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WO2020164090A1 - Prédiction de trajectoire servant à une stratégie de conduite - Google Patents

Prédiction de trajectoire servant à une stratégie de conduite Download PDF

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Publication number
WO2020164090A1
WO2020164090A1 PCT/CN2019/075159 CN2019075159W WO2020164090A1 WO 2020164090 A1 WO2020164090 A1 WO 2020164090A1 CN 2019075159 W CN2019075159 W CN 2019075159W WO 2020164090 A1 WO2020164090 A1 WO 2020164090A1
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Prior art keywords
predictor
hybrid
predictors
static
trajectory
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Ceased
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PCT/CN2019/075159
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English (en)
Inventor
Gaowei Xu
Yao Ge
Maximilian DOEMLING
Dominik Notz
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Bayerische Motoren Werke AG
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Bayerische Motoren Werke AG
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Priority to EP19914873.5A priority Critical patent/EP3924795A4/fr
Priority to PCT/CN2019/075159 priority patent/WO2020164090A1/fr
Priority to CN201980091906.XA priority patent/CN113454555A/zh
Publication of WO2020164090A1 publication Critical patent/WO2020164090A1/fr
Anticipated expiration legal-status Critical
Ceased legal-status Critical Current

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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
    • G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
    • G01C21/26—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 specially adapted for navigation in a road network
    • G01C21/34—Route searching; Route guidance
    • G01C21/3407—Route searching; Route guidance specially adapted for specific applications
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W30/00—Purposes of road vehicle drive control systems not related to the control of a particular sub-unit, e.g. of systems using conjoint control of vehicle sub-units
    • B60W30/08—Active safety systems predicting or avoiding probable or impending collision or attempting to minimise its consequences
    • B60W30/095—Predicting travel path or likelihood of collision
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W40/00—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models
    • B60W40/02—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models related to ambient conditions
    • B60W40/06—Road conditions
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S13/00—Systems using the reflection or reradiation of radio waves, e.g. radar systems; Analogous systems using reflection or reradiation of waves whose nature or wavelength is irrelevant or unspecified
    • G01S13/66—Radar-tracking systems; Analogous systems
    • G01S13/72—Radar-tracking systems; Analogous systems for two-dimensional tracking, e.g. combination of angle and range tracking, track-while-scan radar
    • G01S13/723—Radar-tracking systems; Analogous systems for two-dimensional tracking, e.g. combination of angle and range tracking, track-while-scan radar by using numerical data
    • G01S13/726—Multiple target tracking
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S13/00—Systems using the reflection or reradiation of radio waves, e.g. radar systems; Analogous systems using reflection or reradiation of waves whose nature or wavelength is irrelevant or unspecified
    • G01S13/88—Radar or analogous systems specially adapted for specific applications
    • G01S13/93—Radar or analogous systems specially adapted for specific applications for anti-collision purposes
    • G01S13/931—Radar or analogous systems specially adapted for specific applications for anti-collision purposes of land vehicles
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S7/00—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00
    • G01S7/02—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00
    • G01S7/41—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00 using analysis of echo signal for target characterisation; Target signature; Target cross-section
    • G01S7/415—Identification of targets based on measurements of movement associated with the target
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/04—Architecture, e.g. interconnection topology
    • G06N3/044—Recurrent networks, e.g. Hopfield networks
    • G06N3/0442—Recurrent networks, e.g. Hopfield networks characterised by memory or gating, e.g. long short-term memory [LSTM] or gated recurrent units [GRU]
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W2554/00—Input parameters relating to objects
    • B60W2554/20—Static objects
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W2554/00—Input parameters relating to objects
    • B60W2554/40—Dynamic objects, e.g. animals, windblown objects
    • B60W2554/402—Type
    • B60W2554/4026—Cycles
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W2554/00—Input parameters relating to objects
    • B60W2554/40—Dynamic objects, e.g. animals, windblown objects
    • B60W2554/402—Type
    • B60W2554/4029—Pedestrians
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/04—Architecture, e.g. interconnection topology
    • G06N3/044—Recurrent networks, e.g. Hopfield networks
    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles
    • G08G1/16—Anti-collision systems
    • G08G1/166—Anti-collision systems for active traffic, e.g. moving vehicles, pedestrians, bikes

Definitions

  • the present disclosure relates in general to automated driving vehicles, and in more particular, to trajectory predictions for driving strategies.
