Other vehicle action prediction apparatus, method, and program product

Abstract

The present disclosure relates to other-vehicle-action-prediction apparatuses, other-vehicle-action-prediction methods, and program products. An other-vehicle-action-prediction apparatus predicts an action of another vehicle traveling in the vicinity of a host vehicle, acquires information indicating whether or not a surrounding situation sensor that detects a surrounding situation of the other vehicle is equipped in the other vehicle or information indicating a configuration of the surrounding situation sensor equipped in the other vehicle, and predicts the action of the other vehicle based on the information indicating whether or not the surrounding situation sensor is equipped in the other vehicle or the information indicating the configuration of the surrounding situation sensor equipped in the other vehicle.

Description

Technical Field

[0001] This disclosure relates to other vehicle motion prediction devices, other vehicle motion prediction methods, and program products. Background Technology

[0002] Patent document 1 (Japanese Patent Application Publication No. 2018-180641) describes the actions of acquiring the target vehicle and the actions of presuming the target vehicle.

[0003] While predictions (estimations) of other vehicles' actions have been made in the past, these predictions did not consider whether or not surrounding condition sensors were installed on other vehicles, or the configuration of those sensors. When surrounding condition sensors detected other vehicles, the other vehicle frequently made lane changes, accelerated / decelerated movements to avoid contact / approach. Conversely, if it was assumed that other vehicles were unaware of surrounding vehicles, these same actions were rarely performed. Nevertheless, because previous predictions of other vehicles' actions did not consider whether or not surrounding condition sensors were installed on other vehicles, or the configuration of those sensors, high-precision predictions of other vehicles' actions were not possible. Summary of the Invention

[0004] In view of the above points, the purpose of this disclosure is to provide a device, method and program for predicting the actions of other vehicles with high accuracy.

[0005] (1) One aspect of this disclosure is an other vehicle action prediction device having a processor, wherein the processor is configured to: predict the actions of other vehicles traveling around the vehicle; and acquire information indicating whether a surrounding condition sensor for detecting the surrounding conditions of the other vehicle is provided on the other vehicle or information indicating the configuration of the surrounding condition sensor provided on the other vehicle, the processor predicting the actions of the other vehicle based on the information indicating whether the surrounding condition sensor is provided on the other vehicle or the information indicating the configuration of the surrounding condition sensor provided on the other vehicle.

[0006] (2) In other vehicle action prediction devices of scheme (1), the action of the other vehicle predicted when information indicating that the surrounding condition sensor is not equipped on the other vehicle is different from the action of the other vehicle predicted when information indicating that the surrounding condition sensor is equipped on the other vehicle is obtained.

[0007] (3) In other vehicle action prediction devices of scheme (1) or (2), the processor may be configured to: acquire information indicating whether there are surrounding vehicles driving around the other vehicle; and predict the likelihood of the other vehicle changing lanes by using a prediction model based on information indicating whether the surrounding condition sensor is equipped with the other vehicle or information indicating the configuration of the surrounding condition sensor equipped with the other vehicle and information indicating whether there are surrounding vehicles, wherein the prediction model is a model obtained by learning using training data, the training data being a dataset of labels indicating whether the learning surrounding condition sensor for detecting the surrounding condition of the learning other vehicle is equipped with the learning other vehicle or information indicating the configuration of the learning surrounding condition sensor equipped with the learning other vehicle and information indicating whether there are learning surrounding vehicles driving around the learning other vehicle and information indicating whether the learning other vehicle has changed lanes.

[0008] (4) In any of the other vehicle movement prediction devices in schemes (1) to (3), the processor may be configured to: generate information indicating whether the surrounding condition sensor is equipped with the other vehicle or information indicating the configuration of the surrounding condition sensor equipped with the other vehicle based on images of the other vehicle captured by a camera mounted on the vehicle; and acquire information indicating whether the surrounding condition sensor is equipped with the other vehicle or information indicating the configuration of the surrounding condition sensor equipped with the other vehicle.

[0009] (5) In any of the other vehicle movement prediction devices in schemes (1) to (4), the processor may acquire information obtained through communication between the vehicle and the outside of the vehicle, indicating whether the surrounding condition sensor is equipped on the other vehicle or information indicating the configuration of the surrounding condition sensor equipped on the other vehicle.

[0010] (6) In any of the other vehicle movement prediction devices in schemes (1) to (5), the processor may be configured to: generate information indicating whether the surrounding condition sensor is equipped with the other vehicle or information indicating the configuration of the surrounding condition sensor equipped with the other vehicle, based on radio waves detected by the radar detector mounted on the vehicle and transmitted from the surrounding condition sensor; and acquire information indicating whether the surrounding condition sensor is equipped with the other vehicle or information indicating the configuration of the surrounding condition sensor equipped with the other vehicle.

