Method and system for assisting a driver of a vehicle in driving the vehicle, vehicle and computer program

• US 10,220,841 B2
• Filed: 10/21/2016
• Issued: 03/05/2019
• Est. Priority Date: 10/22/2015
• Status: Active Grant

• Current Assignee: Honda Research Institute Europe GmbH (Honda Motor Company)
• Original Assignee: Honda Research Institute Europe GmbH (Honda Motor Company)
• Inventors: Sendhoff, Bernhard, Eggert, Julian
• Primary Examiner(s): Bendidi, Rachid
• Application Number:
  US15/299,699
• Publication Number:
  US 20170113685A1
• Time in Patent Office: 865 Days
• CPC Class Codes: B60T 2210/32 Vehicle surroundings
• CPC Class Codes: B60W 2050/0075 Automatic parameter input, automatic initialising or calibrating means
• CPC Class Codes: B60W 2400/00 Indexing codes relating to detected, measured or calculated conditions or factors
• CPC Class Codes: B60W 2420/403 Image sensing, e.g. optical camera
• CPC Class Codes: B60W 2420/408 Radar; Laser, e.g. lidar
• CPC Class Codes: B60W 2556/00 Input parameters relating to data
• CPC Class Codes: B60W 2556/10 Historical data
• CPC Class Codes: B60W 30/09 Taking automatic action to avoid collision, e.g. braking and steering
• CPC Class Codes: B60W 30/0956 the prediction being responsive to traffic or environmental parameters
• 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: G06N 20/00 Machine learning
• CPC Class Codes: G06N 5/047 Pattern matching networks; Rete networks
• CPC Class Codes: G06N 7/01 Probabilistic graphical models, e.g. probabilistic networks
• CPC Class Codes: G08G 1/0112 from the vehicle, e.g. floating car data [FCD]
• CPC Class Codes: G08G 1/0129 for creating historical data or processing based on historical data
• CPC Class Codes: G08G 1/0133 for classifying traffic situation
• CPC Class Codes: G08G 1/09626 where the origin of the information is within the own vehicle, e.g. a local storage device, digital map
• CPC Class Codes: G08G 1/165 for passive traffic, e.g. including static obstacles, trees
• CPC Class Codes: G08G 1/166 for active traffic, e.g. moving vehicles, pedestrians, bikes

Abstract

An advanced driver assistant system and a method for assisting a driver of a vehicle in driving the vehicle. The system includes sensing means for sensing an environment of the vehicle, a storing unit for storing a database for storing prototypical situations associated with risk information, a processing unit and an output unit to output an assistance signal on the basis of which driver information can be output and/or vehicle operators can be actuated. The processing unit is configured to determine parameters of the vehicle and/or objects in the environment of the vehicle based upon the sensing means output, to classify a traffic situation on the basis of the determined parameters by comparing the determined parameters with parameters of the prototypical traffic situations, to extract risk information from the database and to calculate an expected risk for the encountered traffic situation on the basis of the extracted risk information.

Claims

1. A method for assisting a driver of a vehicle in driving the vehicle, comprising the steps of:

sensing an environment of the vehicle by sensors mounted on the vehicle;

determining parameters of the vehicle or objects in the environment of the vehicle on the basis of the sensor outputs, wherein a vehicle position is determined as one parameter of the determined parameters;

classifying an encountered traffic situation on the basis of the determined parameters by comparing the determined parameters with parameters of prototypical traffic situations that are stored in a database, wherein each of the prototypical traffic situations is related to one specific road and environment object configuration pattern;

extracting, from the database, risk information associated with the encountered traffic situation from the classification and the respective prototypical traffic situations,

wherein the risk information associated with the prototypical traffic situation includes risk information on most frequent accident types for the respective prototypical traffic situation and the vehicle position;

calculating an expected risk for the encountered traffic situation on the basis of the extracted risk information; and

outputting driver information or actuating vehicle controls on the basis of the calculated expected risk in addition to the classified traffic situation,

wherein the risk information comprises statistics related to a quantification of an expected risk for the respective prototypical traffic situation.

