Technologies and techniques for merging/resolving MAP data and signal phase and timing (SPaT) messages for traffic light data prediction a vehicle in an intelligent transportation system. SPaT data and/or MAP data may be received in a vehicle from a direct source and a network source, and processed to merge data from the two different platforms. The merging of the data allows a vehicle to generate inferences relating to traffic light data received on direct, low-latency, connections, while taking advantage of the more comprehensive data provided from the network source.

A device may include a sign and an adjustment mechanism configured to adjust the sign relative to a vehicle to which the sign couples. The device may receive first data associated with adjusting the sign, and cause, by the adjustment mechanism and based on receiving the first data, the sign to adjust to a first position. The device may receive second data associated with adjusting the sign, and cause, by the adjustment mechanism and based on receiving the second data, the sign to adjust to a second position.

A device may include a sign and an adjustment mechanism configured to adjust the sign relative to a vehicle to which the sign couples. The device may receive first data associated with adjusting the sign, and cause, by the adjustment mechanism and based on receiving the first data, the sign to adjust to a first position. The device may receive second data associated with adjusting the sign, and cause, by the adjustment mechanism and based on receiving the second data, the sign to adjust to a second position.

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.

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.

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.

This document discloses methods of training a classifier to identify traffic signal states in images captured be a vehicle. The vehicle can then use the identified states when making movement decisions when traveling in an environment. The system determines that a traffic signal is within a field of view of the camera (i.e., within an image). The system also receives a signal with signal phase and timing data for the traffic signal. The system processes the images to identify an image that includes the traffic signal. The system analyzes the signal data to determine a state of the traffic signal at the time of image capture, labels the image with a label of determined state, and passes the image and a label to a classifier in order to train the classifier.

This document discloses methods of training a classifier to identify traffic signal states in images captured be a vehicle. The vehicle can then use the identified states when making movement decisions when traveling in an environment. The system determines that a traffic signal is within a field of view of the camera (i.e., within an image). The system also receives a signal with signal phase and timing data for the traffic signal. The system processes the images to identify an image that includes the traffic signal. The system analyzes the signal data to determine a state of the traffic signal at the time of image capture, labels the image with a label of determined state, and passes the image and a label to a classifier in order to train the classifier.

Traffic signals that adapt to traffic conditions are provided with countdown timers. These countdown timers count down from some number towards zero, and indicate the approximate duration remaining before a traffic signal changes state. Since the traffic signal is continuously adapting to traffic conditions, the exact time before a state change occurs is not known in advance. Using a countdown algorithm, the countdown timers imperceptibly modify the countdown sequence in real time so that the traffic signal state change coincides approximately with the moment the countdown reaches its minimum count.

Traffic signals that adapt to traffic conditions are provided with countdown timers. These countdown timers count down from some number towards zero, and indicate the approximate duration remaining before a traffic signal changes state. Since the traffic signal is continuously adapting to traffic conditions, the exact time before a state change occurs is not known in advance. Using a countdown algorithm, the countdown timers imperceptibly modify the countdown sequence in real time so that the traffic signal state change coincides approximately with the moment the countdown reaches its minimum count.