electrical system that monitors and controls the distribution of current to components within the system, including pumps, fans, and actuators. The power subsystem of the vehicle drive subsystem may include components that regulate the power source of the vehicle.

[0034] The vehicle sensor subsystem 144 may include sensors for general operation of the autonomous truck 105, including those that indicate an AV malfunction or other reason for the AV to perform a limited or minimal risk condition (MRC) maneuver. Sensors for general operation of the autonomous vehicle may include cameras, temperature sensors, inertial sensors (IMUs), global positioning systems (GPS), light sensors, light detection and ranging (LiDAR) systems, radar systems, and wireless communications.

[0035] A sound detection array, such as a microphone or microphone array, may be included in the vehicle sensor subsystem 144. The microphones of the sound detection array are configured to receive audio indications of the presence of authorities or instructions from authorities, including sirens and commands such as "pull over". These microphones are mounted or positioned on an exterior portion of the vehicle, particularly on the exterior of the tractor portion of the self-driving truck 105. The microphones used may be of any suitable type, mounted so that they are effective both when the self-driving truck 105 is stationary and when the self-driving vehicle is moving at normal driving speeds.

[0036] The cameras included in the vehicle sensor subsystem 144 may be rear-facing so that flashing lights from emergency vehicles may be observed from around the self-driving truck 105. These cameras may include video cameras, cameras with filters for specific wavelengths, and any other cameras suitable for detecting emergency vehicle lights based on color, flash, or both color and flash.

[0037] The vehicle control subsystem 146 may be configured to control the operation of the autonomous vehicle or truck 105 and its components. Thus, the vehicle control subsystem 146 may include various elements, such as an engine power output subsystem, a braking unit, a navigation unit, a steering system, and an autonomous control unit. The engine power output may control the operation of the engine, including the torque produced or the horsepower provided, and provide control over the gear selection of the transmission. The braking unit may include any combination of mechanisms configured to decelerate the autonomous vehicle 105. The braking unit may use friction to decelerate the wheels in a standard manner. The braking unit may include an anti-lock braking system (ABS), which may prevent the brakes from locking when being braked. The navigation unit may be any system configured to determine the driving path or route of the autonomous vehicle 105. The navigation unit may also be configured to dynamically update the driving path while the autonomous vehicle 105 is operating. In some embodiments, the navigation unit may be configured to merge data from a GPS device and one or more predetermined maps in order to determine the driving path of the autonomous vehicle 105. The steering system may represent any combination of mechanisms that may be operable to adjust the heading of the autonomous vehicle 105 in an autonomous mode or a driver-controlled mode.

[0038] The autonomous control unit may represent a control system that is configured to identify, evaluate, and avoid or otherwise negotiate potential obstacles in the environment of the autonomous vehicle 105. In general, the autonomous control unit may be configured to control the autonomous vehicle 105 to operate without a driver, or to provide driver assistance in controlling the autonomous vehicle 105. In some embodiments, the autonomous control unit may be configured to incorporate data from a GPS device, RADAR, LiDAR (e.g., LIDAR), camera, and / or other vehicle subsystems to determine a driving path or trajectory for the autonomous vehicle 105. Autonomous control may activate systems of the autonomous vehicle 105 that may not be present in a conventional vehicle, including systems that may allow the autonomous vehicle 105 to communicate with surrounding drivers or signal surrounding vehicles or drivers to enable safe operation of the autonomous vehicle 105.

[0039] The onboard control computer 150, which may be referred to as a VCU, includes a vehicle subsystem interface 160, a driving operation module 168, one or more processors 170, a compliance module 166, a memory 175, and a network communication subsystem 178. The onboard control computer 150 controls many (if not all) operations of the autonomous truck 105 in response to information from various vehicle subsystems 140. One or more processors 170 perform operations that allow the system to determine the health of the autonomous vehicle 105, such as whether the autonomous vehicle 105 has failed or encountered a situation that requires service or deviates from normal operation and give instructions. Data from the vehicle sensor subsystem 144 is provided to the VCU 150 so that the state of the autonomous vehicle 105 can be determined. The compliance module 166 can determine what actions the autonomous truck 105 should take to operate in accordance with applicable (e.g., local) regulations. Data from other vehicle sensor subsystems 144 can be provided to the compliance module 166 so that the best course of action can be appropriately determined and executed given the state of the AV. Alternatively or additionally, the compliance module 166 may determine a course of action in conjunction with another operation or control module, such as the driving operation module 168 .