[0011] (7) One aspect of this disclosure is a method for predicting the actions of other vehicles, the method comprising: predicting the actions of other vehicles traveling around the vehicle; and the other vehicle action prediction device acquiring information indicating whether a surrounding condition sensor for detecting the surrounding conditions of the other vehicle is equipped with the other vehicle or information indicating the configuration of the surrounding condition sensor equipped with the other vehicle, wherein the actions of the other vehicle are predicted based on the information indicating whether the surrounding condition sensor is equipped with the other vehicle or the information indicating the configuration of the surrounding condition sensor equipped with the other vehicle.

[0012] (8) One aspect of this disclosure is a program product that records a computer program for causing a processor to perform the following actions: predicting the actions of other vehicles traveling around the vehicle; and acquiring information indicating whether a surrounding condition sensor for detecting the surrounding conditions of the other vehicle is equipped with the other vehicle or information indicating the configuration of the surrounding condition sensor equipped with the other vehicle, wherein the actions of the other vehicle are predicted based on the information indicating whether the surrounding condition sensor is equipped with the other vehicle or the information indicating the configuration of the surrounding condition sensor equipped with the other vehicle.

[0013] According to this disclosure, the actions of other vehicles can be predicted with high accuracy. Attached Figure Description

[0014] Figure 1 This is a diagram showing an example of the vehicle 1 that applies the other vehicle movement prediction device 15 of the first embodiment.

[0015] Figure 2 (A) is a diagram showing an example of the prediction results of the actions of other vehicle OVs obtained by the other vehicle action prediction unit 3F when the surrounding condition sensor that detects the surrounding conditions (surrounding vehicle SV) of other vehicles OVs is not equipped on the other vehicle OVs.

[0016] Figure 2 (B) is a diagram showing an example of the prediction results of the actions of other vehicle OVs obtained by the other vehicle action prediction unit 3F when a surrounding condition sensor is equipped with other vehicle OVs to detect the surrounding conditions (surrounding vehicle SVs).

[0017] Figure 3 This is a flowchart illustrating an example of a process performed by the processor 153 of another vehicle movement prediction device 15 of the first embodiment.

[0018] Figure 4 (A) is a diagram showing an example of the prediction results of the actions of other vehicle OVs obtained by the other vehicle action prediction unit 3F when the surrounding condition sensor that detects the surrounding conditions (surrounding vehicle SV) of other vehicles OVs is not equipped on the other vehicle OVs.

[0019] Figure 4 (B) is a diagram showing an example of the prediction results of the actions of other vehicle OVs obtained by the other vehicle action prediction unit 3F when a surrounding condition sensor is equipped with other vehicle OVs to detect the surrounding conditions (surrounding vehicle SVs). Detailed Implementation

[0020] Hereinafter, embodiments of other vehicle movement prediction devices, other vehicle movement prediction methods, and program products of this disclosure will be described with reference to the accompanying drawings.

[0021] <First Implementation> Figure 1 This is a diagram showing an example of the vehicle 1 that applies the other vehicle movement prediction device 15 of the first embodiment.

[0022] exist Figure 1 In the example shown, the vehicle 1 includes a surrounding condition sensor 11, a vehicle status sensor 12, an HMI (Human Machine Interface) 13, a vehicle control device 14, a steering actuator 14A, a brake actuator 14B, a drive actuator 14C, and other vehicle movement prediction devices 15.

[0023] The surrounding conditions sensor 11 monitors the surrounding conditions of vehicle 1 (e.g., other vehicles (OV) traveling around vehicle 1). Figure 2 (A) and Figure 2 (B) and surrounding vehicles (SV) traveling around other vehicles (OV) (refer to) Figure 2 (A) and Figure 2The surrounding conditions sensor 11 detects obstacles (such as those in the vicinity of vehicle 1) and sends the detection results to the vehicle control device 14 and other vehicle movement prediction devices 15. The surrounding conditions sensor 11 includes, for example, a camera, LiDAR (Light Detection and Ranging), radar, a wireless communication device that obtains information about the external conditions of vehicle 1 from the outside of vehicle 1 via wireless communication, and a radar detector that detects radio waves transmitted (emitted) from radar (not shown) mounted on other vehicles such as OV.

[0024] The vehicle status sensor 12 performs functions such as detecting the status of the vehicle 1 and measuring the position of the vehicle 1, and sends the detection results and position measurement results of the vehicle 1 to the vehicle control device 14 and other vehicle movement prediction devices 15. The vehicle status sensor 12 includes, for example, a vehicle speed sensor, an acceleration sensor, a yaw rate sensor, a gyroscope sensor, and a GPS (Global Positioning System) receiver.

[0025] HMI13 has functions such as accepting various operations of the driver of vehicle 1 and sending signals indicating the operation of the driver of vehicle 1 to vehicle control device 14.

