System and method for vehicle collision mitigation with vulnerable road user context sensing

• US 9,896,030 B2
• Filed: 04/30/2015
• Issued: 02/20/2018
• Est. Priority Date: 04/30/2015
• Status: Active Grant

• Current Assignee: Honda Motor Co., Ltd. (Honda Motor Company)
• Original Assignee: Honda Motor Co., Ltd. (Honda Motor Company)
• Inventors: Sugimoto, Yoichi
• Primary Examiner(s): Khan, Omer S
• Application Number:
  US14/700,223
• Publication Number:
  US 20160318445A1
• Time in Patent Office: 1,027 Days
• Field of Search: 340435
• CPC Class Codes: B60Q 9/008 for anti-collision purposes
• CPC Class Codes: B60W 2554/4029 Pedestrians
• CPC Class Codes: B60W 2554/4042 Longitudinal speed
• CPC Class Codes: B60W 30/08 Active safety systems predicting or avoiding probable or impending collision or attempting to minimise its consequences
• CPC Class Codes: B60W 30/09 Taking automatic action to avoid collision, e.g. braking and steering
• CPC Class Codes: B60W 50/14 Means for informing the driver, warning the driver or prompting a driver intervention
• CPC Class Codes: B60W 60/0027 using trajectory prediction for other traffic participants
• CPC Class Codes: G06F 3/00 Input arrangements for transferring data to be processed into a form capable of being handled by the computer; Output arrangements for transferring data from processing unit to output unit, e.g. interface arrangements
• CPC Class Codes: G06N 3/006 based on simulated virtual individual or collective life forms, e.g. social simulations or particle swarm optimisation [PSO]
• CPC Class Codes: G06V 20/58 Recognition of moving objects or obstacles, e.g. vehicles or pedestrians; Recognition of traffic objects, e.g. traffic signs, traffic lights or roads
• CPC Class Codes: G08G 1/005 including pedestrian guidance indicator
• CPC Class Codes: G08G 1/166 for active traffic, e.g. moving vehicles, pedestrians, bikes
• CPC Class Codes: H04L 67/12 specially adapted for proprietary or special-purpose networking environments, e.g. medical networks, sensor networks, networks in vehicles or remote metering networks
• CPC Class Codes: H04L 67/52 specially adapted for the location of the user terminal
• CPC Class Codes: H04W 4/48 for in-vehicle communication

Abstract

A system and method for vehicle collision mitigation with vulnerable road user context sensing. The system and method include determining one or more biosignal parameters and one or more physical movement parameters associated with a vulnerable road user (VRU). The system and method also include determining a context of the VRU based on the one or more biosignal parameters and one or more physical movement parameters associated with the VRU. Additionally, the system and method include determining one or more physical movement parameters associated with a vehicle. The system and method further include estimating a probability of collision between the VRU and the vehicle based on the context of the VRU, the one or more physical movement parameters associated with the VRU, and the one or more physical movement parameters associated with the vehicle. The system and method also include providing a human machine interface output response based on the estimation of probability of collision between the VRU and the vehicle, wherein the human machine interface output response is provided on at least one of the following: a head unit of the vehicle, a wearable computing device, and a portable device.

Claims

1. A computer-implemented method for vehicle collision mitigation with vulnerable road user context sensing, comprising:

determining one or more biosignal parameters and one or more physical movement parameters associated with a vulnerable road user (VRU), wherein the one or more biosignal parameters associated with the VRU include data pertaining to at least one of;

a heart rate, a respiration rate, and a blood pressure associated with the VRU, wherein the one or more physical movement parameters associated with the VRU include data pertaining to a location and movement of the VRU, wherein determining the one or more biosignal parameters and the one or more physical movement parameters associated with the VRU includes determining exercise threshold values of the VRU, wherein it is determined if a requisite amount of biosignal parameter data and physical movement parameter data have been received to determine the exercise threshold values of the VRU, wherein upon determining that the requisite amount of biosignal parameter data and physical movement parameter data have been received, the biosignal parameter data and extracted velocity data of the VRU are aggregated to determine average biosignal parameter values while the VRU is moving at particular velocities;