[0040] The memory 175 may also contain additional instructions, including instructions to transmit data to, receive data from, interact with, or control one or more of the vehicle drive subsystem 142, the vehicle sensor subsystem 144, and the vehicle control subsystem 146 including the autonomous control system. The vehicle control computer (VCU) 150 can control the functions of the autonomous vehicle 105 based on inputs received from various vehicle subsystems (e.g., the vehicle drive subsystem 142, the vehicle sensor subsystem 144, and the vehicle control subsystem 146). In addition, the VCU 150 can send information to the vehicle control subsystem 146 to guide the trajectory, speed, signaling behavior, etc. of the autonomous vehicle 105. The autonomous control vehicle control subsystem can receive the action plan to be taken from the compliance module 166 of the VCU 150, and thus relay instructions to other subsystems to execute the action plan.

[0041] Figure 2 A flow chart for operating an autonomous vehicle (AV) 105 in view of the health of the autonomous vehicle 105 and the surrounding environment is shown. Although the figure describes the functional steps in a particular order for the purpose of illustration, the process is not limited to any particular order or arrangement of steps. Those skilled in the relevant art will understand that the various steps depicted in the figure may be omitted, rearranged, combined and / or adapted in various ways.

[0042] like Figure 2As shown, in step 205, the vehicle sensor subsystem 144 may receive visual, auditory, or visual and auditory signals indicative of environmental conditions of the autonomous vehicle 105. In step 205, the vehicle sensor subsystem 144 may also receive vehicle health or sensor activity data. These visual and / or auditory signal data are transmitted from the vehicle sensor subsystem 144 to the vehicle control computer system (VCU) 150, as in step 210. In step 215, the driving operation module 168 and / or the compliance module 166 may receive the data transmitted from the vehicle sensor subsystem 144. In step 220, one or both of these modules may determine whether the current state of the autonomous vehicle 105 allows it to proceed in the usual manner, or whether the autonomous vehicle 105 needs to change its route to prevent damage or injury or to allow service. Information indicating a need to change the route of the autonomous vehicle 105 may include an indicator of a sensor failure; an indicator of an engine, brake, or other component failure required for the operation of the autonomous vehicle 105; a determination of visual indications from authorities, such as flares, cones, or signs; a determination of the presence of an authority figure on the road; a determination that a law enforcement vehicle on the road is approaching the autonomous vehicle 105, including from which direction; and a determination that a law enforcement vehicle or first responder vehicle is moving away from the autonomous vehicle or on a road separate from the autonomous vehicle 105. In step 225, this information indicating a need to change the AV's course of action may be used by the compliance module 166 to formulate a new course of action to be taken that takes into account the health of the AV and the surrounding environment. The action to be taken may include slowing down, stopping, pulling onto the shoulder of the road, changing route, changing lanes while staying on the same general route, etc. The course of action to be taken may include initiating communication with a supervisory system (e.g., a control center) or a human-machine interaction system present on the autonomous vehicle 105. In step 230, the course of action to be taken may be transmitted from the VCU 150 to the autonomous control system. Then in step 235 , the vehicle control subsystem 146 causes the autonomous vehicle 105 to operate according to the action plan to be taken received from the VCU 150 .

[0043] It should be understood that the specific order of steps or hierarchy in the process disclosed herein is an example of an exemplary method. Based on design preferences, it is understood that the specific order of steps or hierarchy in the process can be rearranged while remaining within the scope of the present disclosure. The attached method requires that the elements of various steps are presented in a sample order, and is not intended to be limited to the specific order or hierarchy presented.

[0044] Autonomous vehicle supervision system

[0045] Figure 3A system 300 is illustrated that includes one or more autonomous vehicles 105, a control center or supervisory system 350 with a human operator 355, and an interface 362 for interacting with a third party 360. The human operator 355 may also be referred to as a remote center operator (RCO). Communications between the autonomous vehicles 105, the supervisory system 350, and the user interface 362 may occur over a network 370. In some cases, where not all autonomous vehicles 105 in a fleet are able to communicate with the supervisory system 350, the autonomous vehicles 105 may communicate over the network 370 or directly with each other. As described with respect to FIG. Figure 1 As described above, the VCU 150 of each autonomous vehicle 105 may include a module for a network communication subsystem 178 .