[0026] The vehicle control unit 14 controls the steering actuator 14A, brake actuator 14B, and drive actuator 14C, for example, based on information (data, signals) sent from the surrounding condition sensor 11, the vehicle status sensor 12, and the HMI 13. Specifically, the vehicle control unit 14 has an autonomous driving function that allows the vehicle 1 to drive autonomously without requiring operation from the driver of the vehicle 1 to control the steering actuator 14A, brake actuator 14B, and drive actuator 14C. Specifically, the vehicle control unit 14 generates a driving plan for the vehicle 1 to reach its destination, for example, based on map information, the location information of the vehicle 1, and information indicating the destination of the vehicle 1. Furthermore, the vehicle control unit 14 drives the vehicle 1 autonomously according to this driving plan. Specifically, the vehicle control unit 14 uses the detection results of the surrounding condition sensor 11 and other vehicle movement prediction devices (OVs) obtained by the other vehicle movement prediction device 15 (described later) as referenced in the following section. Figure 2 (A) and Figure 2 The vehicle 1 can drive autonomously by modifying its driving plan in a way that avoids collisions with other vehicles such as OV, etc., based on the predicted results of the actions of (B).

[0027] Other vehicle movement prediction device 15 consists of a microcomputer with a communication interface (I / F) 151, a memory 152 and a processor 153.

[0028] The communication interface 151 has interface circuitry for connecting other vehicle movement prediction devices 15 to the surrounding condition sensor 11, vehicle status sensor 12, HMI 13, and vehicle control device 14. The memory 152 stores programs and various data used in the processing executed by the processor 153. The processor 153 functions as an acquisition unit 3A, a sensor information generation unit 3B, a sensor information acquisition unit 3C, a surrounding vehicle information acquisition unit 3D, an object lane recognition unit 3E, and an other vehicle movement prediction unit 3F.

[0029] exist Figure 1 In the example shown, the acquisition unit 3A acquires the detection results of the surrounding conditions of the vehicle 1 obtained by the surrounding condition sensor 11.

[0030] Specifically, the acquisition unit 3A, for example, acquires images captured by a camera acting as a surrounding condition sensor 11, including images of other vehicles (OV). Figure 2 (A) and Figure 2 Images such as (B) are used as the detection results of the surrounding conditions of the vehicle 1 obtained by the LiDAR as the surrounding conditions sensor 11, the detection results of the surrounding conditions of the vehicle 1 obtained by the radar as the surrounding conditions sensor 11, the communication results between the vehicle 1 and the outside of the vehicle 1 obtained by the wireless communication device as the surrounding conditions sensor 11, and information indicating the radio waves transmitted (emitted) from the radar (surrounding conditions sensor) (not shown) mounted on another vehicle OV detected by the radar detector as the surrounding conditions sensor 11, etc., as the detection results of the surrounding conditions of the vehicle 1 obtained by the surrounding conditions sensor 11.

[0031] In addition, Figure 1 In the example shown, the sensor information generation unit 3B generates information indicating whether a surrounding condition sensor for detecting the surrounding conditions of other vehicles (OVs) is equipped on the other vehicles (OVs) or information indicating the configuration of the surrounding condition sensor equipped on the other vehicles (OVs) based on the detection results of the surrounding conditions of the vehicle 1 obtained by the acquisition unit 3A through the surrounding condition sensor 11.

[0032] Specifically, in the example where the acquisition unit 3A acquires images including other vehicles (OVs) captured by the camera serving as the surrounding condition sensor 11, the sensor information generation unit 3B generates information indicating whether a surrounding condition sensor for detecting the surrounding conditions of other vehicles (OVs) is equipped on the other vehicles (OVs) or information indicating the configuration of the surrounding condition sensor equipped on the other vehicles (OVs) based on the images captured by the camera serving as the surrounding condition sensor 11.

[0033] Furthermore, in the example where the acquisition unit 3A acquires information indicating radio waves transmitted (emitted) from a radar mounted on another vehicle OV and detected by the radar detector serving as the surrounding condition sensor 11, the sensor information generation unit 3B generates information indicating whether a surrounding condition sensor (radar) for detecting the surrounding conditions of another vehicle OV is equipped on the other vehicle OV, or information indicating the configuration of a surrounding condition sensor (radar) equipped on the other vehicle OV, based on the radio waves transmitted from a radar mounted on the other vehicle OV and detected by the radar detector serving as the surrounding condition sensor 11.

[0034] Moreover, in Figure 1 In the example shown, the sensor information acquisition unit 3C acquires information indicating whether a surrounding condition sensor for detecting the surrounding conditions of other vehicles (OVs) is equipped on the other vehicle (OV) or information indicating the configuration of the surrounding condition sensor equipped on the other vehicle (OV).

[0035] Specifically, in the example where the sensor information generation unit 3B generates information indicating whether a surrounding condition sensor for detecting the surrounding conditions of other vehicles (OVs) is equipped on another vehicle (OV) or information indicating the configuration of a surrounding condition sensor equipped on another vehicle (OV), the sensor information acquisition unit 3C acquires the information generated by the sensor information generation unit 3B indicating whether a surrounding condition sensor for detecting the surrounding conditions of other vehicles (OVs) is equipped on another vehicle (OV) or information indicating the configuration of a surrounding condition sensor equipped on another vehicle (OV).

