OBSTACLE DETECTION AND MAPPING

• US 20200134327A1
• Filed: 08/20/2019
• Published: 04/30/2020
• Est. Priority Date: 10/26/2018
• Status: Active Grant

• Current Assignee: Autobrains Technologies Ltd.
• Original Assignee: Cartica Al Ltd.
• Inventors: RAICHELGAUZ, Igal, Odinaev, Karina
• Granted Patent: US 11,244,176 B2
• Time in Patent Office: Days
• CPC Class Codes: G05D 1/0061 for transition from automatic pilot to manual pilot and vice versa
• CPC Class Codes: G05D 1/0088 characterized by the autonomous decision making process, e.g. artificial intelligence, predefined behaviours using knowledge based models G06N5/00
• CPC Class Codes: G05D 1/0094 involving pointing a payload, e.g. camera, weapon, sensor, towards a fixed or moving target
• CPC Class Codes: G05D 1/0238 using obstacle or wall sensors G05D1/0246 and G05D1/0289 take precedence; lidar systems designed for anti-collision purposes G01S17/93
• CPC Class Codes: G05D 1/0246 using a video camera in combination with image processing means
• CPC Class Codes: G05D 1/0253 extracting relative motion information from a plurality of images taken successively, e.g. visual odometry, optical flow determining position or orientation from images G06T7/70
• CPC Class Codes: G05D 1/0276 using signals provided by a source external to the vehicle involving a plurality of vehicles G05D1/0287; automatically controlling vehicle speed responsive to externally generated signals B60K31/0058
• CPC Class Codes: G05D 1/0287 involving a plurality of land vehicles, e.g. fleet or convoy travelling traffic control systems for road vehicles G08G1/00, particularly anticollision systems G08G1/16
• CPC Class Codes: G06F 18/211 Selection of the most significant subset of features
• CPC Class Codes: G06F 18/214 Generating training patterns; Bootstrap methods, e.g. bagging or boosting
• CPC Class Codes: G06F 18/23 Clustering techniques
• CPC Class Codes: G06F 18/25 Fusion techniques
• CPC Class Codes: G06F 18/28 Determining representative reference patterns, e.g. by averaging or distorting; Generating dictionaries
• CPC Class Codes: G06N 3/04 Architecture, e.g. interconnection topology
• CPC Class Codes: G06N 3/045 Combinations of networks
• CPC Class Codes: G06N 3/08 Learning methods
• CPC Class Codes: G06T 2207/10016 Video; Image sequence
• CPC Class Codes: G06T 2207/20076 Probabilistic image processing
• CPC Class Codes: G06T 2207/20081 Training; Learning
• CPC Class Codes: G06T 2207/20084 Artificial neural networks [ANN]
• CPC Class Codes: G06T 2207/30252 : Vehicle exterior; Vicinity of vehicle
• CPC Class Codes: G06T 2207/30261 : Obstacle
• CPC Class Codes: G06T 7/246 : using feature-based methods, e.g. the tracking of corners or segments
• CPC Class Codes: G06T 7/70 : Determining position or orientation of objects or cameras camera calibration G06T7/80
• CPC Class Codes: G06T 7/74 : involving reference images or patches
• CPC Class Codes: G06V 10/255 : Detecting or recognising potential candidate objects based on visual cues, e.g. shapes
• CPC Class Codes: G06V 10/762 : using clustering, e.g. of similar faces in social networks
• CPC Class Codes: G06V 10/764 : using classification, e.g. of video objects
• CPC Class Codes: G06V 10/82 : using neural networks
• CPC Class Codes: G06V 20/56 : exterior to a vehicle by using sensors mounted on the vehicle
• 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 2201/08 : Detecting or categorising vehicles
• CPC Class Codes: G06V 40/103 : Static body considered as a whole, e.g. static pedestrian or occupant recognition
• CPC Class Codes: G08G 1/162 : event-triggered
• 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
• 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: H04N 7/183 : for receiving images from a single remote source
• CPC Class Codes: H04N 7/185 : from a mobile camera, e.g. for remote control
• CPC Class Codes: H04W 4/46 : for vehicle-to-vehicle communication [V2V]
• CPC Class Codes: H04W 4/48 : for in-vehicle communication

Abstract

An autonomous driving system for a vehicle comprising: an I/O module operative to communicate with an obstacle avoidance server; at least one sensor operative to provide at least an indication of an obstacle in a path of said vehicle; processing circuitry; and an autonomous driving manager to be executed by said processing circuitry and operative to: detect said at least an indication of an obstacle based on data provided by said at least one sensor, drive said vehicle in accordance with a driving policy associated with said obstacle, and send an obstacle report with obstacle information associated with said at least an indication of an obstacle to said obstacle avoidance server.