[0046] The autonomous vehicles 105 may communicate with the supervision system 350. The supervision system 350 may be used for a variety of purposes, including: tracking the progress of one or more autonomous vehicles 105 (e.g., autonomous trucks); tracking the progress of a fleet of autonomous vehicles 105; sending maneuver instructions to one or more autonomous vehicles 105; monitoring the health of the autonomous vehicle(s) 105; monitoring the status of the cargo of each autonomous vehicle 105 in contact with the supervision system 350; facilitating communications between third parties (e.g., law enforcement, cargo transportation customers) and each or specific autonomous vehicle 105; allowing tracking of specific autonomous vehicles 105 in communication with the supervision system 350 (e.g., third-party tracking of a subset of vehicles in a fleet); arranging maintenance services for the autonomous vehicles 105 (e.g., oil changes, refueling, maintaining levels of other fluids); alerting affected autonomous vehicles 105 of traffic or weather changes that may adversely affect routes or delivery schedules; facilitating over-the-air updates of the autonomous vehicles 105 to keep all components up to date; and other purposes or functions that improve the safety of the autonomous vehicles 105, their cargo, and their surroundings. The supervision system 350 may also determine performance parameters of the autonomous vehicle 105 (e.g., an autonomous truck), including any of the following: data logging frequency, compression rate, location, data type; communication priority; maintenance frequency of the autonomous vehicle 105 (e.g., mileage between maintenance); when a minimum risk condition (MRC) maneuver should be performed while monitoring the vehicle's progress during the maneuver; when control of the autonomous vehicle 105 should be handed over to a human driver (e.g., at a destination parking lot); ensuring that the autonomous vehicle 105 passes a pre-trip inspection; ensuring that the autonomous vehicle 105 executes or complies with legal requirements at checkpoints and weigh stations; ensuring that the autonomous vehicle 105 executes or complies with human instructions at roadblocks, crosswalks, intersections, construction, or accident sites; and the like.

[0047] To allow communication between the autonomous vehicles 105 in the fleet and the supervisory system 350, each autonomous vehicle 105 may be equipped with a communication gateway. The communication gateway may have the ability to perform any of the following: allow AV to supervisory system communication (i.e., V2C) and supervisory system to AV communication (C2V); allow AV to AV communication (V2V) within the fleet; transmit the availability or status of the communication gateway; confirm received communications; ensure the security of remote commands between the autonomous vehicle 105 and the supervisory system 350; reliably communicate the location of the autonomous vehicle at set time intervals; enable the supervisory system 350 to ping the autonomous vehicle 105 to obtain location and vehicle health status; allow various sensor data to be streamed directly to the supervisory system 350; allow automatic alerts between the autonomous vehicle 105 and the supervisory system 350; comply with the ISO 21434 standard; and the like.

[0048] The supervision system 350 may be operated by one or more persons, also referred to as operators or remote center operators (RCOs) 355. The operators 355 may set thresholds for autonomous vehicle health parameters so that when the autonomous vehicle 105 reaches or exceeds the thresholds, preventive measures may be taken. Examples of vehicle health parameters for which thresholds may be established by the operator 355 may include any of the following: fuel level; oil level; miles driven since last maintenance; low tire pressure detected; washer fluid level; brake fluid level; responsiveness of steering and brake subsystems; diesel exhaust fluid (DEF) level; communication capabilities (e.g., lack of responsiveness); positioning sensor capabilities (e.g., GPS, IMU failure); collision detection (e.g., vehicle collision); perception sensor capabilities (e.g., camera, LiDAR, radar, microphone array failure); computing resource capabilities (e.g., VCU or ECU failure or lack of responsiveness, temperature anomalies in computing units); angle between the tractor and trailer of the autonomous vehicle 105 in a towing situation (e.g., tractor trailer, 18-wheeler, or semi-truck); unauthorized entry of a living being (e.g., a person or animal) into the interior of the autonomous vehicle 105; etc. Preventive measures may include performing a minimum risk condition (MRC) maneuver, seeking service, or leaving the highway or other such diversion that is less taxing on the autonomous vehicle 105. An autonomous vehicle 105 whose system health data meets or exceeds a threshold set at the supervisory system 350 or a threshold set by an operator 355 may receive instructions automatically sent from the supervisory system 350 to perform preventive measures.

[0049] An operator 355 may be made aware of conditions that affect one or more autonomous vehicles 105 in communication with or monitored by the supervisory system 350, while the affected autonomous vehicle(s) 105 may be unaware. Such conditions may include: irregular or sudden changes in traffic flow (e.g., traffic jams or accidents); sudden changes in weather; sudden changes in visibility; emergencies (e.g., fires, sinkholes, bridge failures); power outages that affect signal lights; unexpected road works; large or obscure road debris (e.g., objects that the autonomous vehicle cannot identify); law enforcement activity on the road (e.g., car chases or road clearing activities); and the like. These types of conditions that may not be detected by the autonomous vehicle 105 may be brought to the attention of the operator 355 through traffic reports, law enforcement communications, data from other vehicles in communication with the supervisory system 350, reports from drivers of other vehicles in the area, and similar distributed information venues. The autonomous vehicle 105 may not be able to detect such conditions due to limitations of the sensor system or lack of access to information distribution components (e.g., no direct communication with a weather agency). An operator 355 of the supervisory system 350 may push such information to an affected autonomous vehicle 105 in communication with the supervisory system 350. The affected autonomous vehicle 105 may change its route, trajectory, or speed in response to the information pushed from the supervisory system 350. In some cases, the information received by the supervisory system 350 may trigger a threshold condition indicating that an MRC (minimum risk condition) maneuver is warranted; alternatively or additionally, the operator 355 may assess the situation and determine that the affected autonomous vehicle 105 should perform an MRC maneuver, and then send such instructions to the affected vehicle. In these cases, each autonomous vehicle 105 that receives information or instructions from the supervisory system 350 or the operator 355 uses its onboard computing unit (i.e., VCU) to determine how to safely travel, including performing an MRC maneuver, including pulling over or stopping.