  • An automated driving vehicle also known as a driverless car, self-driving car, or robotic car
  • ADV Automated driving vehicles
  • ADV use a variety of techniques to detect their surroundings, such as radar, laser light, GPS, odometry and computer vision.
  • Advanced control systems interpret sensory information to identify appropriate navigation paths, as well as obstacles and relevant signage.
  • HMM hidden Markov model
  • the present disclosure aims to provide a method, an apparatus and a vehicle for trajectory prediction.
  • a computer-implemented method for trajectory prediction comprises obtaining sensor data for an object surrounding a vehicle from at least one sensor installed on the vehicle; feeding the obtained sensor data into a hybrid predictor consisting of a plurality of predictors each with a weight assigned by the hybrid predictor, wherein the plurality of predictors comprise at least a free space predictor and a roadmodel-based predictor; for a current time instant, each of the plurality of predictors giving its own predictions for a predetermined period of time from the current time instant based on historical sensor data from the at least one sensor; the hybrid predictor determining a reliability of each one of the plurality of predictors; the hybrid predictor outputting weighted hybrid predictions for the predetermined period of time from the current time instant based on the respective predictions given by each of the plurality of predictors and the reliability of each one of the plurality of predictors, with assigning a greater weight to the predictor that gave a better prediction in
  • a trajectory prediction apparatus comprising a sensor data obtaining module configured to obtain sensor data for an object surrounding a vehicle from at least one sensor installed on the vehicle; a hybrid predictor consisting of a plurality of predictors each with a weight assigned by the hybrid predictor, wherein the plurality of predictors comprises at least a free space predictor and a roadmodel-based predictor, and the hybrid predictor is configured to: receive the sensor data from the sensor data obtaining module; for a current time instant, use the free space predictor and the roadmodel-based predictor to each give its own predictions for a predetermined period of time from the current time instant based on historical sensor data from the at least one sensor, determine which one of the plurality of predictors gave a better prediction that is closer to the actual movement of the object in previous one or more time instants; and output weighted hybrid predictions for the predetermined period of time from the current time instant based on the respective predictions given by each of the plurality of predictor
  • a vehicle comprising at least one sensor configured to capture sensing data for objects surrounding the vehicle; the trajectory prediction apparatus according to the second embodiment; and a decision module configured to make vehicle control decisions based on the trajectories for objects surrounding the vehicle predicted by the trajectory prediction apparatus.
  • the invention can handle the scenarios where there doesn't exist lanes such as road junctions.
  • the invention can predict the future trajectories and kinematic states of agents which may not move in lanes, such as pedestrians, scooters, bicycles, etc., because the free space predictor will play a significant role in this scenarios.
  • the invention can alleviate the phenomenon that the predicted results are oscillatory when agents are moving with very low velocities or even static in that the hybrid predictor has a static agent classifier.
  • the invention improves the prediction accuracy of future vehicle trajectories and it is more flexible and independent of road structures.
  • Fig. 1 illustrates an exemplary road environment.
  • Fig. 2 shows a schematic block diagram of a trajectory prediction apparatus in accordance with one embodiment of the present invention.
  • Fig. 3 is a partial enlarged view of the road environment of Fig. 1.
  • Fig. 4 is another partial enlarged view of the road environment of Fig. 1.
  • Fig. 5 is a flow diagram of a method for trajectory prediction, in accordance with one embodiment of the present invention.
  • Fig. 6 illustrates an exemplary vehicle according to an embodiment of the present invention.