Claims

1. An autonomous driving system for a vehicle comprising:

an I/O module operative to communicate with an obstacle avoidance server;

at least one sensor operative to provide at least an indication of an obstacle in a path of said vehicle;

processing circuitry; and

an autonomous driving manager to be executed by said processing circuitry and operative to;

detect said at least an indication of an obstacle based on data provided by said at least one sensor, drive said vehicle in accordance with a driving policy associated with said obstacle, and send an obstacle report with obstacle information associated with said at least an indication of an obstacle to said obstacle avoidance server.

2. The autonomous driving system according to claim 1 wherein:

said I/O module is operative to receive an obstacle warning from said obstacle avoidance server; and

said autonomous driving manager comprises an obstacle predictor operative to predict a location for said obstacle based on said obstacle warning.

3. The autonomous driving system according to claim 2 wherein said driving policy is received with said obstacle warning.

4. The autonomous driving system according to claim 2 wherein said obstacle predictor is operative to use an offset from at least one static reference point to predict said location.

5. The autonomous driving system according to claim 2 wherein said autonomous driving manager comprises an obstacle avoidance module operative to enact preventative measures to avoid said obstacle based on said location.

6. The autonomous driving system according to claim 5 wherein said obstacle avoidance module is further operative to enact said preventative measures before said autonomous driving system detects said obstacle.

7. The autonomous driving system according to claim 1 wherein said at least one sensor is operative to:

monitor operation of at least one shock absorber on said vehicle; and

provide information regarding said operation to said autonomous driving system, wherein said indication may be based on at least one of a frequency or severity of at least one shock monitored by said sensor.

8. The autonomous driving system according to claim 1 wherein said at least one sensor is operative to:

capture imagery of entities on said path of said vehicle; and

provide said imagery to said autonomous driving system.

9. The autonomous driving system according to claim 1 wherein said at least one sensor is operative to:

capture telemetry data from said vehicle; and

provide said telemetry data to said autonomous driving system.

10. An obstacle avoidance server comprising:

an I/O module operative to communicate with a plurality of vehicles;

processing circuitry; and

an obstacle avoidance manager to be executed by said processing circuitry and operative to;

receive sensor data from said plurality of vehicles, wherein said sensor data is associated with driving sessions by said plurality of vehicles;

based at least on said sensor data, determine a location for at least one obstacle on a roadway; and

send an obstacle warning to at least one vehicle of said plurality of vehicles, wherein said obstacle warning includes at least said location.

11. The obstacle avoidance server according to claim 10 wherein:

said obstacle avoidance manager comprises an obstacle policy manager operative to determine a driving policy associated with said at least one obstacle; and

said obstacle warning includes at least an indication of said driving policy.

12. The obstacle avoidance server according to claim 11 wherein:

said obstacle avoidance manager comprises an obstacle categorizer operative to categorize said at least one obstacle according to a level of persistence; and

said obstacle policy manager is operative to determine said driving policy based on at least said level of persistence.

13. The obstacle avoidance server according to claim 12 wherein:

said level of persistence is at least one of;

persistent, temporary, or recurring.

14. The obstacle avoidance server according to claim 13 wherein said level of persistence is a hybrid level of persistence, wherein a hybrid level of persistence is a combination of at least two of:

persistent, temporary, or recurring.

15. The obstacle avoidance server according to claim 12 wherein:

said obstacle avoidance manager is operative to include said at least one obstacle in a warning list, wherein said obstacle warning is sent to at least one vehicle of said plurality of vehicles according to at least said warning list;

said obstacle avoidance manager comprises an obstacle timer operative to set a timer for an expected duration of said at least one obstacle at least according to said level of persistence; and

said obstacle avoidance manager is operative to remove said at least one obstacle from said warning list upon expiration of said timer.

16. The obstacle avoidance server according to claim 10 wherein said sensor data is received in an obstacle report indicating said at least one obstacle.

17. A non-transitory computer readable medium that stores instructions that once executed by a vehicle, causes the vehicle to:

provide, by at least one sensor of the vehicle, at least an indication of an obstacle in a path of said vehicle;

detect, by an autonomous driving manager that is executed by a processing circuitry of the vehicle, the at least an indication of an obstacle based on data provided by said at least one sensor;

drive the vehicle, by the autonomous driving manager, in accordance with a driving policy associated with said obstacle; and

send, by an I/O module of the vehicle, to an obstacle avoidance server, an obstacle report with obstacle information associated with said at least an indication of the obstacle.