[0036] Furthermore, in the example where the sensor information generation unit 3B does not generate information indicating whether a surrounding condition sensor for detecting the surrounding conditions of other vehicles (OVs) is equipped with another vehicle (OV) or information indicating the configuration of a surrounding condition sensor equipped with another vehicle (OV), the sensor information acquisition unit 3C acquires information indicating whether a surrounding condition sensor for detecting the surrounding conditions of other vehicles (OVs) is equipped with another vehicle (OV) or information indicating the configuration of a surrounding condition sensor equipped with another vehicle (OV). This information is obtained from the detection result of the surrounding conditions of the vehicle 1 obtained by the acquisition unit 3A through the surrounding condition sensor 11. Specifically, the sensor information acquisition unit 3C, for example, acquires information indicating whether a surrounding condition sensor for detecting the surrounding conditions of other vehicles (OVs) is equipped with another vehicle (OV) or information indicating the configuration of a surrounding condition sensor equipped with another vehicle (OV). This information is obtained as the detection result of the surrounding conditions of the vehicle 1 through communication between the vehicle 1 and the outside of the vehicle 1 via a wireless communication device that serves as the surrounding condition sensor 11.

[0037] In addition, Figure 1 In the example shown, the 3D acquisition of surrounding vehicle information indicates whether there are other vehicles (OV) (refer to...). Figure 2 (A) and Figure 2 (B) surrounding vehicles SV (refer to) Figure 2 (A) and Figure 2 The information (B) is obtained from the detection results of the surrounding conditions of the vehicle 1 obtained by the surrounding condition sensor 11 through the acquisition unit 3A (e.g., camera images, LiDAR detection results, communication results of wireless communication devices, etc.).

[0038] The object lane recognition unit 3E, based on the detection results of the surrounding conditions of the vehicle 1 obtained by the acquisition unit 3A, performs OV (Operational Vehicle View) for other vehicles traveling around the vehicle 1 (refer to...). Figure 2 (A) and Figure 2 (B) and surrounding vehicles (SV) traveling around other vehicles (OV) (refer to) Figure 2 (A) and Figure 2 The identification of objects such as (B) and lanes located around the vehicle 1.

[0039] The Other Vehicle Action Prediction Unit 3F predicts the actions of other vehicles (OVs) based on information obtained by the Sensor Information Acquisition Unit 3C, which indicates whether a surrounding condition sensor that detects the surrounding conditions of other vehicles (OVs) is equipped with the other vehicle (OV), or information indicating the configuration of the surrounding condition sensors equipped with the other vehicle (OV).

[0040] Figure 2(A) and Figure 2 Figure (B) is an example of the detection results DRO (Time Series of Other Vehicles OV) obtained by the surrounding situation sensor 11, the detection results DRS (Time Series of Surrounding Vehicles SV) obtained by the surrounding situation sensor 11, and the prediction results of the actions of other vehicles OV obtained by the other vehicle action prediction unit 3F. In detail, Figure 2 (A) represents an example of the prediction result of the action of other vehicle OV obtained by the other vehicle action prediction unit 3F when the surrounding condition sensor that detects the surrounding condition (surrounding vehicle SV) of other vehicle OV is not equipped on the other vehicle OV. Figure 2 (B) represents an example of the prediction result of the action of the other vehicle OV obtained by the other vehicle action prediction unit 3F when the surrounding condition sensor that detects the surrounding condition (surrounding vehicle SV) of the other vehicle OV is equipped with the other vehicle OV.

[0041] exist Figure 2 In the example shown in (A), vehicle 1 is traveling in lane L1, other vehicle OV is traveling in lane L2, and surrounding vehicle SV wants to pass from the left side of other vehicle OV. Figure 2 The vehicle (A) squeezes past and overtakes other vehicles (OV) from the left side. Specifically, other vehicles (OV) pass from position P2 on lane L2. In order for vehicle 1 to drive safely without colliding with other vehicles (OV), the other vehicle action prediction device 15... Figure 2 The actions of other vehicles (OV) after the time point shown in (A) are predicted.

[0042] Specifically, in Figure 2 In the example shown in (A), the sensor information acquisition unit 3C acquires information indicating that the surrounding condition sensor (more specifically, the surrounding condition sensor that can detect the surrounding conditions of other vehicles OV, such as a rear radar, a rear camera, etc.) is not equipped on other vehicles OV.

[0043] The Other Vehicle Action Prediction Unit 3F predicts the actions of other vehicles based on information obtained by the Sensor Information Acquisition Unit 3C that the surrounding condition sensor (specifically, the surrounding condition sensor capable of detecting surrounding vehicles SV) is not equipped on other vehicles OV, the detection results DRO (specifically, the trajectory of the position of other vehicles OV from the time point when other vehicles OV pass through position P1 to the time point when other vehicles OV pass through position P2), and the detection results DRS (time series detection results of surrounding vehicles SV) obtained by the surrounding condition sensor 11.