  • Fig. 7 illustrates a general hardware environment wherein the present disclosure is applicable in accordance with an exemplary embodiment of the present disclosure.
  • vehicle used through the specification refers to cars of any type, including but not limited to sedans, vans, trucks, buses, or the like. For simplicity, the invention is described with respect to “car” .
  • a or B used through the specification refers to “A and B” and “A or B” rather than meaning that A and B are exclusive, unless otherwise specified.
  • trajectory prediction there are several methods of trajectory prediction in the prior art. Among these methods, more typical trajectory predictions are the roadmodel-based trajectory prediction and the movement-history-based trajectory prediction.
  • the roadmodel-based trajectory prediction is based on existing road models, and thus, apparently, such trajectory prediction is only suitable for those areas where road models exist. Also, it is only applicable to automobiles. In addition, such kind of trajectory prediction also assumes that cars comply with traffic rules, for example, cars will travel along the centerlines of lanes, cars will change direction according to the lane direction indicators, cars will not change lanes arbitrarily or illegally, and the like.
  • the movement-history-based trajectory prediction is based on the movement of an object over a period of time (also referred to as "time horizon" ) to predict future trajectories. How these two trajectory predictions will work in a road environment will be explained with respect to Fig. 1.
  • Fig. 1 illustrates an exemplary road environment. Shown in Fig. 1 is an exemplary three-way intersection comprising a main road in a vertical direction and a branch in a horizontal direction. The main road has 2 lanes, one being a straight-only lane and the other being a right-turn-only lane for cars to turn right into the branch. Also shown in Fig. 1 is a car 102 traveling on the right-turn lane and is about to reach the intersection. At this moment, a roadmodel-based trajectory prediction is able to recognize that the car 102 is traveling on the right-turn lane due to the knowledge of the road model of this intersection.
  • the roadmodel-based trajectory prediction thus gives a prediction that the car 102 will travel along the trajectory shown as the dashed line 104, i.e., turning right at the intersection and driving into the branch.
  • the movement-history-based trajectory prediction is unaware of the road model of the intersection. It gives predictions only based on the movement history of an object over a period of time.
  • the previous two observation positions 102' and 102" of the car 102 are schematically illustrated in Fig. 1. It will be appreciated that only two previous positions are shown for simplicity, and in practice, more historical locations (e.g., 5 or 10 locations) may be necessary to predict the trajectory.
  • the movement-history-based trajectory prediction thus predicts that the car 102 will travel along the trajectory shown by the dashed line 106, i.e., continually travelling straightforward.
  • the roadmodel-based trajectory prediction can generally predict the future trajectory of the vehicle more accurately.
  • such roadmodel-based trajectory prediction can only be applied in areas with road models, and it must be assumed that objects obey traffic rules, and such prediction is only for cars.
  • the movement-history-based trajectory prediction is much more flexible. It works without various assumptions and it is not limited in cars.
  • Its disadvantage is that, since its prediction is based only on historical trajectories, such prediction is actually more similar to summarizing a past trajectory (such as finding a common smooth curve for location points) and extending this trajectory to the future. Therefore, for a motion trajectory with a sharp change (such as a right-angle turn) , the trajectory prediction may deviate greatly from the actual trajectory, and the prediction may be less accurate as the predicted future time is further away from the current time.
  • Fig. 2 shows a schematic block diagram of a trajectory prediction apparatus 200 in accordance with one embodiment of the present invention.
  • the trajectory prediction apparatus 200 may include a sensor data obtaining module 202, a hybrid predictor 204, and a trajectory predicting module 206.
  • the sensor data obtaining module 202 is configured to obtain sensor data for an object surrounding a vehicle from at least one sensor installed on the vehicle.
  • the hybrid predictor 204 may include a plurality of existing predictors.
  • the hybrid predictor 204 may include at least one free space predictor 208 and one road model based predictor 210.
  • the hybrid predictor 204 may optionally include a static object classifier 212.