determining a context of the VRU based on the one or more biosignal parameters and one or more physical movement parameters associated with the VRU;

determining one or more physical movement parameters associated with a vehicle, wherein the one or more physical movement parameters associated with the vehicle include data pertaining to a location and movement of the vehicle;

estimating a probability of collision between the VRU and the vehicle based on the context of the VRU, the one or more physical movement parameters associated with the VRU, and the one or more physical movement parameters associated with the vehicle; and

providing a human machine interface output response based on the estimation of the probability of collision between the VRU and the vehicle, wherein the human machine interface output response is provided on at least one of the following;

a head unit of the vehicle, a wearable computing device, and a portable device includes controlling a braking system of the vehicle to decelerate a speed of the vehicle in a manner that corresponds to the estimation of the probability of collision between the VRU and the vehicle.

2. The computer-implemented method of claim 1, wherein determining the one or more biosignal parameters and one or more physical movement parameters associated with the VRU includes at least one of:

a wearable computing device sensing one or more biosignal parameters associated with the VRU, the wearable computing device sensing one or more physical movement parameters associated with the VRU, and a portable device sensing one or more physical movement parameters associated with the VRU, wherein the one or more biosignal parameters and the one or more physical movement parameters are sensed and stored for a predetermined amount of time on at least one of;

a storage unit of the wearable computing device and a storage unit of the portable device.

3. The computer-implemented method of claim 2, wherein the exercise threshold values includes at least one of:

a resting exercise threshold value, an active exercise threshold value, or a hyperactive exercise threshold value, wherein the exercise threshold values of the VRU are based on analyzing the one or more biosignal parameters associated with the VRU that are sensed and stored for the predetermined amount of time.

4. The computer-implemented method of claim 2, wherein determining the one or more biosignal parameters and the one or more physical movement parameters associated with the VRU includes determining velocity threshold values of the VRU, wherein the velocity threshold values includes at least one of:

a resting velocity threshold value, a walking velocity threshold value, or a running velocity threshold value, wherein the velocity threshold values of the VRU are based on analyzing the physical movement parameters associated with the VRU that are sensed and stored for the predetermined amount of time.

5. The computer-implemented method of claim 4, wherein determining the context of the VRU includes receiving the one or more biosignal parameters and the one or more physical movement parameters associated with the VRU in real time and determining if the one or more biosignal parameters are greater than one or more exercise threshold values and determining if the one or more physical movement parameters are greater than the velocity threshold values of the VRU, wherein the context of the VRU includes at least one of:

a walking context, a running context, a biking context, or a passenger context.

6. The computer-implemented method of claim 5, wherein estimating the probability of collision between the VRU and the vehicle includes determining an overlap between future expected positions of the VRU and future expected positions of the vehicle, wherein the future expected positions of the VRU are determined by analyzing one or more physical movement parameters associated with the VRU in real time, wherein the future expected positions of the vehicle is determined by analyzing one or more physical movement parameters associated with the vehicle in real time.

7. The computer-implemented method of claim 6, wherein estimating the probability of collision between the VRU and the vehicle includes evaluating the context of the VRU, a velocity of the VRU in real time, and a velocity of the vehicle in a real time with respect to the determined overlap between the future expected positions of the VRU and the future expected positions of the vehicle.

8. The computer-implemented method of claim 6, wherein estimating the probability of collision between the VRU and the vehicle includes evaluating one or more collision probability factors to determine at least one of:

a probability that the overlap of estimated future expected positions will result in a collision and a predicted timeframe at which the VRU and the vehicle may collide.

9. The computer-implemented method of claim 1, wherein providing the human machine interface output response includes controlling the human machine interface output response to provide an output response that correspond to the adjusted estimated probability of collision between the VRU and the vehicle, wherein the human machine interface output response includes a warning provided through a display device, an audio device, or a haptic device within the vehicle.

