Modified trajectory planning for autonomous vehicle operation in heterogeneous vehicle environments

Abstract

To provide a method and a device for modifying a trajectory plan for autonomous vehicle operation in heterogeneous vehicle environment.SOLUTION: On the basis of vehicle sensor data, a vehicle travelling ahead is sensed, and a trajectory request for trajectory information of the vehicle can be transmitted. On the basis of a response by the vehicle to the trajectory request, a certainty estimate value for a trajectory of the vehicle is determined. With the certainty estimate value, a distance buffer proportional to the certainty estimate value for autonomous vehicle operation in relation to the vehicle is selected, and a trajectory plan of the autonomous vehicle operation is modified in order to produce a modified trajectory plan on the basis of the distance buffer for modifying the autonomous vehicle operation.SELECTED DRAWING: Figure 4

Description

[Technical field]

[0001] The subject matter described herein relates generally to an apparatus that performs trajectory planning for an autonomous vehicle, and more particularly to modifying trajectory plans for autonomous vehicle operation in a heterogeneous vehicle environment having a combination of vehicles with different levels of autonomous operation capabilities. [Background technology]

[0002] In general, autonomous vehicle operation may be based on various computing systems to assist in autonomous transportation of passengers from one location to another along a travel route. The vehicle environment of a travel route may be a homogenous vehicle environment where the vehicles generally have similar autonomous operation capabilities to facilitate communication and travel coordination among the vehicles. On the other hand, a travel route may be a heterogeneous environment where the vehicles generally are a mix of autonomous operation capabilities (which may include vehicles that do not have autonomous operation capabilities). This mix may reduce the efficiency of a highly autonomous (or fully autonomous) vehicle to travel a travel route in a timely manner without accidents due to the common need to constantly address and / or monitor the unpredictable actions of other vehicles. Therefore, an apparatus and method for improving the operation efficiency of a highly autonomous vehicle to travel a travel route in such a heterogeneous vehicle environment with vehicles that may lack systems and / or mechanisms for similar highly autonomous vehicle operation is desirable. Summary of the Invention [Problem to be solved by the invention]

[0003] An apparatus and method for modifying trajectory planning for autonomous vehicle operation in a heterogeneous vehicle environment that includes a mix of vehicles having lower levels of autonomous operation capability and / or no autonomous operation capability is disclosed. [Means for solving the problem]

[0004] In one implementation, a method is disclosed for autonomous vehicle operation in a heterogeneous vehicle environment. The method includes detecting a vehicle moving ahead based on vehicle sensor data and sending a trajectory request for trajectory information of the vehicle. A certainty estimate of the trajectory of the vehicle is determined based on a response by the vehicle to the trajectory request. The certainty estimate selects a distance buffer proportional to the certainty estimate of the autonomous vehicle operation relative to the vehicle, and modifies the trajectory plan of the autonomous vehicle operation to generate a modified trajectory plan based on the distance buffer to modify the autonomous vehicle operation.

[0005] In another implementation, a vehicle control unit for autonomous vehicle operation in a heterogeneous vehicle environment is disclosed. The vehicle control unit includes a wireless communication interface for servicing communication between a vehicle network and other vehicles, a processor communicatively coupled to the wireless communication interface and to a plurality of vehicle sensor devices, and a memory communicatively coupled to the wireless communication interface and storing a vehicle detection module and a trajectory plan modification module. The vehicle detection module includes instructions that, when executed by the processor, cause the processor to detect a vehicle moving ahead based on the vehicle sensor data and send a trajectory request for trajectory information of the vehicle. The trajectory plan modification module includes instructions that, when executed by the processor, cause the processor to determine a certainty estimate of the vehicle's trajectory based on the response, and select a proportional distance buffer for the autonomous vehicle operation relative to the vehicle according to the certainty estimate. The trajectory plan modification module includes instructions that, when executed by the processor, cause the processor to modify a trajectory plan of the autonomous vehicle operation to generate a modified trajectory plan based on the distance buffer.

[0006] In the description, reference will be made to the following accompanying drawings, in which like reference numerals refer to like parts throughout the several views, and in which: [Brief description of the drawings]

[0007] [Figure 1]1 is a schematic diagram of a vehicle including a vehicle control unit. [Diagram 2] FIG. 2 is a block diagram of the vehicle control unit of FIG. 1. [Diagram 3] 2 shows a functional module block diagram stored in the memory of the vehicle control unit of FIG. 1; [Figure 4] 1 illustrates an example operation for detecting a leading vehicle in a heterogeneous vehicle environment. [Diagram 5] 1 is an example process for autonomous operation in a heterogeneous vehicle environment. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0008] Described herein is modification of trajectory planning for an autonomous vehicle in a heterogeneous vehicle environment.

[0009] One example method includes detecting a vehicle traveling in front of a vehicle having autonomous vehicle operation based on vehicle sensor data. As can be appreciated, a heterogeneous vehicle environment can include vehicles capable of autonomous operation, such as autonomous vehicles, and in some examples, driver-assisted vehicles (i.e., those in which there may be some degree of autonomous operation with the assistance of a vehicle operator), and manually operated vehicles.

[0010] In this regard, the response can convey the vehicle's operational capabilities and associated operational certainty. For example, a trajectory planning response can convey that the vehicle is another vehicle with a full set of autonomous vehicle behaviors. A near real time trajectory response can indicate a driver-assisted vehicle with a partial set of autonomous vehicle behaviors. The near real time trajectory response includes near real time operational parameters of the vehicle, such as at least one of vehicle heading data, vehicle speed data, vehicle lane control data, vehicle steering angle data, and vehicle braking data. A null response may be a deterministic and / or estimated "none" response and can indicate a manually operated vehicle.

[0011] Based on the vehicle's response to the trajectory request, the autonomous vehicle can determine a certainty estimate of the vehicle's trajectory and can select a proportional distance buffer for autonomous vehicle operation relative to a preceding vehicle, which can modify the trajectory plan to generate a modified trajectory plan based on the distance buffer.

[0012] In this regard, autonomous vehicle operation may execute a modified trajectory plan based on the presence of a vehicle that is not operating autonomously.

[0013] 1 is a schematic diagram of a vehicle 100 that includes a vehicle control unit 110. A plurality of object sensor devices 102-1, 102-2, 102-3, 102-4, 102-5, and 102-6 (collectively, object sensor devices 102) are in communication with the control unit 110 to access a heterogeneous vehicle environment 116. As can also be appreciated, the vehicle 100 may be a car, a light truck, a freight transporter, or any other passenger or non-passenger vehicle.

[0014] During autonomous operation, the vehicle control unit may operate to define distance buffer 140. The distance buffer may be defined dimensionally relative to the autonomous vehicle 100. For example, the outer boundary defined by the vehicle control unit may be symmetrical or asymmetrical, as well as include a buffer area that varies around the vehicle 100 based on conditions in the heterogeneous vehicle environment 116.