[0050] System and method for detecting restricted traffic zones for automated driving

[0051] As described herein, aspects of the present disclosure relate to systems and techniques that can detect restricted traffic zones for autonomous driving, such that the autonomous driving vehicle 105 can determine whether it is safe to continue driving through the detected restricted traffic zone, or whether the autonomous driving vehicle 105 should stop driving to avoid entering an area where the autonomous driving vehicle 105 cannot safely travel. Examples of such areas include: emergency zones, construction zones, accident sites, work zones, and traffic control zones.

[0052] When the autonomous vehicle 105 determines that it cannot safely navigate in the restricted traffic area, the autonomous vehicle 105 may terminate the automatic navigation by stopping the autonomous vehicle 105 in its current lane and / or pulling over. In some cases, the autonomous vehicle 105 may be configured to perform an MRC maneuver, in which the autonomous vehicle 105 autonomously maneuvers to a stopping position. In some embodiments, the MRC maneuver may be performed under the supervision of the operator 355 via the supervision system 350.

[0053] Figure 4 is a flow chart illustrating a method 400 for detecting a restricted traffic zone for autonomous driving according to aspects of the present disclosure. Figure 4 , one or more blocks of the method 400 may be implemented, for example, by a processor such as the VCU 150 of the autonomous vehicle 105. The method 400 starts at block 401.

[0054] At block 402, the processor may receive output from a perception system of the autonomous vehicle. As used herein, a perception system may be used to refer to Figure 1 Any and all vehicle sensor subsystems 144 are shown. The processor may also receive previous maps and / or obstacle maps stored in memory 175.

[0055] At block 404, the processor may detect one or more objects indicating the presence of a restricted traffic zone based on the output from the perception system. For example, the processor may be configured to detect objects used by workers to signal the presence of a restricted traffic zone and / or objects typically found in a restricted traffic zone. Examples of such objects include cones, barricades, temporary barricades, obstacles, barrels, emergency lights, emergency signs, emergency vehicles, other types of vehicles, pedestrians, first responders, and construction zone signs.

[0056] At block 406 , the processor may determine that the autonomous vehicle 105 is entering a restricted traffic area based on at least one of the previous map, the obstacle map, and the detected one or more objects. The method 400 ends at block 408 .

[0057] Figure 5 5 is a block diagram illustrating a method for detecting a restricted traffic zone for autonomous driving according to aspects of the present disclosure. Specifically, Figure 5 Provides the execution Figure 4 The method 400 may be implemented in more detail as a sub-frame. Figure 5 , one or more blocks of method 500 may be implemented, for example, by a processor such as VCU 150 of autonomous driving vehicle 105 .

[0058] refer to Figure 5, the processor may receive a previous map 502 (also referred to as TsMap), an obstacle map 504, and a perception output 506. Specifically, the processor may receive the previous map 502 and the obstacle map 504 from the memory 175, and may receive the perception output 506 from the perception system (e.g., Figure 1 The vehicle sensor subsystem ) receives the perception output 506.

[0059] In some implementations, the previous map 502 includes a grid map defining a plurality of drivable lanes in which the autonomous vehicle 105 may travel. The previous map 502 may include a navigation map with lane-level detail sufficient to plan a route between the current location of the autonomous vehicle 105 and a destination that may be used for autonomous driving.

[0060] The obstacle map 504 may include data indicating the location of each of a plurality of obstacles in and / or near an environment in which the autonomous vehicle 105 may travel, including in restricted traffic areas. For example, the obstacle map 504 may be a grid-based map that records prior information of static objects (e.g., objects that do not generally change position). In some embodiments, the obstacle map 504 may be an occupancy grid map that indicates, for each location or voxel, whether a static object is present (e.g., whether the location is occupied) and may include other information, such as the type or semantic label of the object. Together, the prior map 502 and the obstacle map 504 provide information about drivable roads and objects on or near the roads with sufficient accuracy for the autonomous vehicle 105 to navigate to its destination.

[0061] However, previous map 502 and obstacle map 504 may not provide information about the current state of the road and / or objects (e.g., static or dynamic objects) on or near the road, or may provide insufficient information. For example, when a new restricted traffic area, such as a construction area, is established, previous map 502 and obstacle map 504 may not have any information about the drivable area within the restricted traffic area. Therefore, the processor may be configured to supplement previous map 502 and obstacle map 504 with information received from the perception system.

