Systems and methods are provided for navigating based on sensed brake light patterns. In one implementation, a navigation system for a host vehicle may include at least one processing device. The at least one processing device may be programmed to receive, from a camera, a plurality of images representative of an environment ahead of the host vehicle; analyze the plurality of images to identify at least one target vehicle in the environment ahead of the host vehicle; identify, based on analysis of the plurality of images, at least one brake light associated with the target vehicle and at least one characteristic associated with changes in an illumination state of the at least one brake light; and cause a navigational change for the host vehicle based on the identified at least one characteristic associated with the changes in the illumination state of the at least one brake light.

Systems and methods are provided for navigating based on behaviors of following vehicles. In one implementation, a navigation system for a host vehicle may include at least one processing device. The at least one processing device may be 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 first target vehicle and a second target vehicle in the environment of the host vehicle; analyze the plurality of images to determine a distance between the first target vehicle and the second target vehicle; and determine, from the plurality of images, a time to reach a boundary associated with one of the first target vehicle and the second target vehicle, wherein the determined time exceeds a first threshold and does not exceed a second threshold.

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 at least one target vehicle in the environment of the host vehicle; analyze the plurality of images to identify at least one adverse characteristic of the target vehicle relative to the host vehicle; and cause at least one navigational change of the host vehicle to initiate a pass of the target vehicle after identifying the at least one characteristic of the target vehicle.

The present disclosure relates to systems and methods for host vehicle navigation. In one implementation, a navigation system for a host vehicle may include 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 first flow of traffic and a second flow of traffic; determine a presence of at least one navigational state characteristic indicative of an alternating merging of the first flow of traffic and the second flow of traffic into a merged lane; cause at least a first navigational change to allow one target vehicle from the first flow of traffic to proceed ahead of the host vehicle; and cause at least a second navigational change to cause the host vehicle to follow the target vehicle into the merged lane.

The present disclosure relates to systems and methods for host vehicle navigation. In one implementation, a navigation system for a host vehicle may include at least one processing device programmed to receive, from a camera, a plurality of images representative of an environment of the host vehicle; receive, from a camera, a plurality of images representative of an environment of the host vehicle; analyze the images to identify a target vehicle in the environment of the host vehicle; cause a navigational change of the host vehicle to signal to the target vehicle an intent of the host vehicle to make a subsequent navigational maneuver; analyze the images to detect a change in a navigational state of the target vehicle; determine a navigational action for the host vehicle; and cause an adjustment of a navigational actuator of the host vehicle in response to the determined navigational action for the host vehicle.

One variation of a method for autonomously navigating along a crosswalk includes: at a first time, navigating autonomously along a sidewalk toward a crosswalk coinciding with a navigation route assigned to the autonomous vehicle; recording optical data of a scene proximal the autonomous vehicle via an optical sensor integrated into the autonomous vehicle; aligning an anteroposterior axis of the autonomous vehicle to the crosswalk detected in the optical data; identifying a pedestrian proximal the crosswalk in the optical data; in response to the pedestrian entering the crosswalk at a second time succeeding the first time, predicting right of way of the autonomous vehicle to enter the crosswalk; and, in response to predicting right of the autonomous vehicle to enter the crosswalk, autonomously navigating from the sidewalk into the crosswalk and autonomously navigating along the crosswalk to an opposing sidewalk according to the navigation route.

The present radio system transmits an electromagnetic signal to nearby devices requesting the device respond. The radio system also receives responses to the electromagnetic signal from the nearby devices. Based on the radio technology used, the signaling of the transmitted electromagnetic signal may be varied. For example, the transmitted electromagnetic signal may be a Bluetooth, 802.11, or other radio signal. A device that received the signal from the radio unit may transmit a response signal with the same radio technology. However, in some instances, the radio technology used for communication may operate on several radio (e.g., frequency) channels. Both the transmitter and receiver must operate on the same channel at the same time in order to communicate. Thus, it may be desirable to transmit the electromagnetic signal on more than one channel at the same time, in order to increase the chances that a nearby device responds.

A neural network may be utilized for autonomously driving a self-driving vehicle (SDV). The neural network can establish a destination location in local coordinates relative to the SDV. The neural network may then identify one or more navigation points in a forward operational direction of the SDV, and process sensor data from a sensor system of the SDV, the sensor data providing a sensor view of the forward operational direction of the SDV. Utilizing the sensor data, the neural network can operate acceleration, braking, and steering systems of the SDV to continuously follow the one or more navigation points along an established route to the destination location.

A method and system for navigating an autonomous vehicle is disclosed. The method comprises determining a relative velocity between the autonomous vehicle and each of one or more surrounding vehicles. Thereafter, the relative velocity associated with each of the one or more surrounding vehicles may be compared with a predefined relative speed threshold to identify a plurality of vehicles-of-interest from the one or more surrounding vehicles. An area bounded by the plurality of vehicles-of-interest may then be determined as a current navigable space for the autonomous vehicle. Subsequently, a future navigable space for the autonomous vehicle may be predicted based on a predicted velocity of the plurality of vehicles-of-interest and a predicted distance to the plurality of vehicles-of-interest. The autonomous vehicle may then be navigated based on the current navigable space and the future navigable space.

Achieving safety and natural automatic driving needs a control platform using intelligence (such as learning function and artificial intelligence), but it is difficult to ensure operations suited for the behavior of a vehicle by the output of intelligence. An automatic driving device according to the present invention includes a control program for inputting outside information and vehicle information, and outputting a target control value for a vehicle. The control program has a first program for generating a first target control amount on the basis of a dynamically changing algorithm (which outputs operations based on learning function or artificial intelligence), and a second program for generating a second target control amount on the basis of a prescribed algorithm (which outputs operations according to traffic rules or driving morals).