10. A system for providing vehicle collision mitigation with vulnerable road user context sensing, comprising:

a vulnerable road user (VRU) vehicle collision mitigation application that is executed on at least one of;

a wearable computing device worn by and/or in possession of the vulnerable road user, a portable device in possession of the VRU, and a head unit of a vehicle, wherein the wearable computing device includes biosignal sensors and physical signal sensors, the portable device includes physical signal sensors, and the vehicle includes vehicle sensors;

a VRU bio-movement learning module that is included as a module of the VRU vehicle collision mitigation application that determines one or more biosignal parameters and physical movement parameters associated with the VRU, wherein the one or more biosignal parameters associated with the VRU include data pertaining to at least one of;

a heart rate, a respiration rate, and a blood pressure associated with the VRU, wherein the one or more physical movement parameters associated with the VRU include data pertaining to a location and movement of the VRU, wherein determining the one or more biosignal parameters and the one or more physical movement parameters associated with the VRU includes determining exercise threshold values of the VRU, wherein it is determined if a requisite amount of biosignal parameter data and physical movement parameter data have been received to determine the exercise threshold values of the VRU, wherein upon determining that the requisite amount of biosignal parameter data and physical movement parameter data have been received, the biosignal parameter data and extracted velocity data of the VRU are aggregated to determine average biosignal parameter values while the VRU is moving at particular velocities;

a VRU context determination module that is included as a module of the VRU vehicle collision mitigation application that determines a context of the VRU based on the one or more biosignal parameters and the physical movement parameters associated with the VRU;

a vehicle physical movement determination module that is included as a module of the VRU vehicle collision mitigation application that determines one or more physical movement parameters associated with the vehicle, wherein the one or more physical movement parameters associated with the vehicle include data pertaining to a location and movement of the vehicle;

a collision probability estimation module that is included as a module of the VRU vehicle collision mitigation application that estimates a probability of collision between the VRU and the vehicle based on the context of the VRU, the one or more physical movement parameters associated with the VRU, and the one or more physical movement parameters associated with the vehicle; and

a human machine interface (HMI) output control module that is included as a module of the VRU vehicle collision mitigation application that provides a human machine interface output response based on the estimation of the probability of collision between the VRU and the vehicle, wherein the human machine interface output response is provided on at least one of the following;

the head unit of the vehicle, the wearable device, and the portable device includes controlling a braking system of the vehicle to decelerate a speed of the vehicle in a manner that corresponds to the estimation of the probability of collision between the VRU and the vehicle.

11. The system of claim 10, wherein the VRU bio-movement learning module accesses at least one of:

the biosignal sensors of the wearable computing device, the physical signal sensors of the wearable computing device, and the physical signal sensors of the portable device, wherein the biosignal sensors of the wearable computing device sense the one or more biosignal parameters associated with the VRU, the physical signal sensors of the wearable computing device sense the one or more physical movement parameters associated with the VRU, and the physical signal sensors of the portable device sense the one or more physical movement parameters associated with the VRU, wherein the one or more biosignal parameters and the one or more physical movement parameters are sensed and stored for a predetermined amount of time on at least one of;

a storage unit of the wearable computing device and a storage unit of the portable device.

12. The system of claim 11, wherein the VRU bio-movement learning module determines the exercise threshold values of the VRU, wherein the exercise threshold values includes at least one of:

a resting exercise threshold value, an active exercise threshold value, or a hyperactive exercise threshold value, wherein the exercise threshold values of the VRU are based on analyzing the one or more biosignal parameters associated with the VRU that are sensed and stored for the predetermined amount of time.

13. The system of claim 12, wherein the VRU bio-movement learning module determines velocity threshold values of the VRU, wherein the velocity threshold values includes at least one of:

a resting velocity threshold value, a walking velocity threshold value, or a running velocity threshold value, wherein the velocity threshold values of the VRU are based on analyzing the one or more physical movement parameters associated with the VRU that are sensed and stored for the predetermined amount of time.