  • the free space predictor 208 may give the positions of points on the movement trajectory curve corresponding to a plurality of time points in the future as the prediction result.
  • the roadmodel-based predictor 210 may give a prediction of future locations of the object based on its knowledge of the road model where the current location of the object is.
  • the free space predictor 208 (labeled as Predictor 1) and the roadmodel-based predictor 210 (labeled as Predictor 2) may respectively give a prediction of positions at a plurality of time instances (t c ⁇ 1 , t c ⁇ 2 , t c ⁇ 3 , t c ⁇ 4 , ...) in a future period of time (for example, 5 seconds, 10 seconds) , and, The number of time instances may depend on the size of the time horizon and the sampling accuracy. Therefore, a data structure can be contemplated as shown in Table 1 below:
  • Fig. 3 is a partial enlarged view of the road environment of Fig. 1.
  • Fig. 3 it is illustrated that the car 102 has reached the intersection and has just begun to turn to the right. It is assumed that the current time point corresponding to the current position of the car 102 is tc, and its position is marked as Gc. In addition, it can be seen that the previous positions of the car 102 are Gc ⁇ 1 , Gc ⁇ 2 , Gc ⁇ 3 , Gc ⁇ 4 , Gc ⁇ 5 , ..., respectively.
  • predicted values and respectively given by the free space predictor 208 and the roadmodel-based predictor 210 are located at corresponding positions as shown in the figure. It can be seen that the free space predictor 208 considers that the car 102 is traveling in a straight line based on the current and previous positions Gc ⁇ 1 , Gc ⁇ 2 , Gc ⁇ 3 , Gc ⁇ 4 , and Gc ⁇ 5 of the car 102, and thus the prediction values reflect that the car 102 will continue traveling forward along the straight line, which is the trajectory 106.
  • the roadmodel-based predictor 210 since the roadmodel-based predictor 210 is aware of that the car 102 is traveling on the right-turn lane, the predicted values given by it reflect that the car 102 will travel along the trajectory 104. Thus it can be seen that, at the past time instance t c-1 , the predicted value given by the roadmodel-based predictor 210 for the current time tc is closer to the actual value Gc, and therefore the roadmodel-based predictor 210 is currently a more accurate and reliable predictor.
  • the hybrid predictor 204 is configured to fuse the predictions from a plurality of predictors and output fused prediction results.
  • the fusion can be done by assigning a weight to each predictor and using the weighted value as the fused prediction.
  • the Predictor 1 may be assigned a weight w 1 and the Predictor 2 may be assigned a weight w 2 .
  • the fused prediction results can thus be calculated as:
  • the hybrid predictor 204 assigns the roadmodel-based predictor 210 a higher weight, i.e., w 2 > w 1 when predicting the trajectory for future time instances, t c+1 , t c+1 , t c+1 , .... .
  • the specific assigning of the values of w 1 and w 2 can be determined based on the differences between and Gc and and Gc, respectively.
  • Fig. 4 is also a partial enlarged view of the road environment of Fig. 1. Different from Fig. 3, now assuming that the current time instance is t c+1 , and the car 102 has traveled a certain distance forward along the trajectory 104.
  • the free space predictor 208 considered that the car 102 was only slightly off the line based on the current and previous positions Gc, Gc ⁇ 1 , Gc ⁇ 2 , Gc ⁇ 3 , Gc ⁇ 4 , and Gc ⁇ 5 of the car 102, and accordingly, the predicted positions given, reflect that the car 102 would travel along the trajectory 106', which only slightly deviates from the original trajectory 106.
  • the roadmodel-based predictor 210 is aware of that the car 102 is traveling on the right-turn lane, so the predicted value given by it reflect that the car 102 will continue to travel along the trajectory 104. Similar to the case of Fig.
