Patent application number | Description | Published |
20110182475 | TRAFFIC SIGNAL MAPPING AND DETECTION - A system and method provides maps identifying the 3D location of traffic lights. The position, location, and orientation of a traffic light may be automatically extrapolated from two or more images. The maps may then be used to assist robotic vehicles or human drivers to identify the location and status of a traffic signal. | 07-28-2011 |
20140018992 | Transitioning a Mixed-Mode Vehicle to Autonomous Mode - Disclosed are methods and devices for transitioning a mixed-mode autonomous vehicle from a human driven mode to an autonomously driven mode. Transitioning may include stopping a vehicle on a predefined landing strip and detecting a reference indicator. Based on the reference indicator, the vehicle may be able to know its exact position. Additionally, the vehicle may use the reference indictor to obtain an autonomous vehicle instruction via a URL. After the vehicle knows its precise location and has an autonomous vehicle instruction, it can operate in autonomous mode. | 01-16-2014 |
20140121880 | CONTROLLING VEHICLE LATERAL LANE POSITIONING - Methods and systems for controlling vehicle lateral lane positioning are described. A computing device may be configured to identify an object in a vicinity of a vehicle on a road. The computing device may be configured to estimate, based on characteristics of the vehicle and respective characteristics of the object, an interval of time during which the vehicle will be laterally adjacent to the object. Based on the characteristics of the vehicle, the computing device may be configured to estimate longitudinal positions of the vehicle on the road during the interval of time. Based on the respective characteristics of the object, the computing device may be configured to determine a lateral distance for the vehicle to maintain between the vehicle and the object during the interval of time at the longitudinal positions of the vehicle, and provide instructions to control the vehicle based on the lateral distance. | 05-01-2014 |
20140297094 | Controlling Vehicle Lateral Lane Positioning - Methods and systems for controlling vehicle lateral lane positioning are described. A computing device may be configured to identify an object in a vicinity of a vehicle on a road. The computing device may be configured to estimate, based on characteristics of the vehicle and respective characteristics of the object, an interval of time during which the vehicle will be laterally adjacent to the object. Based on the characteristics of the vehicle, the computing device may be configured to estimate longitudinal positions of the vehicle on the road during the interval of time. Based on the respective characteristics of the object, the computing device may be configured to determine a lateral distance for the vehicle to maintain between the vehicle and the object during the interval of time at the longitudinal positions of the vehicle, and provide instructions to control the vehicle based on the lateral distance. | 10-02-2014 |
20140303827 | Systems and Methods for Transitioning Control of an Autonomous Vehicle to a Driver - Methods and systems for adaptive methods for transitioning control to the driver are described. A computing device controlling a vehicle autonomously may be configured to receive a request for a transition of the vehicle from autonomous mode to manual mode through an indication by the driver. The computing device may determine the state of the vehicle based on parameters related to the autonomous operation of the vehicle. Based on the state of the vehicle and the indication, the computing device may determine instructions corresponding to the transition of control, which may include a strategy for the transition and duration of time corresponding to the transition of control. The computing device may provide the instructions to perform the transition of control of the vehicle from autonomous mode to manual mode. | 10-09-2014 |
20140358331 | Transitioning a Mixed-Mode Vehicle to Autonomous Mode - Disclosed are methods and devices for transitioning a mixed-mode autonomous vehicle from a human driven mode to an autonomously driven mode. Transitioning may include stopping a vehicle on a predefined landing strip and detecting a reference indicator. Based on the reference indicator, the vehicle may be able to know its exact position. Additionally, the vehicle may use the reference indictor to obtain an autonomous vehicle instruction via a URL. After the vehicle knows its precise location and has an autonomous vehicle instruction, it can operate in autonomous mode. | 12-04-2014 |
Patent application number | Description | Published |
20120083959 | DIAGNOSIS AND REPAIR FOR AUTONOMOUS VEHICLES - A system and method of controlling a vehicle is provided. In one aspect, the system and method determines the amount of wear on a component of the vehicle and, based on the amount of wear and information derived from the environment surrounding the vehicle (e.g., another vehicle in the path of the vehicle or a requirement to stop at a particular location), maneuvers the vehicle to mitigate further wear on the component. | 04-05-2012 |
20120083964 | ZONE DRIVING - A roadgraph may include a graph network of information such as roads, lanes, intersections, and the connections between these features. The roadgraph may also include one or more zones associated with particular rules. The zones may include locations where driving is typically challenging such as merges, construction zones, or other obstacles. In one example, the rules may require an autonomous vehicle to alert a driver that the vehicle is approaching a zone. The vehicle may thus require a driver to take control of steering, acceleration, deceleration, etc. In another example, the zones may be designated by a driver and may be broadcast to other nearby vehicles, for example using a radio link or other network such that other vehicles may be able to observer the same rule at the same location or at least notify the other vehicle's drivers that another driver felt the location was unsafe for autonomous driving. | 04-05-2012 |