14. The system of claim 13, wherein the VRU context determination module receives one or more biosignal parameters and one or more physical movement parameters associated with the VRU in real time from at least one of:

the biosignal sensors of the wearable computing device, the physical signal sensors of the wearable computing device, or the physical signal sensors of the portable device, wherein the VRU context determination module determines if the one or more biosignal parameters are greater than one or more of the exercise threshold values of the VRU and the VRU context determination module determines if the one or more physical movement parameters are greater than one or more of the velocity threshold values of the VRU, wherein a context of the VRU includes at least one of;

a walking context, a running context, a biking context, or a passenger context.

15. The system of claim 14, wherein the collision probability estimation module determines an overlap between future expected positions of the VRU and future expected positions of the vehicle, wherein the future expected positions of the VRU are determined by analyzing one or more physical movement parameters associated with the VRU in real time, wherein the future expected positions of the vehicle are determined by analyzing one or more physical movement parameters associated with the vehicle in real time.

16. The system of claim 15, wherein the collision probability estimation module evaluates the context of the VRU, a velocity of the VRU in real time, and a velocity of the vehicle in real time with respect to the determined overlap between the future expected positions of the VRU and the future expected positions of the vehicle.

17. The system of claim 15, wherein the collision probability estimation module evaluates one or more collision probability factors to determine a probability that the overlap of estimated future expected positions will result in a collision and a predicted timeframe at which the VRU and the vehicle may collide.

18. The system of claim 10, wherein the HMI output control module controls the human machine interface output response to provide an output response that correspond to the adjusted estimated portability of collision between the VRU and the vehicle, wherein the human machine interface output response includes a warning provided through a display device, an audio device, or a haptic device within the vehicle.

19. A non-transitory computer-readable storage medium storing instructions that when executed by a processor of a computer, causes the processor of the computer to implement a method perform actions, comprising:

determining one or more biosignal parameters and one or more physical movement parameters associated with a vulnerable road user (VRU), wherein the one or more biosignal parameters associated with the VRU include data pertaining to at least one of;

a heart rate, a respiration rate, and a blood pressure associated with the VRU, wherein the one or more physical movement parameters associated with the VRU include data pertaining to a location and movement of the VRU, wherein determining the one or more biosignal parameters and the one or more physical movement parameters associated with the VRU includes determining exercise threshold values of the VRU, wherein it is determined if a requisite amount of biosignal parameter data and physical movement parameter data have been received to determine the exercise threshold values of the VRU, wherein upon determining that the requisite amount of biosignal parameter data and physical movement parameter data have been received, the biosignal parameter data and extracted velocity data of the VRU are aggregated to determine average biosignal parameter values while the VRU is moving at particular velocities;

determining a context of the VRU based on the one or more biosignal parameters and one or more physical movement parameters associated with the VRU;

determining one or more physical movement parameters associated with a vehicle, wherein the one or more physical movement parameters associated with the vehicle include data pertaining to a location and movement of the vehicle;

estimating a probability of collision between the VRU and the vehicle based on the context of the VRU, the one or more physical movement parameters associated with the VRU, and the one or more physical movement parameters associated with the vehicle; and

providing a human machine interface output response based on the estimation the of probability of collision between the VRU and the vehicle, wherein the human machine interface output response is provided on at least one of the following;

a head unit of the vehicle, a wearable device, and a portable device includes controlling a braking system of the vehicle to decelerate a speed of the vehicle in a manner that corresponds to the estimation of the probability of collision between the VRU and the vehicle.

20. The non-transitory computer readable storage medium of claim 19, wherein estimating the probability of collision between the VRU and the vehicle includes determining an overlap between future expected positions of the VRU and future expected positions of the vehicle and evaluating the context of the VRU, a real time velocity of the VRU, and a real time velocity of the vehicle with respect to a determined overlap between the future expected positions of the VRU and the future expected positions of the vehicle.