  • the hybrid predictor 204 will assign the roadmodel-based predictor 210 a higher weight. While, if further considering the position of G c+1 and the predictions and respectively given by the two predictors at time t c-1 , the prediction given by the free space predictor 208 is still on the dashed line 106, and the distance between the prediction and the actual position G c+1 is farther than the distance between the prediction given at time tc and the actual position G c+1 . Meanwhile, the predictions and given at time t c-1 and time tc by the roadmodel-based prediction model 210 approximately coincide with G c+1 . Therefore, the hybrid predictor 204 is further confident in that the reliability of the roadmodel-based predictor 210 is higher at this moment, and thus the roadmodel-based predictor 210 will be given a greater weight.
  • the hybrid predictor 204 gives fused prediction values, such as the trajectory prediction module 206 can provide predicted trajectories based on the fused predicted values.
  • the hybrid predictor 204 may also optionally include a static object classifier 212.
  • a static object classifier 212 It is well known that current sensors are more or less unstable or inaccurate. For example, for an object that is stationary, the observation data of sensors for that object may jitter over time. Therefore, performing a movement trajectory prediction for observation data of a static object is a waste of resources and may also lead to erroneous trajectory prediction.
  • the static object classifier 212 may be configured to classify an object as static or non-static by analysis of sensor data.
  • the static object classifier 212 may distinguish between static and non-static by for example setting thresholds for different types of sensor data, such as classifying an object as a static object when detecting that the speed of the object is below a certain predetermined threshold.
  • the static object classifier 212 may also analyze the distribution of sensor data over time, and classify an object as static when the data exhibits a small fluctuation around a certain value (e.g., a velocity value of zero and an acceleration value of zero) .
  • a certain value e.g., a velocity value of zero and an acceleration value of zero
  • the hybrid predictor 204 outputs the current location of the static object as its prediction result, i.e., predicting that the static object will remain stationary.
  • the static object classifier 212 may optionally further smooth the observation data using a Kalman filter, and then feed it to the free space predictor 208 and the roadmodel- based predictor 210.
  • Fig. 5 is a flow diagram of a method 500 for trajectory prediction in accordance with one embodiment of the present invention.
  • the method 500 begins at block 502, where sensor data for an object surrounding a vehicle may be obtained from at least one sensor installed on the vehicle.
  • the obtained sensor data may be fed into a hybrid predictor, at block 504.
  • the hybrid predictor of the present invention may consist of a plurality of predictors, each with a weight assigned by the hybrid predictor.
  • the plurality of predictors comprise at least a free space predictor, such as the free space predictor 208 in Fig. 2, and a roadmodel-based predictor, such as the roadmodel-based predictor 210.
  • each of the plurality of predictors may give its own predictions for a predetermined period of time from the current time instant.
  • the free space predictor 208 may give its predictions based on historical sensor data from the at least one sensor as previously described with regard to Fig. 3.
  • the roadmodel-based predictor 210 may also give its predictions mainly based on its knowledge of the road model, while it also uses the historical sensor data to determine the position of the object in the road model.
  • the hybrid predictor 204 determines the reliability of each of the plurality of predictors. As an example, this can be determined by evaluating which of the predictors gave a better prediction that is closer to the actual movement of the object in previous one or more time instants, as previously described with regard to Figs 3 and 4.
  • the hybrid predictor may output weighted hybrid predictions for the predetermined period of time from the current time instant based on the respective predictions given by each of the plurality of predictors, with greater weights being assigned to the more reliable predictors.
  • outliers may be dropped from the weighted hybrid predictions.
  • an iterative multivariate Gaussian model is used to fuse all the predictions of different sources with various time horizons after dropping outliers iteratively.
  • a predicted trajectory may be provided based on the weighted hybrid predictions. The method 500 ends.
  • the hybrid predictor 204 has a static object classifier 212
  • the obtained sensor data may be first fed into the static object classifier 212 to classify the object into static or non-static.
  • the static object classifier 212 classifies the object into static, the predicted trajectory can be directly provided based on the static state of the object, which means the predictions of the plurality of predictors are skipped.
  • the static object classifier 212 may optionally smooth the sensor data, such as by using a Kalman filter, before feeding it into the plurality of predictors.