[0044] In detail, the Other Vehicle Action Prediction Unit 3F predicts that there is a 30% probability that another vehicle OV will change lanes from lane L2 to lane L3, a 60% probability that another vehicle OV will not change lanes and will continue to travel in lane L2, and a 10% probability that another vehicle OV will change lanes from lane L2 to lane L1.

[0045] exist Figure 2 In the example shown in (B), vehicle 1 is traveling in lane L1, other vehicle OV is traveling in lane L2, and surrounding vehicle SV wants to pass from the left side of other vehicle OV. Figure 2 The vehicle (B) squeezes past and overtakes other vehicles (OV) from the left side. Specifically, other vehicles (OV) pass from position P2 on lane L2. In order for vehicle 1 to drive safely without colliding with other vehicles (OV), the other vehicle movement prediction device 15... Figure 2 The actions of other vehicles OV after the time point shown in (B) are predicted.

[0046] Specifically, in Figure 2 In the example shown in (B), the sensor information acquisition unit 3C acquires information indicating that a surrounding condition sensor (more specifically, a surrounding condition sensor capable of detecting surrounding vehicles SV, such as a rear radar, a rear camera, etc.) is equipped on the other vehicle OV.

[0047] The Other Vehicle Action Prediction Unit 3F predicts the actions of other vehicles based on information obtained by the Sensor Information Acquisition Unit 3C from surrounding condition sensors (specifically, surrounding condition sensors capable of detecting surrounding vehicles SV) equipped on other vehicles OV, the detection results DRO (specifically, the trajectory of the position of other vehicles OV from the time point when other vehicles OV pass through position P1 to the time point when other vehicles OV pass through position P2), and the detection results DRS (time series detection results of surrounding vehicles SV) obtained by the surrounding condition sensor 11.

[0048] In detail, the Other Vehicle Action Prediction Unit 3F predicts that there is a 10% probability that another vehicle OV will change lanes from lane L2 to lane L3, a 20% probability that another vehicle OV will not change lanes and will continue to travel in lane L2, and a 70% probability that another vehicle OV will change lanes from lane L2 to lane L1.

[0049] exist Figure 2 (A) and Figure 2In the example shown in (B), when the sensor information acquisition unit 3C acquires information indicating that the surrounding condition sensor (specifically, the surrounding condition sensor capable of detecting surrounding vehicles SV) is not equipped with the other vehicle OV, the other vehicle action prediction unit 3F predicts the other vehicle OV's actions (the probability that the other vehicle OV will change lanes from lane L2 to lane L3 is 30%, the probability that the other vehicle OV will not change lanes and continue driving in lane L2 is 60%, and the probability that the other vehicle OV will change lanes from lane L2 to lane L1 is...). The 10% probability is different from the other vehicle action prediction unit 3F's prediction of the other vehicle's actions when the sensor information acquisition unit 3C acquires information about the surrounding conditions of other vehicles (specifically, a surrounding condition sensor that can detect surrounding vehicles SV) that is equipped with other vehicle OV. The prediction is based on the following: the probability that the other vehicle OV will change lanes from lane L2 to lane L3 is 10%; the probability that the other vehicle OV will not change lanes and will continue to travel in lane L2 is 20%; and the probability that the other vehicle OV will change lanes from lane L2 to lane L1 is 70%.

[0050] exist Figure 2 (A) and Figure 2 In the example shown in (B), the trajectory of the position of other vehicle OV from the time point when other vehicle OV passes through position P1 to the time point when other vehicle OV passes through position P2 is used as the detection result DRO of the time series of other vehicle OV obtained by the surrounding condition sensor 11. However, in another example, the detection result of the orientation of other vehicle OV from the time point when other vehicle OV passes through position P1 to the time point when other vehicle OV passes through position P2 can also be used as the detection result DRO of the time series of other vehicle OV obtained by the surrounding condition sensor 11.

[0051] In another example, the detection results of the speed, acceleration, braking mode, etc. of other vehicle OV from the time point when other vehicle OV passes through position P1 to the time point when other vehicle OV passes through position P2 can also be used as the detection result DRO of the time series of other vehicle OV obtained by the surrounding condition sensor 11.

