Abstract: Systems and methods are provided for vehicle navigation. The systems and methods may detect traffic lights. For example, one or more traffic lights may be detected using detection-redundant camera detection paths, a fusion of information from a traffic light transmitter and one or more cameras, based on contrast enhancement for night images, and based on low resolution traffic light candidate identification followed by high resolution candidate analysis. Additionally, the systems and methods may navigation based on a worst time to red estimation.

Abstract: Systems and methods are provided for crowdsourcing road surface information collection. In one implementation, a method of collecting road surface information for a road segment may include receiving at least one image representative of a portion of the road segment, identifying in the at least one image at least one road surface feature along the portion of the road segment, determining a plurality of locations associated with the road surface feature according to a local coordinate system of the vehicle, and transmitting the determined plurality of locations from the vehicle to a server. The determined locations may be configured to enable determination by the server of a line representation of the road surface feature extending along the road segment.

Abstract: A vehicle navigation system may comprise a memory including instructions and circuitry configured by the instructions to identify a target vehicle in an environment of a vehicle that includes the vehicle navigation system. The circuitry may receive image data of the target vehicle from an image capture device of the vehicle; identify, based on analysis of the image data, a situational characteristic of the target vehicle including an indication that the target vehicle is traveling behind an additional vehicle traveling slower than the target vehicle; and change a navigational state of the vehicle to allow an action of the target vehicle. The vehicle may be configured to cause the change in the navigational state based on a determination that the situational characteristic indicates that the target vehicle would benefit from the change in the navigational state.

Abstract: A navigational system for a host vehicle may comprise at least one processing device. The processing device may be programmed to receive a first output and a second output associated with the host vehicle and identify a representation of a target object in the first output. The processing device may determine whether a characteristic of the target object triggers a navigational constraint by verifying the identification of the target object based on the first output and, if the at least one navigational constraint is not verified based on the first output, then verifying the identification of the target object based on a combination of the first output and the second output. In response to the verification, the processing device may cause at least one navigational change to the host vehicle.

Abstract: Methods and apparatus to provide accident avoidance information to passengers of autonomous vehicles are disclosed. An example apparatus includes a safety analyzer to determine an autonomously controlled vehicle is in a safe situation at a first point in time and in a dangerous situation at a second point in time. The example apparatus also includes a user interface generator to: generate user interface data to define content for a user interface to be displayed via a screen, the user interface to include a graphical representation of the vehicle, the user interface to graphically indicate the vehicle is in the safe situation at the first point in time; and modify the user interface data so that the user interface graphically indicates the vehicle is in the dangerous situation at the second point in time.

Abstract: A navigation system for a host vehicle is provided. The system may comprise at least one processing device programmed to receive, from a camera, a plurality of images representative of an environment of the host vehicle; analyze the plurality of images to identify a navigational state associated with the host vehicle; determine a first predefined navigational constraint implicated by at least one aspect of the navigational state; identify, based on analysis of the plurality of images, a presence of at least one navigational constraint augmentation factor; determine a second navigational constraint based on identification of the at least one navigational constraint augmentation factor; determine, based on the identified navigational state, a navigational action for the host vehicle satisfying the second navigational constraint; and cause at least one adjustment of a navigational actuator of the host vehicle in response to the determined navigational action.

Abstract: A system for a vehicle is provided. The system may include a memory and at least one processor configured to: access a plurality of images of a forward-facing view from the vehicle, the plurality of images corresponding to image data obtained by a camera; determine from the images a first lane marking on a first side of a lane, the lane through which the vehicle can navigate, and a second lane marking on a second side of the lane opposite of the first side; navigate the vehicle autonomously relatively centered between the first and second lane markings; determine from the plurality of images that an object is on the first side or the second side of the lane, and the object beyond the first or second lane marking; and navigate the vehicle autonomously to travel over a driving path that is offset from a center of the lane.

Abstract: Systems and methods use cameras to provide autonomous navigation features. In one implementation, a driver assist navigation system for a primary vehicle may include at least one image capture device configured to acquire a plurality of images of an area in a vicinity of the primary vehicle; a data interface; and at least one processing device. The at least processing device may be configured to: receive the plurality of images via the data interface; identify a target object within the plurality of images; monitor, via the plurality of images, a motion of the target object and a distance between the primary vehicle and the target object; determine an indicator of an intercept time between the primary vehicle and the target object based on the monitored motion and the distance between the primary vehicle and the target object; and cause a response in the primary vehicle based on a comparison of the intercept time to a plurality of predetermined intercept thresholds.

Abstract: Systems and methods are provided for navigating an autonomous vehicle using reinforcement learning techniques.

Abstract: Systems and methods are provided for navigating an autonomous vehicle using reinforcement learning techniques.

Abstract: Systems and methods navigate a vehicle by determining a free space region in which the vehicle can travel. In one implementation, a system may include at least one processor programmed to receive from an image capture device, a plurality of images associated with the environment of a vehicle, analyze at least one of the plurality of images to identify a first free space boundary on a driver side of the vehicle and extending forward of the vehicle, a second free space boundary on a passenger side of the vehicle and extending forward of the vehicle, and a forward free space boundary forward of the vehicle and extending between the first free space boundary and the second free space boundary. The first free space boundary, the second free space boundary, and the forward free space boundary may define a free space region forward of the vehicle.