  • Fig. 6 illustrates an exemplary vehicle 600 according to an embodiment of the present invention.
  • the vehicle 600 may comprise at least one sensor 602 configured to capture sensing data for objects surrounding the vehicle, a trajectory prediction apparatus 604 for providing trajectory predictions, such as the trajectory prediction apparatus 200 in Fig. 2, and a decision module 606 configured to make vehicle control decisions based on the trajectories for objects surrounding the vehicle predicted by the trajectory prediction apparatus 604.
  • Fig. 7 illustrates a general hardware environment 700 wherein the present disclosure is applicable in accordance with an exemplary embodiment of the present disclosure.
  • the computing device 700 may be any machine configured to perform processing and/or calculations, may be but is not limited to a work station, a server, a desktop computer, a laptop computer, a tablet computer, a personal data assistant, a smart phone, an on-vehicle computer or any combination thereof.
  • the aforementioned system may be wholly or at least partially implemented by the computing device 700 or a similar device or system.
  • the computing device 700 may comprise elements that are connected with or in communication with a bus 702, possibly via one or more interfaces.
  • the computing device 700 may comprise the bus 702, and one or more processors 704, one or more input devices 706 and one or more output devices 708.
  • the one or more processors 704 may be any kinds of processors, and may comprise but are not limited to one or more general-purpose processors and/or one or more special-purpose processors (such as special processing chips) .
  • the input devices 706 may be any kinds of devices that can input information to the computing device, and may comprise but are not limited to a mouse, a keyboard, a touch screen, a microphone and/or a remote control.
  • the output devices 708 may be any kinds of devices that can present information, and may comprise but are not limited to display, a speaker, a video/audio output terminal, a vibrator and/or a printer.
  • the computing device 700 may also comprise or be connected with non-transitory storage devices 710 which may be any storage devices that are non-transitory and can implement data stores, and may comprise but are not limited to a disk drive, an optical storage device, a solid-state storage, a floppy disk, a flexible disk, hard disk, a magnetic tape or any other magnetic medium, a compact disc or any other optical medium, a ROM (Read Only Memory) , a RAM (Random Access Memory) , a cache memory and/or any other memory chip or cartridge, and/or any other medium from which a computer may read data, instructions and/or code.
  • non-transitory storage devices 710 which may be any storage devices that are non-transitory and can implement data stores, and may comprise but are not limited to a disk drive, an optical storage
  • the non-transitory storage devices 710 may be detachable from an interface.
  • the non-transitory storage devices 710 may have data/instructions/code for implementing the methods and steps which are described above.
  • the computing device 700 may also comprise a communication device 712.
  • the communication device 712 may be any kinds of device or system that can enable communication with external apparatuses and/or with a network, and may comprise but are not limited to a modem, a network card, an infrared communication device, a wireless communication device and/or a chipset such as a Bluetooth TM device, 802.11 device, WiFi device, WiMax device, cellular communication facilities and/or the like.
  • the computing device 700 When the computing device 700 is used as an on-vehicle device, it may also be connected to external device, for example, a GPS receiver, sensors for sensing different environmental data such as an acceleration sensor, a wheel speed sensor, a gyroscope and so on. In this way, the computing device 700 may, for example, receive location data and sensor data indicating the travelling situation of the vehicle.
  • external device for example, a GPS receiver, sensors for sensing different environmental data such as an acceleration sensor, a wheel speed sensor, a gyroscope and so on.
  • the computing device 700 may, for example, receive location data and sensor data indicating the travelling situation of the vehicle.
  • other facilities such as an engine system, a wiper, an anti-lock Braking System or the like
  • non-transitory storage device 710 may have map information and software elements so that the processor 704 may perform route guidance processing.
  • the output device 706 may comprise a display for displaying the map, the location mark of the vehicle and also images indicating the travelling situation of the vehicle.
  • the output device 706 may also comprise a speaker or interface with an ear phone for audio guidance.