[0062] The perception output 506 may include an occupancy grid map including objects detected by the sensor(s) of the vehicle sensor subsystem 144. The perception system may be configured to interpret the raw data received from the vehicle sensor subsystem 144 and generate an occupancy grid map including the occupancy and / or semantic labels of each detected object. For example, the perception system may classify the objects into one or more of the following semantic labels: cones, roadblocks, temporary roadblocks, obstacles, barrels, emergency lights, emergency signs, emergency vehicles, other types of vehicles, pedestrians, first responders, and construction zone signs.

[0063] At block 508, the processor may generate a drivable area through the restricted traffic zone based on at least one of the previous map 502, the obstacle map 504, and the detected one or more objects. As part of block 508, the processor may also determine whether the autonomous vehicle 105 is entering a restricted traffic zone based on the previous map 502, the obstacle map 504, and the perception output 506. In some implementations, the processor may also determine the type of restricted traffic zone (e.g., an emergency zone, a work zone, or a traffic control zone).

[0064] The processor may use one or more objects from the perception output 506 when generating a drivable area. For example, the processor may connect multiple objects to form a curve and use the curve as a boundary between a drivable area and a non-drivable area. For example, when the object includes multiple cones, workers may have placed cones to define the boundary of the drivable area within the restricted traffic area. Therefore, by connecting the cones into a curve, the processor may generate a boundary between a drivable area and a non-drivable area based on the information conveyed by the presence of the cones. The processor may use a similar process to determine the boundary for other objects that indicate the boundary of a drivable area. For example, the processor may form a curve based on the location of roadblocks, temporary roadblocks, obstacles, and / or barrels.

[0065] The processor may also generate a lane map of the drivable area. The lane map may indicate one or more lanes in the drivable area that may be used to perform a minimum risk condition maneuver or to allow the autonomous vehicle 105 to travel through a traffic restricted area. In some embodiments, when generating the lane map, the processor may modify the lane map from the previous map 502 based on an object from the perception output 506. For example, the processor may modify the lane map based on one or more conditions regarding the relative positioning of the object and the lane indicated by the lane map.

[0066] In a first example, the processor may determine whether the curve formed by the object is substantially parallel to the lane in the previous map 502 (e.g., the angle between the curve and the lane markings defining the lane boundary is less than a predetermined angle) and is within a predetermined distance of the lane boundary. The processor may determine a lane closure based on determining that the curve is parallel to the lane and within the predetermined distance of the lane boundary. The processor may then generate a lane map to indicate the lane closure in response to determining the lane closure. As part of generating the lane closure indication, the processor may generate the boundaries of the lane closure (e.g., the boundaries of a restricted traffic area).

[0067] In a second example, the processor may determine whether the angle formed by the curve with the lane in the previous map 502 is greater than a predetermined angle. The processor may determine that the lane is gradually closing based on determining that the angle formed by the curve with the lane is greater than the predetermined angle. The processor may then generate a lane map to indicate that the lane is gradually closing in response to determining that the lane is gradually closing.

[0068] In a third example, the processor may determine that the curve does not meet the criteria of any of the first and second examples described above. In this case, the processor may determine one or more new lanes based on the location of the curve. For example, the processor may identify a new lane that is substantially parallel to the curve. The new lane may not be fixed to the location of any lane within the previous map 502. The generation of a new lane map may be similar to moving the location of the previous lane and / or "redrawing" the lane based on the location of the curve.

[0069] At box 510, the processor can perform an MRC maneuver analysis to determine whether the autonomous vehicle 105 can pass through the restricted traffic area based on the drivable area through the restricted area, or whether the autonomous vehicle 105 should perform an MRC maneuver to stop in its current lane or in an emergency lane.

[0070] In some implementations, the processor may receive a requirement for the autonomous vehicle 105 to drive through the drivable area of ​​the restricted traffic zone from a planning module of the autonomous vehicle 105. The processor may determine whether the autonomous vehicle 105 can drive through the drivable area of ​​the restricted traffic zone or whether the MRC maneuver should be performed based on the requirement.

[0071] In some implementations, one or more requirements for determining whether the autonomous vehicle 105 can travel through the restricted traffic area may vary according to current environmental conditions. For example, the above requirements may include one or more of the following: current traffic conditions, current speed of the autonomous vehicle 105, current perception range of the perception system, and minimum width of the drivable area.

[0072] For example, the processor may determine whether the minimum width of the drivable area is less than a predetermined width. In response to determining that the minimum width is less than the predetermined width, the processor may determine that the autonomous vehicle 105 should perform an MRC maneuver. In some implementations, the predetermined width may be 3 meters, however, the present disclosure is not limited thereto, and the predetermined width may be less than or greater than 3 meters. According to an embodiment, the predetermined width may be based on the width of the autonomous vehicle 105.