20130297140 | ZONE DRIVING - A roadgraph may include a graph network of information such as roads, lanes, intersections, and the connections between these features. The roadgraph may also include one or more zones associated with particular rules. The zones may include locations where driving is typically challenging such as merges, construction zones, or other obstacles. In one example, the rules may require an autonomous vehicle to alert a driver that the vehicle is approaching a zone. The vehicle may thus require a driver to take control of steering, acceleration, deceleration, etc. In another example, the zones may be designated by a driver and may be broadcast to other nearby vehicles, for example using a radio link or other network such that other vehicles may be able to observer the same rule at the same location or at least notify the other vehicle's drivers that another driver felt the location was unsafe for autonomous driving. | 11-07-2013 |
20140016826 | TRAFFIC SIGNAL MAPPING AND DETECTION - A system and method provides maps identifying the 3D location of traffic lights. The position, location, and orientation of a traffic light may be automatically extrapolated from two or more images. The maps may then be used to assist robotic vehicles or human drivers to identify the location and status of a traffic signal. | 01-16-2014 |
20140081507 | DETECTING ROAD WEATHER CONDITIONS - Aspects of the disclosure relate generally to detecting road weather conditions. Vehicle sensors including a laser, precipitation sensors, and/or camera may be used to detect information such as the brightness of the road, variations in the brightness of the road, brightness of the world, current precipitation, as well as the detected height of the road. Information received from other sources such as networked based weather information (forecasts, radar, precipitation reports, etc.) may also be considered. The combination of the received and detected information may be used to estimate the probability of precipitation such as water, snow or ice in the roadway. This information may then be used to maneuver an autonomous vehicle (for steering, accelerating, or braking) or identify dangerous situations. | 03-20-2014 |
20140081573 | DETECTING ROAD WEATHER CONDITIONS - Aspects of the disclosure relate generally to detecting road weather conditions. Vehicle sensors including a laser, precipitation sensors, and/or camera may be used to detect information such as the brightness of the road, variations in the brightness of the road, brightness of the world, current precipitation, as well as the detected height of the road. Information received from other sources such as networked based weather information (forecasts, radar, precipitation reports, etc.) may also be considered. The combination of the received and detected information may be used to estimate the probability of precipitation such as water, snow or ice in the roadway. This information may then be used to maneuver an autonomous vehicle (for steering, accelerating, or braking) or identify dangerous situations. | 03-20-2014 |
20140185880 | TRAFFIC SIGNAL MAPPING AND DETECTION - A system and method provides maps identifying the 3D location of traffic lights. The position, location, and orientation of a traffic light may be automatically extrapolated from two or more images. The maps may then be used to assist robotic vehicles or human drivers to identify the location and status of a traffic signal. | 07-03-2014 |
20140214255 | MODIFYING BEHAVIOR OF AUTONOMOUS VEHICLES BASED ON SENSOR BLIND SPOTS AND LIMITATIONS - Aspects of the present disclosure relate generally to modeling a vehicle's view of its environment. This view need not include what objects or features the vehicle is actually seeing, but rather those areas that the vehicle is able to observe using its sensors if the sensors were completely un-occluded. For example, for each of a plurality of sensors of the object detection component, a computer may an individual 3D model of that sensor's field of view. Weather information is received and used to adjust one or more of the models. After this adjusting, the models may be aggregated into a comprehensive 3D model. The comprehensive model may be combined with detailed map information indicating the probability of detecting objects at different locations. A model of the vehicle's environment may be computed based on the combined comprehensive 3D model and detailed map information and may be used to maneuver the vehicle. | 07-31-2014 |
20140324268 | ZONE DRIVING - A roadgraph may include a graph network of information such as roads, lanes, intersections, and the connections between these features. The roadgraph may also include one or more zones associated with particular rules. The zones may include locations where driving is typically challenging such as merges, construction zones, or other obstacles. In one example, the rules may require an autonomous vehicle to alert a driver that the vehicle is approaching a zone. The vehicle may thus require a driver to take control of steering, acceleration, deceleration, etc. In another example, the zones may be designated by a driver and may be broadcast to other nearby vehicles, for example using a radio link or other network such that other vehicles may be able to observer the same rule at the same location or at least notify the other vehicle's drivers that another driver felt the location was unsafe for autonomous driving. | 10-30-2014 |