  • the bus 702 may include but is not limited to Industry Standard Architecture (ISA) bus, Micro Channel Architecture (MCA) bus, Enhanced ISA (EISA) bus, Video Electronics Standards Association (VESA) local bus, and Peripheral Component Interconnect (PCI) bus. Particularly, for an on-vehicle device, the bus 702 may also include a Controller Area Network (CAN) bus or other architectures designed for application on an automobile.
  • ISA Industry Standard Architecture
  • MCA Micro Channel Architecture
  • EISA Enhanced ISA
  • VESA Video Electronics Standards Association
  • PCI Peripheral Component Interconnect
  • CAN Controller Area Network
  • the computing device 700 may also comprise a working memory 714, which may be any kind of working memory that may store instructions and/or data useful for the working of the processor 704, and may comprise but is not limited to a random access memory and/or a read-only memory device.
  • working memory 714 may be any kind of working memory that may store instructions and/or data useful for the working of the processor 704, and may comprise but is not limited to a random access memory and/or a read-only memory device.
  • Software elements may be located in the working memory 714, including but are not limited to an operating system 716, one or more application programs 718, drivers and/or other data and codes. Instructions for performing the methods and steps described in the above may be comprised in the one or more application programs 718, and the units of the aforementioned apparatus 800 may be implemented by the processor 704 reading and executing the instructions of the one or more application programs 718.
  • the executable codes or source codes of the instructions of the software elements may be stored in a non-transitory computer-readable storage medium, such as the storage device (s) 710 described above, and may be read into the working memory 714 possibly with compilation and/or installation.
  • the executable codes or source codes of the instructions of the software elements may also be downloaded from a remote location.
  • the present disclosure may be implemented by software with necessary hardware, or by hardware, firmware and the like. Based on such understanding, the embodiments of the present disclosure may be embodied in part in a software form.
  • the computer software may be stored in a readable storage medium such as a floppy disk, a hard disk, an optical disk or a flash memory of the computer.
  • the computer software comprises a series of instructions to make the computer (e.g., a personal computer, a service station or a network terminal) execute the method or a part thereof according to respective embodiment of the present disclosure.

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Abstract

Certains exemples de la présente invention concernent un procédé, un appareil, et un véhicule de prédiction de trajectoire. Le procédé consiste : à obtenir des données de capteur concernant un objet environnant un véhicule provenant d'au moins un capteur installé sur le véhicule (502) ; à entrer les données de capteur obtenues dans un prédicteur hybride constitué d'une pluralité de prédicteurs dotés chacun d'un poids attribué par le prédicteur hybride, la pluralité de prédicteurs comprenant au moins un prédicteur d'espace libre et un prédicteur basé sur un modèle de route (504) ; à donner, par chaque prédicteur de la pluralité de prédicteurs, en ce qui concerne un instant actuel, ses propres prédictions pour une période de temps prédéfinie à partir de l'instant actuel en fonction de données de capteur historiques provenant dudit capteur (506) ; à déterminer, par le prédicteur hybride, une fiabilité de chaque prédicteur de la pluralité de prédicteurs (508) ; à délivrer en sortie, par le prédicteur hybride, des prédictions hybrides pondérées concernant la période de temps prédéfinie à partir de l'instant actuel en fonction des prédictions respectives données par chaque prédicteur de la pluralité de prédicteurs et de la fiabilité de chaque prédicteur de la pluralité de prédicteurs, tout en attribuant un poids supérieur au prédicteur ayant donné la meilleure prédiction audit instant précédent (510) ; et à fournir une trajectoire prédite basée sur les prédictions hybrides pondérées (512).
PCT/CN2019/075159 2019-02-15 2019-02-15 Prédiction de trajectoire servant à une stratégie de conduite Ceased WO2020164090A1 (fr)

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CN201980091906.XA CN113454555A (zh) 2019-02-15 2019-02-15 用于驾驶策略的轨迹预测

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