[0052] exist Figure 1In the example shown, the other vehicle action prediction unit 3F uses a prediction model to predict the likelihood of another vehicle OV changing lanes. This prediction is based on information obtained by the sensor information acquisition unit 3C indicating whether a surrounding condition sensor (specifically, a surrounding condition sensor capable of detecting surrounding vehicles SV) is equipped on the other vehicle OV, or information indicating the configuration of the surrounding condition sensor equipped on the other vehicle OV that detects the surrounding conditions of the other vehicle OV, and information obtained by the surrounding vehicle information acquisition unit 3D indicating whether a surrounding vehicle SV exists. The prediction model is obtained by learning using training data, which is a dataset containing labels indicating whether a learning surrounding condition sensor (not shown) that detects the surrounding conditions of the learning other vehicle (not shown) is equipped on the learning other vehicle, or information indicating the configuration of the learning surrounding condition sensor equipped on the learning other vehicle, information indicating whether a learning surrounding vehicle (not shown) is driving around the learning other vehicle, and information indicating whether the learning other vehicle has changed lanes.

[0053] Specifically, in Figure 2 In the example shown in (A), the Other Vehicle Action Prediction Unit 3F predicts a 40% probability that the other vehicle (OV) will change lanes, and a 60% probability that the other vehicle (OV) will not change lanes. Figure 2 In the example shown in (B), the Other Vehicle Action Prediction Unit 3F predicts that there is an 80% probability that the Other Vehicle OV will change lanes, and a 20% probability that the Other Vehicle OV will not change lanes.

[0054] In another example, other vehicle action prediction units 3F can also be used by using [the technology / method] with [other technologies / methods]. Figure 1 The example shown uses different methods to obtain prediction models that predict the likelihood of other vehicles (OVs) changing lanes. These prediction models are based on information obtained by the sensor information acquisition unit 3C indicating whether a surrounding condition sensor that detects the surrounding conditions of other vehicles (OVs) is equipped on the other vehicle (OV), or information indicating the configuration of a surrounding condition sensor equipped on the other vehicle (OV) that detects the surrounding conditions of other vehicles (OVs), and information obtained by the surrounding vehicle information acquisition unit 3D indicating whether a surrounding vehicle (SV) exists.

[0055] Figure 3 This is a flowchart illustrating an example of a process performed by the processor 153 of another vehicle movement prediction device 15 of the first embodiment.

[0056] exist Figure 3 In the example shown, in step S10, the acquisition unit 3A acquires the detection results of the surrounding conditions of the vehicle 1.

[0057] In step S11, the sensor information acquisition unit 3C acquires information indicating whether a surrounding condition sensor for detecting the surrounding conditions of other vehicles (OVs) is equipped on the other vehicles (OVs) or information indicating the configuration of the surrounding condition sensor equipped on the other vehicles (OVs).

[0058] In step S12, the other vehicle action prediction unit 3F predicts the action of the other vehicle OV based on information obtained in step S11 indicating whether a surrounding condition sensor that detects the surrounding condition of the other vehicle OV is equipped on the other vehicle OV or information indicating the configuration of the surrounding condition sensor equipped on the other vehicle OV.

[0059] In the other vehicle action prediction device 15 of the first embodiment, the action of other vehicles can be predicted with high accuracy compared to the case where the prediction of the action of other vehicles is not based on whether a surrounding condition sensor that detects the surrounding conditions of other vehicles is equipped with other vehicles or the configuration of a surrounding condition sensor equipped with other vehicles.

[0060] <Second Implementation> The vehicle 1 that applies the other vehicle movement prediction device 15 of the second embodiment is configured in the same way as the vehicle 1 that applies the other vehicle movement prediction device 15 of the first embodiment, except for the points described later.

[0061] Figure 4 (A) and Figure 4 Figure (B) is an example of the detection results DRO (Time Series of Other Vehicles OV) obtained by the surrounding situation sensor 11, the detection results DRS (Time Series of Surrounding Vehicles SV) obtained by the surrounding situation sensor 11, and the prediction results of the actions of other vehicles OV obtained by the other vehicle action prediction unit 3F of the other vehicle action prediction device 15 of the second embodiment. In detail, Figure 4 (A) represents an example of the prediction result of the action of other vehicle OV obtained by the other vehicle action prediction unit 3F when the surrounding condition sensor that detects the surrounding condition (surrounding vehicle SV) of other vehicle OV is not equipped on the other vehicle OV. Figure 4 (B) represents an example of the prediction result of the action of the other vehicle OV obtained by the other vehicle action prediction unit 3F when the surrounding condition sensor that detects the surrounding condition (surrounding vehicle SV) of the other vehicle OV is equipped with the other vehicle OV.

[0062] exist Figure 4In the example shown in (A), vehicle 1 is traveling in lane L2, other vehicle OV is traveling in front of vehicle 1, and surrounding vehicle SV wants to pass from the left side of other vehicle OV. Figure 4 The vehicle (A) squeezes past and overtakes other vehicles (OV) from the left side. Specifically, other vehicles (OV) pass from position P2 on lane L2. In order for vehicle 1 to drive safely without colliding with other vehicles (OV), the other vehicle action prediction device 15... Figure 4 The actions of other vehicles (OV) after the time point shown in (A) are predicted.