[0015] For example, automobiles, along with other automobiles, generally operate and / or navigate in a two-dimensional space. Aircraft, along with other aircraft, generally operate and / or navigate in a three-dimensional space. In the example of FIG. 1 , distance buffer 140 may accommodate a two-dimensional space relative to autonomous vehicle 100, both laterally (i.e., across autonomous vehicle 100) and longitudinally (i.e., along the length of autonomous vehicle 100). Distance buffer 140 may include distance buffer starboard component 142 and distance buffer port component 144, which relate to the lateral space of autonomous vehicle 100. Distance buffer 140 may also include distance buffer rear component 146 and distance buffer front component 148, which relate to the longitudinal space of autonomous vehicle 100. Distance buffer 140 may be defined based on a Cartesian coordinate system and / or based on a polar coordinate system.

[0016] As can be appreciated, vehicle control unit 110 can define zones of distance buffer 140. Such zones can be ranked, for example, by the importance that another vehicle may have to the trajectory path 134 of the autonomous vehicle. In general, other vehicles ahead of vehicle 100 can have a greater influence on trajectory path 134 than those behind vehicle 100. Thus, in this regard, zones of distance buffer 140 relating to distance buffer ahead component 148 can provide a larger buffer value compared to those of distance buffer behind component 146.

[0017] In operation, vehicle control unit 110 may base components 142, 144, 146, and 148 of distance buffer 140 on certainty estimates of trajectories of other vehicles that may affect trajectory plan 134. For example, the trajectory of a manually operated vehicle may generally be unpredictable by vehicle control unit 110 since the observed vehicle behavior may be based on object sensor devices 102, while the trajectory of another autonomous vehicle may generally be predictable by vehicle control unit 110 since the autonomous vehicle behavior may be based on object sensor devices 102 and inter-vehicle communication data (e.g., trajectory plans, speed, orientation, etc. of other autonomous vehicles).

[0018] Based on the confidence estimate of the leading vehicle's trajectory, the vehicle control unit may select a distance buffer 140 proportional to the confidence estimate of the autonomous vehicle 100's operation in relation to another vehicle. The vehicle control unit 110 may modify the trajectory plan 134 of the autonomous vehicle 100 to generate a modified trajectory plan based on the distance buffer 140, which may then operate to modify the operation of the autonomous vehicle 100, as described in more detail with reference to Figures 2-5.

[0019] The object sensor units 102 may be positioned on the exterior surface of the vehicle 100, or may be positioned in a concealed manner relative to the vehicle for aesthetic purposes. Additionally, the sensors may operate at frequencies where the vehicle body or portions thereof appear transparent to the individual sensor units.

[0020] Communications between the sensor devices and vehicle control units, including the vehicle control unit 110, may be bus-based and may also be used or operated by other systems of the vehicle 100. For example, the object sensor devices 102 may be coupled by a combination of network architectures, such as a Body Electronic Area Network (BEAN), a Controller Area Network (CAN) bus configuration, an Audio Visual Communication-Local Area Network (AVC-LAN) configuration, and / or other combinations of additional communication-system architectures to provide communications between devices and systems of the vehicle 100.

[0021] The output of the example object sensor device 102 may be used by the vehicle control unit 110 to detect vehicle lane boundaries on a road or objects within the vehicle route 134, to capture live video of the vehicle environment 116, to determine vehicle speed, etc.

[0022] The object sensor device 102, as will be described in more detail with reference to Figures 1-5, can operate to provide tactile or relational changes in the heterogeneous vehicle environment 116, such as, by way of example, approaching pedestrians, bicyclists, objects, vehicles, road debris, and other such vehicle obstacles (or potential vehicle obstacles), such as leading vehicles and / or vehicles ahead of the vehicle 100.

[0023] The object sensor device 102 may be provided by a Light Detection And Ranging (LIDAR) system, where the object sensor device 102 is capable of capturing data relating to laser light reflections from physical objects within the environment of the vehicle 100. The object sensor device 102 may also include a combination of laser (LIDAR) and millimeter wave radar devices. LIDAR and radar based devices are operable to detect objects along with their velocity (i.e., relative and / or absolute speed).

[0024] The object sensor devices 102, alone or in combination, can operate to capture depth images or otherwise generate depth information for a captured image. For example, the object sensor devices 102 may be configured to capture images (such as in visual and non-visual spectral wavelengths, audible and non-audible wavelengths, etc.).

[0025] In this regard, the object sensor devices 102 are operable to determine distance vector measurements of objects in the heterogeneous vehicle environment 116. For example, each of the object sensor devices 102 may be configured to detect and / or analyze structured light, time of flight (e.g., of signals for Doppler sensing), Light Detection and Ranging (LIDAR), light fields, and other information to determine depth / distance, direction, and / or velocity of objects.

[0026] In operation, multiple object sensor devices 102-1-102-6 may be used in combination or independently to evaluate heterogeneous vehicle environment 116 longitudinally and / or laterally relative to vehicle 100 to detect objects such as terrain, foliage, pedestrians, etc. In one example, object sensor devices 102-1, 102-2, and 102-3 may operate longitudinally to detect objects in front of vehicle 100, such as may be used to evaluate distance buffer front component 148, and object sensor devices 102-4, 102-5, and 102-6 may operate longitudinally to detect objects behind vehicle 100, such as may be used to evaluate distance buffer rear component 146.

[0027] As a further example, object sensor units 102-3 and 102-6 may operate laterally to detect objects to the right of vehicle 100, such as may be used to evaluate distance buffer rightward component 142, and object sensor units 102-2 and 102-4 may operate laterally to detect objects to the left of vehicle 100, such as may be used to evaluate distance buffer leftward component 144.

[0028] Each of the object sensor devices 102 may implement an individual function to provide a collective object parameter detection based on the individual spatial portions 118. For example, the object sensor device 102-1 may operate to detect an object, and the object sensor device 102-2 may operate to detect an object velocity relative to the vehicle 100. The object sensor device 102-3 may also operate in combination with the object sensor device 102-1 to detect and / or confirm the detection of an object. As can be appreciated, the velocity of an object may be determined via the object sensor devices and extrapolated by successive position detection measurements of the object and calculating the speed and / or direction of movement (e.g., for forming a motion vector) based thereon.

[0029] As can be appreciated, individual sets of object sensor devices may operate in combination with other sets of object sensor devices, and may operate alternately or asynchronously, simultaneously, or individually, based on the desired sensing functionality and / or application.

