METHOD AND SYSTEM FOR PREDICTING MOVEMENT BEHAVIOR OF A TARGET TRAFFIC OBJECT

• US 20130054106A1
• Filed: 06/18/2012
• Published: 02/28/2013
• Est. Priority Date: 08/22/2011
• Status: Active Grant

• Current Assignee: Honda Research Institute Europe GmbH (Honda Motor Company)
• Original Assignee: Honda Research Institute Europe GmbH (Honda Motor Company)
• Inventors: SCHMDDERICH, Jens, REBHAN, Sven
• Granted Patent: US 8,903,588 B2
• Time in Patent Office: Days
• US Class Current: 701/96
• CPC Class Codes: B60W 2554/4042 Longitudinal speed
• CPC Class Codes: B60W 2554/4043 Lateral speed
• CPC Class Codes: B60W 30/0956 the prediction being responsive to traffic or environmental parameters
• CPC Class Codes: B60W 30/16 Control of distance between vehicles, e.g. keeping a distance to preceding vehicle
• CPC Class Codes: B60W 50/0097 Predicting future conditions
• CPC Class Codes: G01S 13/726 Multiple target tracking
• CPC Class Codes: G01S 13/931 of land vehicles
• CPC Class Codes: G01S 2013/9323 Alternative operation using light waves
• CPC Class Codes: G01S 2013/9324 Alternative operation using ultrasonic waves
• CPC Class Codes: G06F 18/256 of results relating to different input data, e.g. multimodal recognition
• 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: G06V 20/588 Recognition of the road, e.g. of lane markings; Recognition of the vehicle driving pattern in relation to the road
• CPC Class Codes: Y02T 10/84 Data processing systems or methods, management, administration

Abstract

A method for computationally predicting future movement behaviors of at least one target object can have the steps of

• • producing sensor data by at least one sensor physically sensing the environment of a host vehicle, and
  • computing a plurality of movement behavior alternatives of a target object sensed by the sensor(s).

The context based prediction step uses a set of classifiers, each classifier estimating a probability that said sensed target object will execute a movement behavior at a time. The method can also include validating the movement behavior alternatives by a physical prediction comparing measured points with trajectories of situation models and determining at least one trajectory indicating at least one possible behavior of the traffic participant, estimating at least one future position of the traffic participant based on the at least one trajectory, and

• • outputting a signal representing the estimate future position.

Claims

1. A method for computationally predicting future movement behaviors of at least one target object, said method comprising the steps of:

producing sensor data by at least one sensor physically sensing the environment of a host vehicle,

computing a plurality of movement behavior alternatives of a target object sensed by the sensor(s), by predicting movement behaviors of the traffic target vehicle applying a context based prediction step using indirect indicators or indicator combinations derived from said sensor data,

wherein said context based prediction step uses a set of classifiers, each classifier estimating a probability that said sensed target object will execute a movement behavior at a time, wherein the probability is calculated based on a feature vector calculated for each classifier,

validating the movement behavior alternatives by a physical prediction comparing measured points with trajectories of situation models and determining at least one trajectory indicating at least one possible behavior of the traffic participant, wherein the at least one movement behavior is a sub-set of the possible movement behavior alternatives,

estimating at least one future position of the traffic participant based on the at least one trajectory, and

outputting a signal representing the estimated future position.

2. The method of claim 1,

wherein the estimated future position signal is supplied to an actuator or display means of the vehicle.

3. The method of claim 1,

comprising a mismatch detection step detecting inconsistencies between the context based prediction and the physical prediction, and wherein based on the detected mismatch a fall-back vehicle operation function or a communication function for communicating information to a driver is activated.

4. The method of claim 3, wherein the fall-back vehicle operation function is a function that influences a vehicle'"'"'s controls in a default or predefined manner.

5. The method according to claim 1, wherein the communication function signals the driver with an alarm or informs the driver on the current situation.

6. The method according to claim 1, wherein specific indicators are contained in the situation models and are used to find out how well a situation model matches to the current situation.

7. The method according to claim 1, wherein a situation model is activated based on the current situation identified by information at least partially derived from sensor data or based on self-localization information of the host vehicle.

8. A driver assistance system, designed for being mounted on a host vehicle and executing the method according to claim 1.

9. The driver assistance system according to claim 8, comprising at least one of

a device for detecting traffic objects or a device for estimating their position and velocity,

a device for detecting road infrastructure elements,

a device for car-to-car communication,

a device for self-localization or a device for reading the host-vehicle'"'"'s state.

10. The driver assistance system according to claim 8, further comprising a model selector unit selecting and activating situation models based on information at least partially derived from sensor data or based on self-localization information of the host-vehicle.

11. The driver assistance system according to claim 8, comprising a computing system computing direct indicators or computing indirect indicators and the context based prediction.

12. The driver assistance system according to claim 8, comprising a validation unit employing a validator function, a model unit providing the trajectory model or a predictor unit outputting the estimate.

13. The driver assistance system according to claim 8, wherein the driver assistance system comprises an Adaptive Cruise Control.

14. A computer software program product embodied on a non-transitory computer-readable medium, said product performing, when executed on a computer,. the method according to claim 1.

15. A vehicle including a driver assistance system according to claim 8, wherein the driver assistance system influences controls of the vehicle based on the output estimate.