Aspects of the present disclosure describe dynamic road traffic noise mapping using DFOS over a telecommunications network that enables mapping of road traffic-induced noise at any observer location. DFOS is used to obtain instant traffic data including vehicle speed, volume, and vehicle types, based on vibration and acoustic signal along the length of a sensing fiber along with location information. A sound pressure level at a point of interest is determined, and traffic data associated with such point is incorporated into a reference noise emission database and a wave propagation theory for total sound pressure level prediction and mapping. Real-time wind speed using DFOS—such as distributed acoustic sensing (DAS)—is obtained to provide sound pressure adjustment due to the wind speed.

System and methods for routing vehicles via rail and road are provided. One or more rail vehicles (e.g., trains, light rail, or other vehicles that travel exclusively via rail, etc.) and one or more hybrid vehicles that travel on road and rail may be tracked or monitored. The hybrid vehicle may be routed off the rail, such as onto an intersecting road, when a rail line interference or conflict exists with one or more rail vehicles.

A route conflict with emergency vehicle identification system includes an infrastructure receiving vehicle location, speed and trajectory information of an emergency vehicle. An automobile vehicle is in communication with the infrastructure and receives the vehicle location, speed and trajectory information of the emergency vehicle. A navigation system of the automobile vehicle operates using global positioning system (GPS) data and preloaded map data to generate a map identifying at least one roadway and a potential conflict on the at least one roadway between projected paths of the emergency vehicle and the automobile vehicle on the at least one roadway. A head-up display or a head-down display of the automobile vehicle visually presents the potential conflict.

An in-vehicle/out-vehicle cooperation device includes a data receiving unit configured to receive sensor data from the plurality of sensors via an in-vehicle network, an inter-end delay time estimation unit configured to estimate a transmission delay time of sensor data between ends from each of the plurality of sensors to a predetermined device communicable by a wireless communication device, a value determination unit configured to determine a value of sensor data output from the plurality of sensors based on a state of the vehicle and states of the plurality of sensors, and a selection unit configured to select a part of the sensor data based on transmission delay time estimated by the delay time estimation unit and the value determined by the value determination unit, and transmit a copy of the selected sensor data to the predetermined device via the wireless communication device.

In one embodiment, an attribute application associates content with a road segment. In operation, the attribute application generates a spatial reference identifier based on coordinates associated with the attribute. The attribute application then generates an attribute based on the content and the spatial reference identifier. Finally, the attribute application transmits the road segment attribute to a navigation system that performs at least one navigation operation based on a road database and the attribute. Because the attribute is specified based on spatial referencing, the attribute application requires fewer resources to generate attributes than conventional approaches that generate different attributes for different versions and formats of road databases.

In some implementations, a mobile device transmits traffic information to a server for analysis. The traffic information includes movement information including detected stops and durations of detected stops. The traffic information is analyzed to detect traffic patterns that indicate locations of stop signs and/or stop lights. The traffic information is analyzed to determine durations of stops at stop signs and/or stop lights. The durations of stops is associated with a time of day and/or day of the week. In some implementations, navigational routes is determined based stop sign and/or stop light information, including the delays attributable to detected stop signs and/or stop lights.

Systems and methods for vehicle-to-vehicle communications are provided. An example computer-implemented method includes obtaining from a first autonomous vehicle, by a second autonomous vehicle, a first compressed intermediate environmental representation. The first compressed intermediate environmental representation is indicative of at least a portion of an environment of the second autonomous vehicle. The method includes generating a first decompressed intermediate environmental representation by decompressing the first compressed intermediate environmental representation. The method includes determining, using one or more machine-learned models, an updated intermediate environmental representation based at least in part on the first decompressed intermediate environmental representation and a second intermediate environmental representation generated by the second autonomous vehicle. The method includes generating an autonomy output for the second autonomous vehicle based at least in part on the updated intermediate environmental representation.

Embodiments relate to a system, computer program product, and method for determining shelter areas for two-wheeler vehicles, and, more specifically, for dynamically distinguishing the behavior of two-wheeler vehicles and non-two-wheeler vehicles as an indicator of shelter areas from inclement weather. The behavior of the vehicles is distinguished through a plurality of two-wheeler vehicles slowing down and congregating at a particular location as a shelter against inclement weather, while non-two-wheeler vehicles may slow down, however, not stop proximate this location.

Systems and apparatuses for using machine learning to generate a safety output are provided. In some examples, data may be received from a plurality of sources, may be analyzed and one or more machine learning datasets may be generated based on the analyzed data. In some arrangements, data may be received from one or more vehicles. The vehicles may be autonomous, semi-autonomous, or non-autonomous, and/or configured to operate in one or more of those modes. The data may be evaluated based on the one or more machine learning datasets to determine a safety output associated with the data. The safety output may then be used to classify the data and/or to generate one or more instructions for operation of an autonomous vehicle. The instruction(s) may be transmitted to the autonomous vehicle and may modify operation of the vehicle (e.g., to improve safety associated with the vehicle).

A method and system includes predicting energy consumption of a vehicle using a statistical model. The method includes obtaining a plurality of input vectors for plurality of points in time, wherein each input vector includes a plurality of variables with a weight vector. Thereafter, the energy level for each input vector is captured for each point in time. Subsequent to capturing the energy level, the method includes predicting a change in energy level of the vehicle using the statistical model.