[0073] In response to determining that the autonomous vehicle 105 cannot drive through the restricted traffic area, the processor can cause the autonomous vehicle 105 to perform an MRC maneuver. The processor can also determine, based on the drivable area, whether the MRC maneuver should include the autonomous vehicle 105 stopping in the current lane, stopping in the emergency lane, or stopping in a different part of the drivable area.

[0074] At box 512, the processor may perform a validity check to determine whether any portion of the drivable area can be output as a new lane map. In some embodiments, the processor may verify whether the lane map satisfies one or more validation requirements for updating the previous map 502. The processor may provide the lane map to a previous map update device (e.g., via the supervisory system 350) in response to determining that the lane map satisfies one or more validation requirements. The one or more validation requirements may include one or more of the following between the lane map and the previous map: continuity requirements, connectivity requirements, and smoothness requirements.

[0075] At box 514, the processor may output a new lane map in response to the new lane map passing the validity check of box 512. For example, the processor may provide the new lane map to a previous map update device. The processor may also determine that the autonomous driving vehicle 105 can drive through the drivable area, and navigate the autonomous driving vehicle 105 in the drivable area based on the lane map.

[0076] In some embodiments, the autonomous vehicle 105 may record the results of the method implemented by block diagram 500 regardless of whether the new lane map passes the validity check of block 512. For example, the autonomous vehicle 105 may record the results of the method implemented by block diagram 500. Figure 5 The data generated by the method is provided to the supervision system 300 or another centralized server to identify false positives and / or false negatives. The data can be used to improve the method when it is performed by the autonomous driving vehicle 105 in the future. For example, the requirement for the autonomous driving vehicle 105 to travel through the drivable area and / or one or more verification requirements can be based on the requirements of the method performed by the autonomous driving vehicle 105. Figure 5 The method is regulated by data generated by one or more autonomous vehicles 105.

[0077] In some embodiments, the processor may also use map hard boundaries and / or soft boundaries to determine whether to use the identified restricted traffic area when navigating the autonomous vehicle 105 to reduce false positives, where the detected restricted traffic area would negatively affect the navigation of the autonomous vehicle 105. For example, the processor may use hard boundaries and / or soft boundaries to determine whether to use the determination of a given lane closure when navigating the autonomous vehicle 105. In some implementations, the previous map 502 may include the location(s) of any hard boundaries and / or soft boundaries near the road. For example, a hard boundary may include a boundary near a road that a vehicle cannot actually cross. Examples of hard boundaries include: hard guardrails and / or concrete walls. Soft boundaries may include, for example, boundaries near a road that a vehicle can actually cross. Examples of soft boundaries include pavement boundaries of a road (e.g., a boundary between a pavement and unpaved ground next to the pavement).

[0078] The processor may determine whether a boundary of a restricted traffic zone (e.g., a lane closure) is outside a hard boundary (e.g., on the opposite side of the hard boundary from the autonomous vehicle 105). In response to determining that the boundary of the restricted traffic zone is outside the hard boundary, the processor may avoid using the determined restricted traffic zone when navigating the autonomous vehicle 105.

[0079] The processor may determine whether a boundary of a restricted traffic zone (e.g., a lane closure) is located outside a predetermined distance outside the soft boundary (e.g., on an opposite side of the soft boundary relative to the autonomous vehicle 105). In response to determining that the boundary of the restricted traffic zone is located outside the predetermined distance outside the soft boundary, the processor may avoid using the determined restricted traffic zone when navigating the autonomous vehicle 105. In response to the restricted traffic zone being closer than the predetermined distance outside the soft boundary, the processor may use the restricted traffic zone when navigating the autonomous vehicle 105. For example, as described herein, the processor may cause the autonomous vehicle 105 to slow down, change lanes, and / or perform MRC based on the identified restricted traffic zone.

[0080] By avoiding the use of a detected restricted traffic zone under certain conditions (e.g., when the restricted traffic zone is outside of a hard boundary and / or outside of a predetermined distance outside of a soft boundary), the processor can reduce the occurrence of false positives that may cause the processor to slow down the autonomous vehicle 105. Thus, when a detected restricted traffic zone does not affect the drivable area available to the autonomous vehicle 105, the processor can continue to navigate the autonomous vehicle 105 with fewer false positives.

[0081] In certain embodiments, the processor may also be configured to apply a time cone filter to reduce false alarms associated with detected cones. As described herein, the processor may be configured to determine the boundary of a restricted traffic zone (e.g., a lane closure) based on the positions of detected cones and / or posts. However, in some cases, the processor may detect cones or posts that are false alarms representing the location of a boundary. For example, one or more cones and / or posts may have moved from their initial positions, and therefore, the positions of the moved cone(s) and / or posts may be incorrectly interpreted as the boundary of a restricted traffic zone. By using a time cone filter, the processor can reduce the occurrence of false alarms due to changes in the positions of cone(s) and / or posts.