[0030] In operation, the vehicle control unit 110 is operable to receive vehicle sensor data output by the object sensor device 102, which may provide a LIDAR system. The object sensor device detects a velocity V along a trajectory plan 134. 100 While moving in the vehicle 100, vehicle sensor data related to laser reflections from physical objects in the area surrounding the vehicle 100. By way of example, the signal reflections may include vehicle route markings, such as centerlines, lane lines and edges of the vehicle route 134, traffic signs, etc.

[0031] As can be appreciated, with this vehicle sensor data, vehicle control unit 110 can be operative to detect a vehicle moving ahead and transmit a trajectory request for vehicle trajectory information via antenna 112. Based on a response by the vehicle to the trajectory request, vehicle control unit 110 can determine a confidence estimate of the leading vehicle's trajectory and can select a distance buffer 140 proportional to the confidence estimate of the autonomous vehicle operation relative to the vehicle. Vehicle control unit 110 can modify trajectory plan 134 of the autonomous vehicle operation to generate a modified trajectory plan based on distance buffer 140 to modify the autonomous vehicle operation, as described in more detail with reference to Figures 2-5.

[0032] 2 is a block diagram of a vehicle control unit 110 including a wireless communication interface 202, a processor 204, and a memory 206 communicatively coupled via a bus 208. The vehicle control unit 110 can provide an example platform for the apparatus and methods detailed with reference to FIGS.

[0033] The processor 204 may be a conventional central processing unit or any other type of device or devices capable of manipulating or processing information. As can be appreciated, the processor 204 may be a single processing device or multiple processing devices. Such processing devices may be microprocessors, microcontrollers, digital signal processors, microcomputers, central processing units, field programmable gate arrays, programmable logic devices, state machines, logic circuits, analog circuits, digital circuits, and / or any device that manipulates signals (analog and / or digital) based on hard-coding of circuit and / or operational instructions.

[0034] Memory (and / or memory elements) 206 may be communicatively coupled to the processor 204 and may be operable to store one or more modules described herein. A module may contain instructions that, when executed, cause the processor 204 to implement one or more of the various processes and / or operations described herein.

[0035] The memory and / or memory elements 206 may be a single memory device, multiple memory devices, and / or embedded circuitry of the processor 204. Such memory devices may be read-only memory, random access memory, volatile memory, non-volatile memory, static memory, dynamic memory, flash memory, cache memory, and / or any device that stores digital information. Furthermore, the configurations described herein may have the form of a computer program product embodied in one or more computer readable mediums having computer readable program code embodied thereon, for example, stored thereon. Any combination of one or more computer readable media may be utilized. The computer readable medium may be a computer readable signal medium or a computer readable storage medium.

[0036] The phrase "computer-readable storage medium" means a non-transitory storage medium. A computer-readable storage medium may be, for example, without limitation, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination thereof. In the context of this specification, a computer-readable storage medium may be any tangible medium that can contain or store a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer-readable medium may be transmitted using any suitable medium, including, without limitation, wireless, wired, fiber optic, cable, RF, or the like, or any suitable combination thereof.

[0037] Memory 206 is capable of storing machine-readable instructions, or instructions, such that the machine-readable instructions may be accessed by processor 204. Machine-readable instructions may comprise, for example, logic or algorithms written in a programming language or generation thereof (e.g., 1GL, 2GL, 3GL, 4GL, or 5GL), such as a machine language that may be directly executed by processor 204, or an assembly language that may be compiled or assembled into machine-readable instructions and stored on memory 206, Object-Oriented Programming (OOP) such as JAVA, Smalltalk, C++, or the like, traditional procedural programming languages, scripting languages, microcode, etc. Thus, machine-readable instructions may be written in a Hardware Description Language (HDL), such as logic implemented via a Field-Programmable Gate Array (FPGA) configuration or an Application-Specific Integrated Circuit (ASIC) or other equivalent. Thus, the methods and apparatus described herein can be implemented in any conventional computer programming language, as pre-programmed hardware elements, or as a combination of hardware and software components.

[0038] It should be noted that when processor 204 includes multiple processing units, the processing units may be centrally located (e.g., directly coupled to one another via a wired and / or wireless bus structure) or distributed (e.g., cloud computing via indirect coupling via a local and / or world area network). It should be further noted that when processor 204 implements one or more of its functions via state machines, analog circuits, digital circuits, and / or logic circuits, the memory and / or memory elements that store the corresponding operating instructions may be incorporated within or external to the circuits that include the state machines, analog circuits, digital circuits, and / or logic circuits.

[0039] It is further noted that memory 206 stores, and processor 204 executes, hard-coded and / or operational instructions of modules corresponding to at least some of the steps and / or functions illustrated in FIGS. 1-5.

[0040] The vehicle control unit 110 may include one or more modules, at least some of which are described herein. The modules may be implemented as computer readable program code that, when executed by the processor 204, implements one or more of the various processes described herein. One or more of the modules may be components of the processor 204, or one or more of the modules may be executed on and / or distributed among other processing systems to which the processor 204 is operatively coupled. The modules may include instructions (e.g., program logic) that are executable by one or more processors 204.

[0041] The wireless communication interface 202 generally controls and manages data received via the vehicle network 212. There is no limitation to the present disclosure operating on any particular hardware configuration, and thus essential features herein may be substituted, removed, added, or otherwise modified for improved hardware and / or firmware configurations as the disclosure develops.

[0042] The antenna 112 may include one or more conductive elements that communicate with electromagnetic signals transmitted by Global Positioning System satellites. The received signals may be converted into data signals that inform the location (e.g., latitude and longitude position) and further inform the positioning of the vehicle in relation to road data.

[0043] Vehicle control unit 110 may be communicatively coupled to receive signals from Global Positioning System satellites, such as via antenna 112 of vehicle control unit 110 or other such vehicle antennas (not shown). Antenna 220 operates to provide communications with vehicle control unit 200 through vehicle-to-vehicle (V2V) communications 240, through vehicle-to-infrastructure (V2I) communications 250, and through wireless communications 230.

[0044] In vehicle-to-vehicle (V2V) communication 240, the vehicle 100 may send messages to another vehicle, and the other vehicle may send messages to the vehicle 100 through dedicated short-range wireless communication to exchange messages. In the example provided by FIG. 2, the vehicle-to-vehicle communication 240 provides vehicle maneuver information such as lane changes (e.g., traffic lane change command 240), speed increases, sudden stops, excessive deceleration due to congestion caused by heavy traffic, traffic signals, accidents, etc., along with responses 244 based on trajectory requests 242. Furthermore, the vehicle-to-vehicle communication 240 can also have the form of chain messages transmitted wirelessly by other vehicles. In effect, the vehicle control unit 110 can receive advance notice or notification of changes in traffic congestion upon approach. Chain messages can be used in increasing "string stability" in a group of vehicles capable of vehicle-to-vehicle communication, as will be described in more detail with reference to FIGS. 3-5.

