Vehicle with computing means for monitoring and predicting traffic participant objects

• US 9,104,965 B2
• Filed: 01/07/2013
• Issued: 08/11/2015
• Est. Priority Date: 01/11/2012
• Status: Active Grant

• Current Assignee: Honda Research Institute Europe GmbH (Honda Motor Company)
• Original Assignee: Honda Research Institute Europe GmbH (Honda Motor Company)
• Inventors: Fritsch, Jan, Butz, Martin, Alin, Andreas
• Primary Examiner(s): Vincent, David
• Assistant Examiner(s): Kim, David H
• Application Number:
  US13/735,064
• Publication Number:
  US 20130179382A1
• Time in Patent Office: 946 Days
• Field of Search: None
• US Class Current: 1/1
• CPC Class Codes: B60W 2050/0052 Filtering, filters
• CPC Class Codes: B60W 2050/0075 Automatic parameter input, automatic initialising or calibrating means
• CPC Class Codes: B60W 2050/143 Alarm means
• CPC Class Codes: B60W 2050/146 Display means
• CPC Class Codes: B60W 2520/10 Longitudinal speed
• CPC Class Codes: B60W 2554/00 Input parameters relating to objects
• CPC Class Codes: B60W 2554/4026 Cycles
• CPC Class Codes: B60W 2554/4029 Pedestrians
• CPC Class Codes: B60W 2554/4041 Position
• CPC Class Codes: B60W 2554/80 Spatial relation or speed relative to objects
• CPC Class Codes: B60W 2556/50 of positioning data, e.g. GPS [Global Positioning System] data
• CPC Class Codes: B60W 2556/55 using telemetry
• CPC Class Codes: B60W 2556/65 Data transmitted between vehicles
• CPC Class Codes: B60W 30/095 Predicting travel path or likelihood of collision
• CPC Class Codes: B60W 30/12 Lane keeping
• CPC Class Codes: B60W 40/04 Traffic conditions
• CPC Class Codes: B60W 50/0097 Predicting future conditions
• CPC Class Codes: B60W 50/14 Means for informing the driver, warning the driver or prompting a driver intervention
• CPC Class Codes: B60W 50/16 Tactile feedback to the driver, e.g. vibration or force feedback to the driver on the steering wheel or the accelerator pedal
• CPC Class Codes: B60W 60/0027 using trajectory prediction for other traffic participants
• CPC Class Codes: G06N 5/02 : Knowledge representation; Symbolic representation
• CPC Class Codes: G06N 7/01 : Probabilistic graphical models, e.g. probabilistic networks
• CPC Class Codes: G08G 1/161 : Decentralised systems, e.g. inter-vehicle communication

Abstract

An anticipatory monitoring and prediction system can include methods for generating effective, accurate predictions of other traffic objects in the vicinity of an ego-car. The invention proposes to combine approximate probability distributions (ADPs) of agent states with Attractor Functions (AFs) for generating distributed probabilistic representations of the potential future states of the observed traffic objects. AFs are selected based on both the current road context, in which the ego-car is situated, and the current states of all participating objects. The generated predictions can be used to filter incoming sensory information for better object state estimations, rate the nature of the behavior of other traffic objects by comparing generated predictions with actual perceived sensor information, or infer accident likelihoods by comparing the predicted state distributions of objects and the ego-car. Warning and information signals or control commands can be issued in a driving assistance system.

Claims

1. A method for predicting a state of at least one physical traffic object, the method including the steps of:

generating sensorial information, based on the sensorial information, computing an approximate probability distribution of a current state of the at least one object represented in the sensorial information, and

predicting a future state of the at least one object by updating the approximate probability distribution using standard Bayesian filtering concepts,

the method being characterized by

using at least one attractor function to modify the predicting step,

wherein each attractor function represents a potential state trajectory from the current state to a potential future state determined according to context information.

2. The method according to claim 1, further including a step of

sensing with sensors for providing context information about surrounding objects for the at least one attractor function,

wherein the sensors are such as one or more cameras, radars, laser scanners, and/or Vehicle-to-Vehicle communication systems.

3. The method according to claim 1, further including a step of

environment sensing with sensors for providing context information about the static environment for the at least one attractor function,

wherein the sensors are such as one or more cameras, radars, laser scanners, GPS/navigation systems, or Infrastructure-to-Vehicle communication systems.

4. The method according to claim 1, further including steps of

performing continuously a plurality of future state predictions for the at least one object, and filtering each prediction with incoming sensory information, in order to obtain an improved determination of a new current state of the at least one object.

5. The method according to claim 1,

wherein a difference between a predicted future state and an observed future state of the at least one object enables the system to classify the behavior of the monitored object as normal or unusual.

6. The method according to claim 1,

wherein the prediction of the future state of the at least one object resulting in a predicted approximate probability distribution can be used to derive likely trajectories of movement for the at least one object.

7. The method according to claim 1,

wherein a comparison of the observed future state with the probability distribution of the current state can be used to infer which attractor function dominated the behavior of the at least one object.

8. The method according to claim 1,

wherein, as a function of the predicted future state of the at least one object, a visual or acoustic signal is generated, or an effector of a vehicle, such as a steering wheel, an accelerator, or a brake, is operated.

9. The method according to claim 1,

wherein the method is used for robotics safety in corresponding control systems.

10. A device for predicting a state of at least one object, wherein the device comprises:

Sensor means, preferably camera means, means, functionally connected to be supplied with signals from the sensor means, for determining an approximate probability distribution of a current state of the at least one object sensed by the sensor means, and

means for predicting a future state of the at least one object by updating the approximate probability distribution using standard Bayesian filtering concepts,

the device being characterized by

being adapted to use at least one attractor function to modify the predicting step,

wherein each attractor function represents a potential state trajectory from the current state to a potential future state determined according to context information.

11. Land, air or sea vehicle, preferably a car or motorcycle, provided on-board with a device of claim 10.