[0082] As part of implementing the time cone filter, the processor may divide the road into a grid. According to an embodiment, the grid may be the same as the grid of the occupancy grid map generated based on the perception output 506. However, in other embodiments, the grid used in the time cone filter may be independent of other grids generated by the processor. In some embodiments, the grid may include a coordinate system, such as a northeast up (ENU) coordinate system, however, aspects of the present disclosure are not limited thereto. In one example, the grid may be formed by 1×1 square meter grid locations.

[0083] The processor may assign each cone and / or post detected in the perception output 506 to a position within the grid. The processor may determine whether the number of cones and / or posts within a given grid position is greater than a threshold number. In response to determining that the number of cones and / or posts within a given grid position is greater than the threshold number, the processor may determine that the grid position forms part of a boundary of a restricted traffic area (e.g., a lane closure). When the number of cones and / or posts within a given grid position is equal to or less than the threshold number, the processor may determine that the grid position does not form part of a boundary of a restricted traffic area (e.g., a lane closure), in other words, filter any cones or posts detected at the given grid position. By filtering cones and / or posts in this manner, the processor may reduce the number of false positives described above.

[0084] As part of detecting the restricted traffic zone, for example, as discussed in connection with method 400 and / or block diagram 500, the processor may determine one or more sets of data that may be provided to the vehicle control subsystem 146 for use in navigating the autonomous vehicle 105. Depending on the embodiment, the processor may determine one or more of the following: the number of lanes closed due to the restricted traffic zone, the area of ​​the restricted traffic zone, one or more boundaries of the restricted traffic zone, or a direction associated with the restricted traffic zone (e.g., whether the restricted traffic zone is on the left or right side of the road).

[0085] Depending on the distance between the current position of the autonomous vehicle 105 and the restricted traffic zone, the processor may not be able to accurately determine each of the above-mentioned sets of data to be provided to the vehicle control subsystem 146. For example, when the distance of the autonomous vehicle 105 from the restricted traffic zone exceeds a threshold distance (e.g., 200 meters), the perception output 506 may not have sufficient details to determine, for example, the boundary of the restricted traffic zone and / or the number of closed lanes in the restricted traffic zone. Therefore, when the distance of the autonomous vehicle 105 from the restricted traffic zone exceeds the threshold distance, the processor may only provide a subset of the data set related to the restricted traffic zone to the vehicle control subsystem 146.

[0086] In some implementations, the processor may determine a confidence level for each set of data associated with the restricted traffic zone. The processor may provide each set of data having a confidence level above a threshold level to the vehicle control subsystem 146. In other implementations, the processor may be configured to determine the extent of the restricted traffic zone when the distance of the restricted traffic zone from the autonomous vehicle 105 is greater than a threshold distance. When the distance to the restricted traffic zone is greater than the threshold distance, the processor may provide the extent of the restricted traffic zone to the vehicle control subsystem 146 in place of other data sets. This may reduce false positives (e.g., when determining the number of closed lanes) when the perception output 506 may not have sufficient detail (e.g., determining the number of closed lanes). Conclusion

[0087] Although this document relates to self-driving trucks, it should be understood that any self-driving ground vehicle can have such features. Self-driving vehicles that travel on the ground may include: semi-trailers, tractor trailers, 18-wheelers, trucks, Class 8 vehicles, passenger cars, transporters, freight trucks, recreational vehicles, golf carts, transport vehicles, etc.

[0088] Although several embodiments are provided in the present disclosure, it should be understood that the disclosed systems and methods may be embodied in many other specific forms without departing from the spirit or scope of the present disclosure. The present embodiments are considered to be illustrative rather than restrictive, and are not intended to be limited to the details given herein. For example, various elements or components may be combined or integrated in another system, or certain features may be omitted or not implemented.

[0089] In addition, without departing from the scope of the present disclosure, the techniques, systems, subsystems and methods described and shown as discrete or separate in various embodiments may be combined or integrated with other systems, modules, techniques or methods. Other items shown or discussed as coupled or directly coupled or communicating with each other may be indirectly coupled or communicated through some interface, device or intermediate component, whether electrically, mechanically or otherwise. Other examples of changes, substitutions and variations are within the ability of those skilled in the art to determine and may be made without departing from the spirit and scope disclosed herein.

[0090] To assist the Patent Office and any reader of any patent issuing based upon this application in interpreting the claims appended hereto, applicants note that unless “means” or “step” is expressly used in a particular claim, no invocation is intended to invoke 35 U.S.C. § 112(f), as it existed on the date of filing this application.

Claims

1. A method comprising: receiving output from a perception system of the autonomous vehicle; detecting one or more objects indicative of the presence of a restricted traffic zone based on the output from the perception system; as well as Determining that the autonomous driving vehicle is entering the restricted traffic area is based on at least one of a previous map, an obstacle map, and the one or more detected objects.