[0045] Vehicle-to-infrastructure communications 250 can operate to broadcast traffic stops, such as traffic lights or traffic signs, and provide advance notification to vehicle control unit 110 of the likelihood of an upcoming traffic congestion, as well as to beacons and / or vehicle-to-infrastructure devices that are operable to collect local traffic information and local traffic congestion and broadcast the collected data. V2I communications 250 can include data related to traffic congestion levels, such as red light violation warning data, curve speed warning data, stop sign gap assistance data, slow down zone warning data, stop sign violation warning data, and railroad crossing violation warning data.

[0046] During operation, the vehicle control unit 110 may receive map layer data 252 in response to a map layer data request 250 via the antenna 112. The map layer data 252 may be provided by a third party server operated by an organization that provides applications, such as mapping applications, and map application layer data, including road information data, traffic layer data, geolocation layer data, etc.

[0047] The map layer data 252 may include road data that may be utilized by the vehicle control unit 110 along with the trajectory plan 134 (FIG. 1). For example, the map layer data 252 may be provided in a Route Network Description File (RNDF) format. The route network description file, for example, defines accessible road segments and provides information such as waypoints, stop sign locations, lane widths, checkpoint locations, parking locations, etc. A route network does not have an implicit start and end point. A server, such as the server 233, may also provide data as a Mission Description File (MDF) for autonomous and / or driver-assisted vehicle operation. The Mission Description File (MDF) may operate to define checkpoints to reach a mission, such as along the travel route 134 (FIG. 1).

[0048] The wireless communication 226 may be based on one or more wireless communication system specifications. For example, the wireless communication system may operate according to one or more standard specifications including, without limitation, 3rd Generation Partnership Project (3GPP), 4th Generation Partnership Project (4GPP), 5th Generation Partnership Project (5GPP), Long Term Evolution (LTE), LTE Advanced, RFID, IEEE 802.11, Bluetooth, Advanced Mobile Phone Services (AMPS), digital AMPS, Global System for Mobile communications (GSM), Code Division Multiple Access (CDMA), Local Multi-point Distribution Systems (LMDS), Multi-Channel-Multi-point Distribution Systems (MMDS), IrDA, Wireless USB, Z-Wave, ZigBee, and / or variations thereof.

[0049] As may be appreciated, the communication paths 213 of the vehicle network 212 may be formed from any suitable medium for transmitting signals, such as, for example, conductive wires, conductive traces, optical waveguides, or the like. Additionally, the communication paths 213 may be formed from any combination of media capable of transmitting signals. In one embodiment, the communication paths 213 may include any combination of conductive traces, conductive wires, connectors, and buses that cooperate to allow transmission of electrical data signals to components such as processors, memories, sensors, input devices, output devices, and communication devices.

[0050] Thus, the communication paths 213 may be provided by a vehicle bus, or combinations thereof, such as, for example, a Body Electronics Area Network (BEAN), a Controller Area Network (CAN) bus configuration, an Audio Visual Communication-Local Area Network (AVC-LAN) configuration, a Local Interconnect Network (LIN) configuration, a Vehicle Area Network (VAN) bus, and / or other combinations of additional communication-system architectures for providing communication between devices and systems of the vehicle 100.

[0051] The term "signal" may be understood to refer to a waveform (e.g., electrical, optical, magnetic, mechanical, or electromagnetic), such as DC, AC, sine wave, triangular wave, square wave, vibration, and the like, that has the ability to travel through at least some of the media described herein.

[0052] The individual modules and associated instructions of the vehicle control unit 110, when executed by the processor 204, cause the processor 204 to receive vehicle data and select an appearance mode from a plurality of appearance modes based on the vehicle sensor data relative to a threshold value, and when executed by the processor 204, the associated instructions cause the processor to apply the appearance mode to the virtual lane marker data to generate enhanced virtual lane marker data, and transmit the enhanced virtual lane marker data for display by the vehicle graphical user interface via the wireless communication interface 202, as will be described in more detail with reference to Figures 3-5.

[0053] FIG. 3 shows a functional module block diagram stored within memory 206 (FIG. 2) of vehicle control unit 110, where memory 206 stores a vehicle detection module 308 and a trajectory modification module 312.

[0054] The vehicle detection module 308 includes instructions that, when executed by the processor, cause the processor to detect a vehicle moving in front of the autonomous vehicle based on vehicle sensor data and send a trajectory request for vehicle trajectory information.

[0055] The vehicle control unit 110 (FIGS. 1 and 2) can receive the responses 244. As can be appreciated, the responses 244 can include a trajectory planning response related to the first certainty estimate, a near real time trajectory response related to the second certainty estimate, and a null response related to the third certainty estimate.

[0056] Null responses may include a NACK response and a "none" response, in which a vehicle ahead of the autonomous vehicle sending the trajectory request has indicated a rejection of the request message (e.g., an incompatible communication protocol) and / or some type of error (e.g., data in the request is corrupted or intended for a different destination vehicle).

[0057] The "none" response informs the autonomous vehicle that it did not receive any response from the vehicle ahead. Optionally, the trajectory request may be resent, or the vehicle ahead of the autonomous vehicle may instead be evaluated as a manually operated vehicle with a low confidence level.

[0058] The vehicle detection module 308 may provide the response 244 to the trajectory plan modification module 312 .

[0059] The trajectory planning modification module 312 includes instructions that, when executed by the processor, cause the processor to determine a certainty estimate of the vehicle's trajectory. As can be appreciated, the response 244 can include, for example, a trajectory planning response related to the first certainty estimate, a near real-time trajectory response related to the second certainty estimate, and a null response related to the third certainty estimate. The near real-time trajectory response can include near real-time operating parameters of the vehicle, such as at least one of vehicle heading data, vehicle speed data, vehicle lane control data, vehicle steering angle data, and vehicle braking data.

[0060] The first, second and third example confidence estimates may be understood to be coarse confidence estimates, i.e., baseline confidence estimates may be associated with different responses. As may be appreciated, the first, second and third confidence estimates may be based on numerical values, e.g., confidence values ​​of 100%, 50%, 0%, etc., may be based on fuzzy logic values, e.g., high confidence, low confidence, low confidence, etc., and may be based on other suitable forms of logic implementation.

[0061] The fine certainty estimate may be based on operational data that may be included not only in response to a trajectory request, but also in observations via vehicle sensors of a vehicle ahead of the autonomous vehicle. For example, the response 244 may include speed data, steering data, braking data, etc., based on which a fine certainty estimate may be generated in combination with a coarse certainty estimate.

[0062] From the autonomous vehicle's point of reference, other autonomous vehicles may have a high level of operational certainty. In contrast, manually operated vehicles may have a low level of operational certainty, and driver-assisted operated vehicles may have varying levels of certainty between autonomous and manual operation due to the varying levels of information that may be communicated to the autonomous vehicle.