2. The method according to claim 1, wherein

the database is updated over time using the sensor signal during operation.

3. The method according to claim 2, wherein

the database is updated by first updating a common database that is stored externally from the vehicle(s) which is then copied to the vehicle.

4. The method according to claim 1, wherein

the database is updated over time using monitoring signals of sensors of a plurality of vehicles which are in communicative connection with the database.

5. The method according to claim 1, wherein

the risk information on one of the accident types is selected on the basis of the vehicle position.

6. The method according to claim 1, wherein

risk factors are determined from the sensor outputs, and these risk factors are used in addition to the risk information extracted from the database to calculate the expected risk.

7. The method according to claim 1, wherein

vehicle dynamics are determined from the sensor outputs and these vehicle dynamics are used in classification of the traffic situation.

8. An advanced driver assistant system for assisting a driver of a vehicle in driving the vehicle, said system comprising a processor connected to a storage unit, the system comprising:

sensing means for sensing an environment of the vehicle; and

a database stored in the storage unit storing prototypical traffic situations associated with risk information,

wherein the processor is configured to

determine parameters of the vehicle or objects in the environment of the vehicle on the basis of the sensing means output, wherein a vehicle position is determined as one parameter of the determined parameters,

classify a traffic situation on the basis of the determined parameters by comparing the determined parameters with parameters of the prototypical traffic situations, wherein each of the prototypical traffic situations is related to one specific road and environment object configuration pattern,

extract risk information from the database and to calculate an expected risk for the encountered traffic situation on the basis of the extracted risk information,

wherein the risk information associated with the prototypical traffic situation includes risk information on most frequent accident types for the respective prototypical traffic situation and the vehicle position, and

output an assistance signal on the basis of the calculated extracted risk information in addition to the classified traffic situation to a human machine interface or to controls of the vehicle, and

wherein the risk information comprises statistics related to a quantification of an expected risk for the respective prototypical traffic situation.

9. The advanced driver assistant system according to claim 8, wherein

the system comprises a common database.

10. The advanced driver assistant system according to claim 8, wherein

the sensing means is configured to determine at least the position of the vehicle.

11. The advanced driver assistant system according to claim 10, wherein

the processor is configured to select risk information that is stored associated with a specific road configuration defining a prototypical traffic situation on the basis of the vehicle position.

12. The advanced driver assistant system according to claim 8, wherein

the processor is further configured to determine risk factors from the sensing means output and to combine these risk factors and the extracted risk information when the expected risk is calculated.

13. The advanced driver assistant system according to claim 8, wherein

the processor is further configured to determine from the sensing means outputs vehicle dynamics and to base the classification of the traffic situation also on these vehicle dynamics.

14. A computer program embodied on a non-transitory computer-readable medium, said computer program, when executed by a processor, causes the processor to:

sense an environment of the vehicle by sensors mounted on the vehicle;

determine parameters of the vehicle or objects in the environment of the vehicle on the basis of the sensor outputs, wherein a vehicle position is determined as one parameter of the determined parameters;

classify an encountered traffic situation on the basis of the determined parameters by comparing the determined parameters with parameters of prototypical traffic situations that are stored in a database, wherein each of the prototypical traffic situations is related to one specific road and environment object configuration pattern;

extract, from the database, risk information associated with the encountered traffic situation from the classification and the respective prototypical traffic situations,

wherein the risk information associated with the prototypical traffic situation includes risk information on most frequent accident types for the respective prototypical traffic situation and the vehicle position;

calculate an expected risk for the encountered traffic situation on the basis of the extracted risk information; and

output driver information or actuating vehicle controls on the basis of the calculated expected risk in addition to the classified traffic situation,

wherein the risk information comprises statistics related to a quantification of an expected risk for the respective prototypical traffic situation.