Abstract: Systems and techniques are described for how to augment failure detection of a vehicle to detect failures that would otherwise go undetected by the control system of the vehicle, and to mitigate such failures when detected. Performance of the vehicle is observed over time, and based on these observations, the systems and methods determine whether the vehicle is exhibiting a performance characteristic that is indicative of a failure. If so, and if the control system does not identify the failure, then the system generates response data that causes the control system of the vehicle to respond to the performance characteristic that is indicative of the failure.

Abstract: Systems and techniques are described for on-demand traffic resource allocation. A system determines, for a traffic resource allocation request for a vehicle, a tracking capability for the vehicle. The tracking capability can be one of multiple different tracking capabilities, and tracking capability enables a tracking of a vehicle by a tracking method that is unique to the tracking capability and different from each other tracking capability. The system generates, for the vehicle, a tracking token that includes data that describes the tracking capability determined for the vehicle and that enables the traffic resource allocation system to track the vehicle using the tracking token and according to the tracking capability. The system then enables tracking of the vehicle according to the tracking capability described by the data in the tracking token.

Abstract: Systems and techniques are described for consuming data in an intelligent transport system. In some implementations, a system includes a display screen device and sensors. The sensors generates data describing sensor observations of a roadway at a first location and provides data describing the observations to the display screen device. The display screen device receives the data and determines an event and a type of the event. The display screen device displays second data indicative of the type of event, the second data being of a format that is consumable by a sensor on a vehicle traversing the roadway towards the first location, the sensor (i) located within a first resolution distance from the display screen device and (ii) located outside a second resolution distance of detecting the event, wherein the second data is used by an on-board processing system of the vehicle to adjust its driving behavior.

Abstract: Systems and techniques are described for a signal head processing system. A signal head detector can be mounted by or attached to an existing control light signal head and provide data indicating the state, i.e., on, off, blinking, of each light of the control light signal head. The signal head detector can communicate with a nearby gateway device. The gateway device, in turn, communicates with a back end server, and the back end server performs near real-time control light pattern analysis and provides near real-time light status. Alternatively, the signal head detector can communication directly with the based end server. The server analysis can include frequency of light change per canister, intersection geometry and lane flows, per-intersection control lighting patterns, seasonal, e.g., time of day, lighting patterns, anomaly detection and failure detection.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for monitoring characteristics of cameras used for road surveillance. In some implementations, a plurality of first image frames are obtained from a camera monitoring a specific geographical area. For each first image frame of the plurality of first image frames, a second image frame is generated by adjusting a viewpoint of the first image frame. A third image frame is generated by rasterizing the second image frame. Photonic content is identified in the third image frame. Invariant components are determined in a plurality of third image frames based on the photonic content identified in subsequent third image frames. Position changes of the camera are determined by identifying position changes of the invariant components in the subsequent third image frames. In response to determining the position changes of the camera, a position of the camera is adjusted.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for monitoring vehicles traversing a roadway using acoustic sensors. In some implementations, a server obtains data from an acoustic sensor monitoring road actors traversing a roadway at a first location. The server obtains data from an imaging sensor monitoring the road actors traversing the roadway at a second location. The server generates correlation data using the data from the acoustic sensor and the data from the imaging sensor. The server determines observations of the road actors traversing the roadway using the data from the acoustic sensor and the data from the imaging sensor. The server trains a machine-learning model to estimate characteristics of the road actors using the correlation data and the determined observations of the road actors from the imaging sensor and the acoustic sensor.

Abstract: Systems and techniques are described for consuming data in an intelligent transport system. In some implementations, a system includes a display screen device and sensors. The sensors generates data describing sensor observations of a roadway at a first location and provides data describing the observations to the display screen device. The display screen device receives the data and determines an event and a type of the event. The display screen device displays second data indicative of the type of event, the second data being of a format that is consumable by a sensor on a vehicle traversing the roadway towards the first location, the sensor (i) located within a first resolution distance from the display screen device and (ii) located outside a second resolution distance of detecting the event, wherein the second data is used by an on-board processing system of the vehicle to adjust its driving behavior.

Abstract: Systems and techniques are described for identifying, monitoring, and sharing vehicle information amongst sensors. In some implementations, a system includes a central server and a plurality of sensors. The plurality of sensors are positioned in a fixed location relative to a roadway. Each sensor in the plurality of sensors is configured to: detect vehicles in a first field of view on the roadway. For each detected vehicle, each sensor is configured to identify features of the detected vehicle and perform operations for each feature. The operations include generating feature data representing the feature, generating a unique identification of the detected vehicle from the detected vehicles by concatenating the feature data representing the identified features of the detected vehicle, and adding the unique identification to a list.

Abstract: Systems and techniques are described for enabling access and egress to dedicated lanes in a vehicular environment. In some implementations, a system include a central server, a gantry system, and a plurality of sensors. The plurality of sensors are positioned in a fixed location relative to a roadway. Each sensor in the plurality of sensors can detect vehicles in a field of view on the roadway. For each detected vehicle, each sensor can generate sensor data and provide the generated sensor data to the gantry system. The gantry system can receive the sensor data and determine whether the detected vehicle can access the dedicated lane based on the received sensor data. In response to determining the detected vehicle can access the dedicated lane, the gantry system can display an entry speed, open a gate to enable the detected vehicle access, and display an access indicator to the detected vehicle.

Abstract: Systems and techniques are described for identifying, monitoring, and sharing vehicle information amongst sensors. In some implementations, a system includes a central server and a plurality of sensors. The plurality of sensors are positioned in a fixed location relative to a roadway. Each sensor in the plurality of sensors is configured to: detect vehicles in a first field of view on the roadway. For each detected vehicle, each sensor is configured to identify features of the detected vehicle and perform operations for each feature. The operations include generating feature data representing the feature, generating a unique identification of the detected vehicle from the detected vehicles by concatenating the feature data representing the identified features of the detected vehicle, and adding the unique identification to a list.

Abstract: Systems and techniques are described for identifying, monitoring, and sharing vehicle information amongst sensors. In some implementations, a system includes a central server and a plurality of sensors. The plurality of sensors are positioned in a fixed location relative to a roadway. Each sensor in the plurality of sensors is configured to: detect vehicles in a first field of view on the roadway. For each detected vehicle, each sensor is configured to identify features of the detected vehicle and perform operations for each feature. The operations include generating feature data representing the feature, generating a unique identification of the detected vehicle from the detected vehicles by concatenating the feature data representing the identified features of the detected vehicle, and adding the unique identification to a list.

Abstract: Systems and techniques are described for identifying, monitoring, and sharing vehicle information amongst sensors. In some implementations, a system includes a central server and a plurality of sensors. The plurality of sensors are positioned in a fixed location relative to a roadway. Each sensor in the plurality of sensors is configured to: detect vehicles in a first field of view on the roadway. For each detected vehicle, each sensor is configured to identify features of the detected vehicle and perform operations for each feature. The operations include generating feature data representing the feature, generating a unique identification of the detected vehicle from the detected vehicles by concatenating the feature data representing the identified features of the detected vehicle, and adding the unique identification to a list.