[0063] A manually operated vehicle may have a relatively large certainty estimate (or large uncertainty value) because future driving actions may not be easily recognized and / or may not be recognized due to human characteristics. Human characteristics may further affect the uncertainty due to the operator's driving experience, medical condition, attention state, etc. Furthermore, real-time or near real-time observations of a manually operated vehicle cannot necessarily be translated into a predictable trajectory plan from the perspective of an autonomous vehicle.

[0064] The vehicle may also be a driver assisted operation vehicle that may include various second certainty estimates. In this regard, the vehicle operator may primarily control the vehicle operation, but the vehicle may include vehicle-to-vehicle communication capabilities to communicate trajectory planning and even operation metric data (such as speed data, steering data, braking data, etc.) as well as vehicle operator data related to an identified vehicle operator profile. For example, the vehicle operator may be identified by biometric data, an electronic signature device on the operator's persona, etc., and matched to a vehicle profile. The vehicle operator profile, when present, may be communicated along with other operation data.

[0065] Based on the certainty estimate 314, the trajectory plan modification module 312 may select a distance buffer 140 for the autonomous vehicle in relation to the vehicle based on the certainty estimate 314. Based on the distance buffer 140, the trajectory plan modification module 312 may modify the trajectory plan 134 of the autonomous vehicle 100 ( FIG. 1 ) based on the distance buffer 140 to generate a modified trajectory plan 218.

[0066] As can be appreciated, in a heterogeneous vehicle environment, a vehicle in front of an autonomous vehicle may not be an autonomous vehicle and therefore may involve a certain level of certainty about the vehicle's behavior (such as perceived erratic driving, inability to maintain speed, frequent braking, etc.).

[0067] When the response 244 may be a null response, the trajectory plan modification module 312 may operate to improve the certainty estimate 314 based on observed operating characteristics of the preceding vehicle (or vehicle ahead). The trajectory plan modification module 312 may accordingly estimate a trajectory plan for the vehicle based on speed, wheel direction, ability to stay within a traffic lane, etc., and from this may generate a short-term estimated trajectory plan for a given ahead observation period. Based on the estimated trajectory plan with the associated estimated certainty estimate, a certainty estimate 314 of the estimated trajectory plan and response 244 may be generated. The trajectory plan modification module 312 may select a distance buffer 140 for the autonomous vehicle relative to the preceding vehicle.

[0068] The distance buffer 140 generally relates to the remaining distance to the vehicle ahead. If the certainty estimate is favorable, autonomous and driver-assisted operation may be implemented, such as for Adaptive Cruise Control (ACC), and improved traffic flow may be provided by utilizing a Cooperative Adaptive Cruise Control (CACC) system. As may be appreciated, CACC systems may be provided not only via automated vehicle devices, but also by driver-assisted vehicle devices. Moreover, such capabilities may be provided in other autonomous systems to accommodate manually operated vehicles and to evaluate autonomous system decisions based on certainty estimates of other vehicles while operating in a heterogeneous vehicle environment.

[0069] Adaptive cruise control systems are evaluated based on "string stability": oscillations can be introduced into the traffic flow by braking and accelerating vehicles and can then be amplified in the upstream direction.

[0070] Adaptive cruise control may also be referred to as active cruise control, autonomous cruise control, intelligent cruise control, radar cruise control, etc. In adaptive cruise control, distance may be measured by vehicle object sensors 102 (FIG. 1).

[0071] In operation, a vehicle operator (such as in a driver assistance operation) may activate the adaptive cruise control and set a desired speed. The vehicle operator can then select a desired distance buffer to the vehicle ahead (such as by pressing a button to cycle between short, medium, and long remaining distances).

[0072] In general, adaptive cruise control systems do not have a high degree of "string stability." A human driver may have reduced string stability because human reaction times and actions, such as moving a foot from the throttle to the brake pedal, are delayed. Adaptive cruise control systems may improve this delay, but a phase delay occurs due to the estimation algorithms required to convert discrete distance measurements (provided by vehicle object sensor devices, such as those based on radar and / or LIDAR) into a metric of range change over time (i.e., acceleration and deceleration of the leading vehicle).

[0073] In contrast, a Collaborative Adaptive Cruise Control (CACC) system improves string stability by including wireless inter-vehicle communication and associated control logic to improve the vehicle-following capabilities of the Adaptive Cruise Control (ACC) system. String stability can be improved by reducing the delay in vehicle response to the leading vehicle.

[0074] Vehicle-to-vehicle communication operating in a CACC system (such as in autonomous or more advanced driver-assisted operation) provides information not only about the vehicle directly ahead of the vehicle, but also about one or more preceding vehicles ahead, i.e., it conveys key parameters of the vehicles in the "string", such as position, speed, acceleration, etc.

[0075] As can be appreciated, distance buffer 140 may be an agreed upon value between vehicles and associated logic having vehicle-to-vehicle communication. With the introduction of manually operated vehicles or driver assisted vehicles with limited functionality, vehicle 100 may be operable to adjust distance buffer 140 based on confidence estimate 314.

[0076] The modified trajectory plan 218 may be provided to a transmission module 318 for formatting for delivery via wireless communication interface 202, via wireless communications 240 and / or 250, to vehicle network 212, and / or to other vehicles.

[0077] 4 illustrates an example operation for detecting a leading vehicle 402 having object sensor device 102-1 in a heterogeneous vehicle environment. For purposes of clarity, the example relates to distance buffer forward component 148 of distance buffer 140, but it should be understood that vehicle control unit 110 (FIG. 1) may similarly operate to select distance buffer rear, leftward, and rightward components of distance buffer 140 (FIG. 1).

[0078] Vehicle 100, via vehicle control unit 110 (FIG. 1), is operative to detect a leading vehicle 402 based on vehicle sensor data 216 based on object points 406 of vehicle 402. Autonomous vehicle 100 may be operative to transmit a trajectory request 242 for trajectory information of vehicle 402. Vehicle 100 may receive a response to trajectory request 242.

[0079] The vehicle control unit 110 (FIG. 1) of the vehicle 100 can be operative to determine a certainty estimate of the trajectory of the vehicle 402 and to select a distance buffer forward component 148 of the distance buffer 140 associated with the vehicle 402 based on the certainty estimate. As can be appreciated, the object sensor device 102-1 can be operative to detect the vehicle 402 associated with a distance 420. The distance 420 is generally related to the sensor range of the object sensor device 102-1. The other distance buffer components 142, 144, and 146 (FIG. 1) of the distance buffer 140 can be selected as associated with vehicles that may be positioned laterally, longitudinally, or combinations thereof relative to the autonomous vehicle 100.