[0063] Specifically, in Figure 4 In the example shown in (A), the sensor information acquisition unit 3C acquires information indicating that the surrounding condition sensor (more specifically, the surrounding condition sensor that can detect the surrounding conditions of other vehicles OV, such as a rear radar, a rear camera, etc.) is not equipped on other vehicles OV.

[0064] The Other Vehicle Action Prediction Unit 3F predicts the actions of other vehicles based on information obtained by the Sensor Information Acquisition Unit 3C that the surrounding condition sensor (specifically, the surrounding condition sensor capable of detecting surrounding vehicles SV) is not equipped on other vehicles OV, the detection results DRO (specifically, the trajectory of the position of other vehicles OV from the time point when other vehicles OV pass through position P1 to the time point when other vehicles OV pass through position P2), and the detection results DRS (time series detection results of surrounding vehicles SV) obtained by the surrounding condition sensor 11.

[0065] Specifically, the Other Vehicle Action Prediction Unit 3F predicts a 20% probability that the Other Vehicle OV will accelerate, a 60% probability that the Other Vehicle OV will not accelerate or decelerate and will continue to maintain its speed, and a 20% probability that the Other Vehicle OV will decelerate.

[0066] exist Figure 4 In the example shown in (B), vehicle 1 is traveling in lane L2, other vehicle OV is traveling in front of vehicle 1, and surrounding vehicle SV wants to pass from the left side of other vehicle OV. Figure 4 The vehicle (B) squeezes past and overtakes other vehicles (OV) from the left side. Specifically, other vehicles (OV) pass from position P2 on lane L2. In order for vehicle 1 to drive safely without colliding with other vehicles (OV), the other vehicle movement prediction device 15... Figure 4 The actions of other vehicles OV after the time point shown in (B) are predicted.

[0067] Specifically, in Figure 4 In the example shown in (B), the sensor information acquisition unit 3C acquires information indicating that a surrounding condition sensor (more specifically, a surrounding condition sensor capable of detecting surrounding vehicles SV, such as a rear radar, a rear camera, etc.) is equipped on the other vehicle OV.

[0068] The Other Vehicle Action Prediction Unit 3F predicts the actions of other vehicles based on information obtained by the Sensor Information Acquisition Unit 3C from surrounding condition sensors (specifically, surrounding condition sensors capable of detecting surrounding vehicles SV) equipped on other vehicles OV, the detection results DRO (specifically, the trajectory of the position of other vehicles OV from the time point when other vehicles OV pass through position P1 to the time point when other vehicles OV pass through position P2), and the detection results DRS (time series detection results of surrounding vehicles SV) obtained by the surrounding condition sensor 11.

[0069] In detail, the Other Vehicle Action Prediction Unit 3F predicts that there is a 20% probability that the Other Vehicle OV will accelerate, a 20% probability that the Other Vehicle OV will not accelerate or decelerate and will continue to maintain its speed, and a 60% probability that the Other Vehicle OV will decelerate (that is, allow surrounding vehicles SV to overtake the Other Vehicle OV).

[0070] <Third Implementation Method> The vehicle 1 that applies the other vehicle movement prediction device 15 of the third embodiment is configured in the same way as the vehicle 1 that applies the other vehicle movement prediction device 15 of the first embodiment or the second embodiment, except for the points described later.

[0071] As described above, in the vehicle 1 using the other vehicle action prediction device 15 of the first embodiment, the vehicle control device 14 has an autonomous driving function that allows the vehicle 1 to drive autonomously without the driver needing to operate the steering actuator 14A, brake actuator 14B, and drive actuator 14C. Specifically, the vehicle control device 14 generates a driving plan for the vehicle 1 to reach its destination, for example, based on map information, the location information of the vehicle 1, and information indicating the destination of the vehicle 1. Furthermore, the vehicle control device 14 drives the vehicle 1 autonomously according to this driving plan. More specifically, the vehicle control device 14 uses the detection results of the surrounding condition sensor 11 and other vehicle OV (OV) obtained by the other vehicle action prediction device 15 (see reference 15) to determine the location of the vehicle 1. Figure 2 (A) and Figure 2The vehicle 1 can drive autonomously by modifying its driving plan in a way that avoids collisions with other vehicles such as OV, etc., based on the predicted results of the actions of (B).

[0072] On the other hand, in the vehicle 1 that applies the other vehicle movement prediction device 15 of the third embodiment, the vehicle control device 14 has a driving assistance function. Specifically, the vehicle control device 14, based on the detection results of the surrounding situation sensor 11 and the other vehicle movement prediction device 15, obtains the other vehicle movement (OV) (refer to...) Figure 2 (A) and Figure 2 If the predicted result of the action of (B) is such that it is necessary to avoid collisions between this vehicle 1 and other vehicles OV, an alarm indicating the subject matter is output to HMI13.

