METHOD AND ARRANGEMENT FOR DETERMINING SAFE VEHICLE TRAJECTORIES

• US 20160327953A1
• Filed: 04/27/2016
• Published: 11/10/2016
• Est. Priority Date: 05/05/2015
• Status: Active Grant

• Current Assignee: Volvo Car Corporation (AB Volvo)
• Original Assignee: Volvo Car Corporation (AB Volvo)
• Inventors: NILSSON, Jonas, SORSTEDT, Joakim Lin, BRANNSTROM, Mattias Erik, ALI, Mohammad
• Granted Patent: US 10,037,036 B2
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: B60W 2554/4029 Pedestrians
• CPC Class Codes: B60W 2556/20 Data confidence level
• CPC Class Codes: B60W 30/025 related to comfort of drivers or passengers
• CPC Class Codes: B60W 30/095 Predicting travel path or likelihood of collision
• CPC Class Codes: B60W 30/143 Speed control B60W30/16 takes precedence
• CPC Class Codes: B60W 30/16 Control of distance between vehicles, e.g. keeping a distance to preceding vehicle
• CPC Class Codes: B60W 30/18145 Cornering
• CPC Class Codes: B60W 40/04 Traffic conditions
• CPC Class Codes: B60W 50/0097 Predicting future conditions
• CPC Class Codes: B60W 60/0011 involving control alternatives for a single driving scenario, e.g. planning several paths to avoid obstacles
• CPC Class Codes: B60W 60/00274 considering possible movement changes
• CPC Class Codes: G01S 13/931 of land vehicles
• CPC Class Codes: G01S 2013/9315 Monitoring blind spots
• CPC Class Codes: G01S 2013/932 using own vehicle data, e.g. ground speed, steering wheel direction
• CPC Class Codes: G01S 2013/9322 using additional data, e.g. driver condition, road state or weather data
• 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: G01S 2013/93271 in the front of the vehicles
• CPC Class Codes: G08G 1/16 Anti-collision systems road vehicle drive control systems for predicting or avoiding probable or impending collision otherwise than by control of a particular sub-unit B60W30/08

Abstract

A method and arrangement for determining safe vehicle trajectories for a vehicle equipped with sensors for monitoring a surrounding environment, taking into account sensing limitations, as well as a vehicle including such an arrangement. The method includes detecting observable obstacles, detecting unobservable areas, adding virtual obstacles in unobservable areas, associating each observable obstacle and each virtual obstacle with one or more hypothetical events and assigning an occurrence probability to each combination of obstacle and one or more events, and determining safe vehicle trajectories based on both observable obstacles and virtual obstacles and the occurrence probability of each combination of obstacle and one or more events.

Claims

1. A method for determining safe vehicle trajectories for a vehicle equipped with sensors for monitoring a surrounding environment, taking into account sensing limitations, the method comprising:

detecting observable obstacles using one or more of the sensors;

detecting, using a processor, unobservable areas;

adding, using the processor, virtual obstacles in unobservable areas;

associating, using the processor, each observable obstacle and each virtual obstacle with one or more hypothetical events and assigning an occurrence probability to each combination of obstacle and one or more events; and

determining, using the processor, safe vehicle trajectories based on both observable obstacles and virtual obstacles and the occurrence probability of each combination of obstacle and one or more events.

2. The method according to claim 1 wherein unobservable areas are determined as at least one of:

areas outside a sensing range of the vehicle sensors;

areas for which the confidence of obtained sensor readings falls below a predetermined confidence threshold; and

areas occluded by objects in the vehicle surrounding environment.

3. The method according to claim 1 wherein virtual obstacles, the associated hypothetical events of which exceed a predetermined probability to occur, are added to the unobservable areas.

4. The method according to claim 1 wherein safe vehicle trajectories are determined such that, should a low-probability hypothetical event occur, a first margin is applied, and such that, should a high-probability hypothetical event occur, a second margin is applied, where the first margin is relatively smaller than the second margin.

5. The method according to claim 1 wherein an environment category is determined from a set of predetermined environment categories and the one or more hypothetical events selected from a set of predetermined hypothetical events based on the determined environment category, and virtual obstacles having different associated hypothetical events are assigned different probabilities to occur depending on the determined environment category.

6. The method according to claim 5 wherein statistic data for previous occurrences of observed obstacles associated with predetermined environment categories is relied on for determining probabilities for virtual obstacles to occur depending on the determined environment category.

7. The method according claim 4 wherein the first and second margins are either physical margins to an obstacle or margins in terms of vehicle dynamics.

8. The method according to claim 7 wherein physical margins to an obstacle are distances from a safe vehicle trajectory to the obstacle, and margins in terms of vehicle dynamics are margins in one or more of vehicle longitudinal-, lateral- and yaw-acceleration levels required for following a safe vehicle trajectory.

9. The method according to claim 8 wherein an increased physical margin to an obstacle means an increased distance from a safe vehicle trajectory to the obstacle and a decreased physical margin to an obstacle mean a reduced distance from a safe vehicle trajectory to the obstacle, and an increased margin in terms of vehicle dynamics means reduced vehicle acceleration levels required for following a safe vehicle trajectory and a decreased margin in terms of vehicle dynamics means increased vehicle acceleration levels required for following a safe vehicle trajectory.

16. The method according to claim 1 wherein the vehicle is at least a semi-autonomous vehicle, and the method further comprises controlling the vehicle or outputting information to a vehicle driver based on the safe vehicle trajectories determined.

10. An arrangement for determining safe vehicle trajectories for a vehicle equipped with sensors for monitoring a surrounding environment, taking into account sensing limitations, the arrangement comprising:

a processor arranged to process readings from the vehicle sensors for monitoring the surrounding environment, wherein the processor is configured to detect observable obstacles, detect unobservable areas, add virtual obstacles in unobservable areas, associate each observable obstacle and each virtual obstacle with one or more hypothetical events and assign an occurrence probability to each combination of obstacle and one or more events, and determine safe vehicle trajectories based on both observable obstacles and virtual obstacles and the occurrence probability of each combination of obstacle and one or more events.

11. The arrangement according to claim 10 wherein the processor further is configured to determine unobservable areas as at least one of:

areas outside a sensing range of the vehicle sensors;

areas for which the confidence of obtained sensor readings fall below a predetermined confidence threshold; and

areas occluded by objects in the vehicle surrounding environment.

12. The arrangement according to claim 10 wherein the processor further is configured to determine safe vehicle trajectories such that, should a low-probability hypothetical event occur, a first margin is applied, and such that, should a high-probability hypothetical event occur, a second margin is applied, where the first margin is relatively smaller than the second margin.

13. The arrangement according to claim 10 wherein the processor further is configured to determine an environment category from a set of predetermined environment categories and select the one or more hypothetical events from a set of predetermined hypothetical events based on the determined environment category, and assign to virtual obstacles having different associated hypothetical events different probabilities to occur depending on the determined environment category.

14. The arrangement according to claim 13 wherein the processor further is configured to rely on statistic data for previous occurrences of observed obstacles associated with predetermined environment categories for determining probabilities for virtual obstacles to occur depending on the determined environment category.

15. A vehicle equipped with sensors for monitoring a surrounding environment, the vehicle comprising the arrangement according to claim 10.

17. The arrangement of claim 10 wherein the vehicle is at least a semi-autonomous vehicle, and the safe vehicle trajectories determined are used to control the vehicle or output information to a vehicle driver.