[0080] As can be appreciated, the distance buffer front component 148 of the distance buffer 140 can increase with decreasing certainty estimates and can decrease with increasing certainty estimates. Thus, by accommodating vehicles in various operating states (such as autonomous, driver assisted, manually operated, etc.), more efficient traffic flow can be realized in a heterogeneous vehicle environment.

[0081] As can be appreciated, in the example environment of a Cooperative Adaptive Cruise Control (CACC) system, a certainty estimate relating to the vehicle can be communicated to following vehicles via V2V wireless communication 240. Vehicles following vehicle 100 can select individual distance buffers in the string to correspond to a "certainty" estimate in relation to the operation of vehicle 402. Also, as can be appreciated, different autonomous and / or driver-assisted vehicle models can have different operating characteristics. For example, different manufacturers can each have different acceleration characteristics, braking characteristics, responsiveness, etc., which can be further considered in the selection of distance buffer 140.

[0082] 5 is an example process 500 for autonomous operation in a heterogeneous vehicle environment. In operation 502, the vehicle control unit can detect a vehicle moving ahead of the vehicle based on vehicle sensor data. When a preceding or forward vehicle is detected in operation 504, a trajectory request can be sent in operation 506. The trajectory request relates to the vehicle's trajectory information. The trajectory information can include trajectory planning at various predefined markers located on GPS and / or mapping technology, vehicle speed, vehicle make, model, firmware installation, destination points, and trajectory point actions (such as slow down, stop, turn, accelerate, etc.).

[0083] Based on the response to the trajectory request in operation 508, a certainty estimate of the vehicle's trajectory is determined in operation 510. As can be appreciated, the response can include (a) a trajectory planning response related to the first certainty estimate, (b) a near real-time trajectory response related to the second certainty estimate, and (c) a null response related to the third certainty estimate. The near real-time trajectory response includes near real-time operating parameters of the vehicle, such as at least one of vehicle heading data, vehicle speed data, vehicle lane control data, vehicle steering angle data, and vehicle braking data.

[0084] A null response may include a NACK response or a "none" response, in which case the vehicle ahead of the autonomous vehicle sending the trajectory request has indicated a rejection of the request message (e.g., incompatible communication protocol) and / or some type of error (e.g., data in the request is corrupted or intended for a different destination vehicle).

[0085] The "none" response informs the autonomous vehicle that it did not get any response from the vehicle ahead. Optionally, the trajectory request may be resent, or alternatively, the vehicle ahead of the autonomous vehicle may be evaluated as a manually operated vehicle with a low confidence level.

[0086] The first, second and third example confidence estimates may be understood to be coarse confidence estimates, i.e., baseline confidence estimates may be associated with different responses. As may be appreciated, the first, second and third confidence estimates may be based on numerical values, e.g., confidence values ​​of 100%, 50%, 0%, may be based on fuzzy logic values, e.g., high confidence action, low confidence action, low confidence action, etc., and may be based on other suitable forms of logic implementation.

[0087] The fine certainty estimate may be based on operational data that may be included with the response to the trajectory request, as well as with observations via vehicle sensors of a vehicle ahead of the autonomous vehicle. For example, the response 244 may include speed data, steering data, braking data, vehicle model data, communication protocol data, etc., based on which a fine certainty value may be generated in combination with the coarse certainty estimate.

[0088] From the autonomous vehicle's point of reference, other autonomous vehicles may have a high level of operational certainty. In contrast, manually operated vehicles may have a low level of operational certainty, and driver-assisted operated vehicles may have varying levels of certainty between autonomous and manual operation due to the varying levels of information that may be communicated to the autonomous vehicles.

[0089] A manually operated vehicle may have a relatively large certainty estimate (or large uncertainty value) because future driving actions may not be easily recognized and / or may not be recognized due to human characteristics. Human characteristics may further affect the uncertainty due to the operator's driving experience, medical condition, attention state, etc. Furthermore, real-time or near real-time observations of a manually operated vehicle cannot necessarily be translated into a predictable trajectory plan in the context of an autonomous vehicle.

[0090] The vehicle may also be a driver assisted operating vehicle that may include various second certainty estimates. In this regard, the vehicle operator may primarily control the vehicle operation, but the vehicle may also include vehicle-to-vehicle communication capabilities to communicate trajectory planning and even operation metric data (such as speed data, steering data, braking data, etc.) as well as vehicle operator data related to an identified vehicle operator profile. For example, the vehicle operator may be identified by biometric data, an electronic signature device on the operator person, etc., and matched to a vehicle profile. The vehicle operator profile, if present, may be communicated along with other operation data.

[0091] The confidence estimate of operation 510 may select a distance buffer for the autonomous vehicle operation relative to the vehicle in operation 512, and may modify the trajectory plan of the autonomous vehicle operation to generate a modified trajectory plan based on the distance buffer in operation 514. As can be appreciated, modifications to the trajectory plan may be rippled to adjust times for changing lanes, engaging in vehicle maneuvers, arrival times at a destination, etc.

[0092] In operation 516, the modified trajectory plan may be broadcast to other vehicles. In this regard, vehicle-to-vehicle communication, vehicle-to-infrastructure communication, or the like may be used to communicate the modified trajectory plan to other vehicles. In this regard, other vehicles capable of autonomous vehicle operation may adjust their individual trajectory plans accordingly, as well as further adjust distance buffers between them and other vehicles in the heterogeneous vehicle environment.

[0093] Although specific combinations of various functions and features of the invention have been explicitly described herein, other combinations of these features and functions are possible and are expressly encompassed within the scope of the invention, without being limited by the specific examples disclosed herein.

[0094] Those skilled in the art will appreciate that the terms "substantially" or "approximately" as used herein provide an industry-accepted range of tolerance to the relativity between their corresponding terms and / or items.

[0095] Those skilled in the art will further appreciate that the term "coupled" as used herein includes direct coupling and indirect coupling via another component, element, circuit, or module, where in the case of indirect coupling, the intervening component, element, circuit, or module does not change the information of the signal, but may adjust its current level, voltage level, and / or power level. Those skilled in the art will also appreciate that inferred coupling (i.e., where one element is inferredly coupled to another element) includes direct and indirect coupling between two elements in the same manner as "coupled."

[0096] As the term "module" is used in the description of the figures, a module includes functional blocks implemented in hardware, software, and / or firmware that perform one or more functions, such as processing an input signal to generate an output signal. A module as used herein may include sub-modules that are themselves modules.

[0097] Thus, described herein are several embodiments, including preferred embodiments, as well as apparatus and methods, for implementing autonomous vehicle operation in a heterogeneous vehicle environment.