[0073] As described above, embodiments of other vehicle motion prediction devices, other vehicle motion prediction methods, and program products of this disclosure have been described with reference to the accompanying drawings. However, other vehicle motion prediction devices, other vehicle motion prediction methods, and program products of this disclosure are not limited to the above embodiments, and appropriate modifications can be made without departing from the spirit of this disclosure. The configurations of the various examples of the above embodiments can also be appropriately combined. In the examples of the above embodiments, the processing performed in the other vehicle motion prediction device 15 has been described as software processing performed by executing a program, but the processing performed in the other vehicle motion prediction device 15 can also be hardware processing. Alternatively, the processing performed in the other vehicle motion prediction device 15 can be a combination of software and hardware processing. Furthermore, the program stored in the memory 152 of the other vehicle motion prediction device 15 (the program that implements the functions of the processor 153 of the other vehicle motion prediction device 15) can be provided, circulated, etc., for example, recorded on a computer-readable storage medium (program product) such as a semiconductor memory, magnetic recording medium, or optical recording medium.

Claims

1. An additional vehicle movement prediction device, comprising a processor, wherein, The processor is configured to: Predicting the actions of other vehicles traveling around this vehicle; and Acquire information indicating whether a surrounding condition sensor for detecting the surrounding conditions of the other vehicle is equipped on the other vehicle, or information indicating the configuration of the surrounding condition sensor equipped on the other vehicle. The processor predicts the actions of the other vehicles based on information indicating whether the surrounding condition sensors are equipped on the other vehicles or information indicating the configuration of the surrounding condition sensors equipped on the other vehicles.

2. The other vehicle movement prediction device according to claim 1, wherein, The predicted actions of other vehicles when information indicating that the surrounding condition sensors are not equipped on the other vehicles are different from the predicted actions of other vehicles when information indicating that the surrounding condition sensors are equipped on the other vehicles are obtained.

3. The other vehicle movement prediction device according to claim 1, wherein, The processor is configured to: Obtain information indicating whether there are surrounding vehicles traveling around the other vehicle; By using a predictive model, the likelihood of another vehicle changing lanes is predicted based on information indicating whether the surrounding condition sensors are equipped with the other vehicle, or information indicating the configuration of the surrounding condition sensors equipped with the other vehicle, and information indicating the presence of the surrounding vehicle. The predictive model is obtained through learning using training data, which is a dataset of labels indicating whether the learning surrounding condition sensors used to detect the surrounding conditions of the learning other vehicle are equipped with the learning other vehicle, or information indicating the configuration of the learning surrounding condition sensors equipped with the learning other vehicle, information indicating the presence of learning surrounding vehicles traveling around the learning other vehicle, and information indicating whether the learning other vehicle has changed lanes.

4. The other vehicle movement prediction device according to claim 1, wherein, The processor is configured to: Based on images of other vehicles captured by a camera mounted on the vehicle, information is generated indicating whether the surrounding condition sensor is equipped on the other vehicles or information indicating the configuration of the surrounding condition sensor equipped on the other vehicles; and Acquire information indicating whether the surrounding condition sensor is equipped on the other vehicle or information indicating the configuration of the surrounding condition sensor equipped on the other vehicle.

5. The other vehicle movement prediction device according to claim 1, wherein, The processor acquires information obtained through communication between the vehicle and the outside of the vehicle, indicating whether the surrounding condition sensor is equipped on the other vehicle or the configuration of the surrounding condition sensor equipped on the other vehicle.

6. The other vehicle movement prediction device according to claim 1, wherein, The processor is configured to: Based on radio waves detected by a radar detector mounted on the vehicle and transmitted from the surrounding condition sensor, information indicating whether the surrounding condition sensor is equipped on the other vehicle or information indicating the configuration of the surrounding condition sensor equipped on the other vehicle is generated; and Acquire information indicating whether the surrounding condition sensor is equipped on the other vehicle or information indicating the configuration of the surrounding condition sensor equipped on the other vehicle.

7. Another method for predicting vehicle movement, comprising: Predict the movements of other vehicles traveling around this vehicle; as well as Acquire information indicating whether a surrounding condition sensor for detecting the surrounding conditions of the other vehicle is equipped on the other vehicle, or information indicating the configuration of the surrounding condition sensor equipped on the other vehicle. In the other vehicle action prediction methods, The actions of the other vehicles are predicted based on information indicating whether the surrounding condition sensors are equipped on the other vehicles or information indicating the configuration of the surrounding condition sensors equipped on the other vehicles.

8. A program product having a computer program recorded thereon, the computer program being configured to cause a processor to perform the following actions: Predicting the actions of other vehicles traveling around this vehicle; and Acquire information indicating whether a surrounding condition sensor for detecting the surrounding conditions of the other vehicle is equipped on the other vehicle, or information indicating the configuration of the surrounding condition sensor equipped on the other vehicle, wherein... The actions of the other vehicles are predicted based on information indicating whether the surrounding condition sensors are equipped on the other vehicles or information indicating the configuration of the surrounding condition sensors equipped on the other vehicles.

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