2. The method of claim 1, wherein the previous map comprises a grid map defining a plurality of drivable lanes, and wherein the obstacle map comprises a location of each of a plurality of obstacles in the restricted traffic area.

3. The method according to claim 1, further comprising: A drivable area through the restricted traffic area is generated based on at least one of the previous map, the obstacle map, and the one or more detected objects.

4. The method according to claim 3, further comprising: Determine whether the autonomous driving vehicle can drive through the restricted traffic area based on the drivable area.

5. The method according to claim 4, further comprising: In response to determining that the automated driving vehicle cannot travel through the restricted traffic area, causing the automated driving vehicle to perform a minimum risk condition maneuver.

6. The method of claim 5, wherein causing the autonomous driving vehicle to execute the minimum risk condition includes determining, based on the drivable area, whether the autonomous driving vehicle should stop in a current lane, stop in an emergency lane, or stop in a different portion of the drivable area.

7. The method according to claim 6, further comprising: connecting the one or more detected objects to form a curve, The generating of the drivable area includes using the curve as a boundary between the drivable area and the non-drivable area.

8. The method according to claim 7, further comprising: A lane map is generated based on the drivable area, wherein the lane map indicates one or more lanes in the drivable area, and the one or more lanes can be used to perform the minimum risk condition maneuver or to enable the autonomous driving vehicle to drive through the traffic restricted area.

9. The method according to claim 8, further comprising: determining that the curve is parallel to a lane from the previous map and within a predetermined distance of a boundary of the lane; as well as determining that the lane is closed based on determining that the curve is parallel to the lane and within the predetermined distance of the boundary of the lane, Wherein the lane map indicates the lane closure in response to determining that the lane is closed.

10. The method according to claim 8, further comprising: determining that the angle formed by the curve and the lane from the previous map is greater than a predetermined angle; as well as Based on determining that the angle formed by the curve and the lane is greater than the predetermined angle, determining that the lane is gradually closed, The lane map indicates that the lane is gradually closing in response to determining that the lane is gradually closing.

11. The method according to claim 8, further comprising: verifying whether the lane map satisfies one or more verification requirements for updating the previous map; as well as In response to determining that the lane map satisfies the one or more validation requirements, the lane map is provided to a previous map update device. 12 . The method of claim 11 , wherein the one or more verification requirements include one or more of the following between the lane map and the previous map: a continuity requirement, a connectivity requirement, and a smoothness requirement.

13. The method according to claim 8, further comprising: Determining that the autonomous driving vehicle is capable of traversing the drivable area; as well as The autonomous driving vehicle is guided through the drivable area based on the lane map.

14. The method according to claim 5, further comprising: Determining that the minimum width of the drivable area is less than a predetermined width; as well as In response to determining that the minimum width is less than the predetermined width, it is determined that the automated driving vehicle should perform the minimum risk condition maneuver.

15. The method according to claim 3, further comprising: receiving, from a planning module of the autonomous driving vehicle, a requirement for the autonomous driving vehicle to travel through the drivable area; as well as Based on the requirements, it is determined whether the autonomous driving vehicle can drive through the drivable area or whether a minimum risk condition maneuver should be performed.

16. The method of claim 15, wherein the requirement comprises one or more of: current traffic conditions, a current speed of the autonomous vehicle, and a current perception range of the perception system.

17. The method of claim 1, wherein the one or more objects detected include one or more of: cones, barricades, temporary barricades, obstacles, barrels, emergency lights, emergency signs, emergency vehicles, vehicles, pedestrians, first responders, and construction zone signs.

18. The method of claim 1, wherein the restricted traffic area comprises one or more of the following: an emergency area, a construction area, an accident site, a work area, and a traffic control area.

19. An apparatus comprising: at least one processor; as well as at least one memory including computer program code, the at least one memory and the computer program code being configured to, with the at least one processor, cause the apparatus to: receiving output from a perception system of the autonomous vehicle; detecting the presence of one or more emergency area warning devices based on said output from said sensing system; receiving a prior map indicating an environment in which the autonomous vehicle is capable of navigating; as well as A determination is made that the autonomous vehicle is entering an emergency area based on the detected presence of the one or more emergency area warning devices and the previous map.

20. A non-transitory computer readable medium storing computer program instructions that, when executed by at least one processor, cause the at least one processor to: receiving output from a perception system of the autonomous vehicle; detecting the presence of one or more emergency area warning devices based on said output from said sensing system; receiving a prior map indicating an environment in which the autonomous vehicle is capable of navigating; as well as A determination is made that the autonomous vehicle is entering an emergency area based on the detected presence of the one or more emergency area warning devices and the previous map.

Images