[0098] The above description relates to what are presently considered to be the most practical embodiments, however, it is understood that the disclosure is not limited to these embodiments, but on the contrary is intended to cover various modifications and equivalent arrangements included within the spirit and scope of the appended claims, which should be accorded the broadest interpretation so as to encompass all such modifications and equivalent structures as are permitted under law. The invention disclosed in this specification includes the following aspects. [Aspect 1] 1. A method for autonomous vehicle operation in a heterogeneous vehicle environment, comprising: Detecting a vehicle moving ahead based on vehicle sensor data; Transmitting a trajectory request for trajectory information of the vehicle; Based on a response by the vehicle to the trajectory request, determining a confidence estimate of the vehicle's trajectory; selecting a distance buffer proportional to the certainty estimate of the autonomous vehicle operation relative to the vehicle; modifying a trajectory plan of the autonomous vehicle movement to generate a modified trajectory plan based on the distance buffer to modify the autonomous vehicle movement; The method according to claim 1, [Aspect 2] 2. The method of embodiment 1, further comprising broadcasting the modified trajectory plan. [Aspect 3] The response to the trajectory request may include: a trajectory planning response including a predicted trajectory for a given forward look-ahead period; a near real-time trajectory response including near real-time operating parameters of the vehicle; A null response; The method of embodiment 1, comprising at least one of: Aspect 4 the trajectory planning response is related to a first certainty estimate; the near real-time trajectory response is related to a second certainty estimate; 4. The method of embodiment 3, wherein the null response is related to a third certainty estimate. Aspect 5 The near real-time operating parameters of the vehicle include: Vehicle orientation data; Vehicle speed data; Vehicle lane control data; Vehicle steering angle data; Vehicle braking data; and The method of embodiment 3, comprising at least one of: Aspect 6 estimating a trajectory plan for the vehicle based on the responses and generating therefrom an estimated trajectory plan for a given forward look period, the estimated trajectory plan being related to a fine certainty estimate; determining the confidence estimate of the estimated trajectory plan for the vehicle based on a coarse confidence estimate and a fine confidence estimate associated with the responses; selecting the distance buffer for the autonomous vehicle relative to the vehicle; 2. The method of embodiment 1, comprising the steps of: Aspect 7 When the response corresponding to the trajectory request includes a null response, the certainty estimate of the trajectory of the vehicle is: estimating a trajectory stability state of the vehicle based on the vehicle sensor data; determining the confidence estimate of the trajectory of the vehicle from the trajectory stability state; selecting the distance buffer for the autonomous vehicle relative to the vehicle; The method of embodiment 1, wherein the determination is made by Aspect 8 1. A method for autonomous vehicle operation in a heterogeneous vehicle environment, comprising: Detecting a vehicle moving ahead of a lead vehicle based on vehicle sensor data of the lead vehicle among a plurality of vehicles under the autonomous vehicle operation; Transmitting a trajectory request for trajectory information of the vehicle; Based on a response by the vehicle to the trajectory request, determining a confidence estimate of the vehicle's trajectory; selecting a distance buffer for the autonomous vehicle relative to the vehicle; modifying a trajectory plan of the autonomous vehicle to generate a modified trajectory plan based on the distance buffer for broadcast to the plurality of vehicles; The method according to claim 1, Aspect 9 9. The method of claim 8, further comprising broadcasting the modified trajectory plan to the plurality of vehicles. Aspect 10 The response to the trajectory request may include: a trajectory planning response including a predicted trajectory for a given forward look-ahead period; a near real-time trajectory response including near real-time operating parameters of the vehicle; A null response; The method of embodiment 8, comprising at least one of: Aspect 11 the trajectory planning response is related to a first certainty estimate; the near real-time trajectory response is related to a second certainty estimate; 11. The method of embodiment 10, wherein the null response is related to a third certainty estimate. Aspect 12 The near real-time operating parameters of the vehicle include: Vehicle orientation data; Vehicle speed data; Vehicle lane control data; Vehicle steering angle data; Vehicle braking data; and 11. The method of embodiment 10, comprising at least one of: Aspect 13 estimating a trajectory plan for the vehicle based on the responses and generating therefrom an estimated trajectory plan for a given look ahead period, the estimated trajectory plan being related to a fine certainty estimate; determining a confidence estimate of the estimated trajectory plan of the vehicle based on a coarse confidence estimate and the fine confidence estimate related to the responses; selecting the distance buffer for the autonomous vehicle relative to the vehicle; 9. The method of embodiment 8, comprising the steps of: Aspect 14 When the response to the trajectory request includes a null response, the certainty estimate of the vehicle's trajectory is estimating a trajectory stability state of the vehicle based on the vehicle sensor data; determining the confidence estimate of the vehicle's trajectory from the trajectory stability state; selecting the distance buffer for the autonomous vehicle relative to the vehicle; The method of embodiment 8, wherein the determination is made by Aspect 15 1. A vehicle control unit for autonomous vehicle operation in a heterogeneous vehicle environment, comprising: a wireless communication interface for servicing communication with a vehicle network; a processor communicatively coupled to the wireless communication interface and to a plurality of vehicle sensor devices; a memory communicatively coupled to the processor; having The memory includes: When executed by the processor, the processor: Detecting a vehicle moving in front of the autonomous vehicle based on vehicle sensor data; Transmitting a trajectory request for trajectory information of the vehicle; receiving a response to the trajectory request; a vehicle detection module including instructions to When executed by the processor, the processor: determining a confidence estimate of the vehicle's trajectory based on the responses; selecting a distance buffer for the autonomous vehicle relative to the vehicle and the certainty estimate; and modifying a trajectory plan for the autonomous vehicle to generate a modified trajectory plan based on the distance buffer. a trajectory planning modification module including instructions for modifying the trajectory plan; A vehicle control unit comprising: Aspect 16 16. The vehicle control unit of embodiment 15, further comprising a transmission module comprising instructions that, when executed by the processor, cause the processor to broadcast the modified trajectory plan. Aspect 17 The response to the trajectory request may include: a trajectory planning response including a predicted trajectory for a given forward look-ahead period; a near real-time trajectory response including near real-time operating parameters of the vehicle; A null response; 16. The vehicle control unit of embodiment 15, having at least one of: Aspect 18 the trajectory planning response is related to a first certainty estimate; the near real-time trajectory response is related to a second certainty estimate; 18. The vehicle control unit of embodiment 17, wherein the null response is related to a third certainty estimate. Aspect 19 The trajectory planning modification module, when executed by the processor, causes the processor to: Estimating a trajectory plan for the vehicle based on the responses, and generating therefrom an estimated trajectory plan for a given forward look period, the estimated trajectory plan being related to a fine certainty estimate; determining the confidence estimate of the estimated trajectory plan for the vehicle based on a coarse confidence estimate and the fine confidence estimate associated with the responses; selecting the distance buffer for the autonomous vehicle relative to the vehicle; 16. The vehicle control unit of embodiment 15, further comprising instructions to Aspect 20 The trajectory planning modification module, when executed by the processor, causes the processor to: when the response to the trajectory request includes a null response; estimating a trajectory stability state of the vehicle based on the vehicle sensor data; determining the certainty estimate of the trajectory of the vehicle from the trajectory stability state; selecting the distance buffer for the autonomous vehicle relative to the vehicle; 16. The vehicle control unit of embodiment 15, further comprising instructions for determining the certainty estimate of the vehicle's trajectory by:

Claims

1. 1. A method for autonomous vehicle operation in a heterogeneous vehicle environment, comprising: detecting a vehicle moving forward in a direction of the autonomous vehicle based on vehicle sensor data from the autonomous vehicle; Transmitting a trajectory request for trajectory information of the detected vehicle; a response by the detected vehicle to the trajectory request, a trajectory planning response including a predicted trajectory for a given forward look-ahead period; a near real-time trajectory response including near real-time operating parameters of the sensed vehicle; A null response; based on a response to the trajectory request having one of: determining a confidence estimate of the detected vehicle trajectory; selecting a distance buffer that decreases as the certainty estimate of the autonomous vehicle movement relative to the detected vehicle increases; modifying a trajectory plan of the autonomous vehicle movement to generate a modified trajectory plan based on the distance buffer to modify the autonomous vehicle movement of the autonomous vehicle; having a response to the trajectory request indicating a level of autonomous vehicle operation capability of the detected vehicle; the trajectory planning response corresponds to a vehicle with autonomous vehicle operation capability; the near real-time trajectory response corresponds to a driver assistance level of autonomy capability; The method, wherein the null response does not correspond to a level of autonomous vehicle operation capability.

2. The method of claim 1 , further comprising broadcasting the modified trajectory plan.

3. the trajectory planning response is related to a first certainty estimate; the near real-time trajectory response is related to a second certainty estimate; The method of claim 1 , wherein the null response is related to a third certainty estimate.

4. estimating a trajectory plan for the detected vehicle based on the responses and generating therefrom an estimated trajectory plan for a predefined forward observation period, the estimated trajectory plan being related to a fine certainty estimate based on motion data contained in the responses and observations via vehicle sensors of the detected vehicle; determining a confidence estimate of the estimated trajectory plan of the detected vehicle based on a coarse confidence estimate based solely on motion data included in the response and the fine confidence estimate; selecting the distance buffer for the autonomous vehicle relative to the detected vehicle; 2. The method of claim 1, comprising:

5. 1. A method for autonomous vehicle operation in a heterogeneous vehicle environment, comprising: Detecting a vehicle moving forward in a direction of the autonomous vehicle based on vehicle sensor data of an autonomous vehicle of a plurality of vehicles under the autonomous vehicle operation; Transmitting a trajectory request for trajectory information of the detected vehicle; A response to the trajectory request by the detected vehicle, a trajectory planning response including a predicted trajectory for a given forward look-ahead period; a near real-time trajectory response including near real-time operating parameters of the sensed vehicle; A null response; based on a response to the trajectory request having one of: determining a confidence estimate of the detected vehicle trajectory; selecting a distance buffer for the autonomous vehicle relative to the detected vehicle; modifying a trajectory plan of the autonomous vehicle to generate a modified trajectory plan based on the distance buffer for broadcast to the plurality of vehicles; having a response to the trajectory request indicating a level of autonomous vehicle operation capability of the detected vehicle; the trajectory planning response corresponds to a vehicle with autonomous vehicle operation capability; the near real-time trajectory response corresponds to a driver assistance level of autonomy capability; The method, wherein the null response does not correspond to a level of autonomous vehicle operation capability.

6. The method of claim 5 , further comprising broadcasting the modified trajectory plan to the plurality of vehicles.

7. the trajectory planning response is related to a first certainty estimate; the near real-time trajectory response is related to a second certainty estimate; The method of claim 5 , wherein the null response is related to a third certainty estimate.

8. estimating a trajectory plan for the detected vehicle based on the responses and generating therefrom an estimated trajectory plan for a predetermined look ahead period, the estimated trajectory plan relating to a fine certainty estimate based on motion data contained in the responses and observations via vehicle sensors of the detected vehicle; determining a confidence estimate of the estimated trajectory plan of the detected vehicle based on a coarse confidence estimate based solely on motion data included in the response and the fine confidence estimate; selecting the distance buffer for the autonomous vehicle relative to the detected vehicle; 6. The method of claim 5, comprising:

9. 1. A vehicle control unit for autonomous vehicle operation in a heterogeneous vehicle environment, comprising: a wireless communication interface for servicing communication with a vehicle network; a processor communicatively coupled to the wireless communication interface and to a plurality of vehicle sensor devices; a memory communicatively coupled to the processor; having The memory includes: When executed by the processor, the processor: Detecting a vehicle moving forward in a direction of the autonomous vehicle based on vehicle sensor data from the autonomous vehicle; Transmitting a trajectory request for trajectory information of the detected vehicle; A response to the trajectory request, a trajectory planning response including a predicted trajectory for a given forward look-ahead period; a near real-time trajectory response including near real-time operating parameters of the sensed vehicle; A null response; receiving a response to the trajectory request having one of: a vehicle detection module including instructions to When executed by the processor, the processor: determining a confidence estimate of the detected vehicle trajectory based on the responses; selecting a distance buffer for the autonomous vehicle relative to the detected vehicle and the certainty estimate; and modifying a trajectory plan for the autonomous vehicle to generate a modified trajectory plan based on the distance buffer. a trajectory planning modification module including instructions for modifying the trajectory plan; Including, a response to the trajectory request indicating a level of autonomous vehicle operation capability of the detected vehicle; the trajectory planning response corresponds to a vehicle with autonomous vehicle operation capability; the near real-time trajectory response corresponds to a driver assistance level of autonomy capability; The null response does not correspond to a level of autonomous vehicle operation capability.

10. The vehicle control unit of claim 9 , further comprising a transmission module comprising instructions that, when executed by the processor, cause the processor to broadcast the modified trajectory plan.

11. the trajectory planning response is related to a first certainty estimate; the near real-time trajectory response is related to a second certainty estimate; The vehicle control unit of claim 9 , wherein the null response is related to a third certainty estimate.

12. The trajectory planning modification module, when executed by the processor, causes the processor to: estimating a trajectory plan for the detected vehicle based on the responses, and generating therefrom an estimated trajectory plan for a predetermined look ahead period, the estimated trajectory plan being related to a fine certainty estimate based on motion data contained in the responses and observations via vehicle sensors of the detected vehicle; determining a confidence estimate of the estimated trajectory plan of the detected vehicle based on a coarse confidence estimate based solely on motion data included in the response and the fine confidence estimate; selecting the distance buffer for the autonomous vehicle relative to the detected vehicle; The vehicle control unit of claim 9 further comprising instructions to do so.

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