A method includes receiving vehicle data for a plurality of vehicles in a parking facility over time, the vehicle data including a time t1 indicating when each of the vehicles leaves its corresponding parking spot in the parking facility, and a time t2 indicating when each of the plurality of vehicles reaches an exit of the parking facility. An egress time is determined for each of the plurality of vehicles based at least in part on the corresponding times t1 and t2. A timestamp is assigned to each of the egress times, resulting in timestamped egress times. A current egress time of the parking facility is estimated based at least in part on the timestamped egress times. An action recommendation is based at least in part on the current egress time of the parking facility and is outputted for subsequent viewing by the driver of the particular vehicle.

A network device receives, from multiple smart cameras located at different road segments of multiple real-world road segments, first data describing a location and configuration of each of the multiple real-world road segments, and second data describing movement of one or more objects at each of the multiple real-world road segments. The network device generates multiple virtual road segments based on the first data, wherein each of the multiple virtual road segments describes one of the multiple real-world road segments; and uses a physics engine to simulate, based on the generated multiple virtual road segments and the second data, movement of vehicles or pedestrians through the multiple real-world road segments to analyze traffic characteristics, trends or events. The network device generates road segment alterations for reconfiguring one or more of the multiple real-world road segments based on the analysis of the traffic characteristics, trends, or events.

A system and a method for generating traffic information are provided. The system for generating traffic information includes a vehicle that obtains a location and a surrounding image in real time, and a server that receives the location and the surrounding image from the vehicle, and calculates a time required to pass through a specified section and an average speed passing through the specified section based on the location and the surrounding image from the vehicle.

Systems and methods limit communication of redundant telematics data in V2X systems. A communications management device receives telematics data from multiple sources in a service area and calculates a trajectory each of the objects identified by the telematics data. The communications management device selects high vulnerability trajectories based on the calculated trajectories and identifies when the telematics data from different sources, of the multiple sources, corresponds to a same vehicle. When duplicate sources are determined to provide tracking data corresponding to the same vehicle, the communications management device reports (to a collision avoidance system) the tracking data from only the most accurate of the duplicate sources.

The correctness of information transmitted by a vehicle is assessed. Vehicle information about vehicle characteristics of a vehicle is received, wherein a vehicle trust value is assigned to the vehicle. Detector information about the vehicle characteristics is received, which is based on a detection of the vehicle characteristics by at least one detector outside the vehicle, wherein a detector trust value is assigned to the detector. Then, the correctness of the vehicle information is assessed. A correctness value is assigned to the vehicle information, and the vehicle trust value is updated. The vehicle information may be discarded when the correctness value is too low. Vehicles with a high trust value may validate other vehicles, which may in turn validate even more vehicles. In this way, a chain of vehicles with a high trust value (or trusted vehicles) may be established.

A road management apparatus including a communication unit and a microprocessor. The microprocessor is configured to perform acquiring detection information via the communication unit, the detection information being information from a detection device mounted on each vehicle, and estimating a traveling lane on which each vehicle travels based on the detection information. The memory stores driving information including at least one of vehicle speed information and operation information of a direction indicator in association with the traveling lane, the driving information being obtained from the detection information acquired in each of a first period and a second period earlier than the first period. The microprocessor is configured to further perform estimating an occurrence of an abnormality on a road in the first period, in accordance with a degree of divergence between the driving information in the first period and the second period stored in the memory.

A computer-implemented method of updating an auto insurance policy is provided. The method may include (1) determining that a customer's mobile device has a Telematics Application (“App”) installed on it, the Telematics App configured to (i) receive telematics data associated with another vehicle via a wireless communication broadcast; (ii) determine a travel event from analysis of the telematics data received, and (iii) generate a corrective action based upon the telematics data received or travel event determined that alleviates the risk of vehicle collision. The method may also include (2) monitoring, with the customer's permission, an amount or percentage of usage of the Telematics App on the customer's mobile device while the customer is driving in an insured vehicle; and (3) adjusting an insurance policy premium or discount based upon usage of the Telematics App to facilitate rewarding risk-averse drivers and encourage usage of risk mitigation or prevention technology.

A processing apparatus for generating route navigation data is provided, to, generate training data based on road network data corresponding to a network of roads in a defined geographical area, and journey data sets, each journey data set comprising data indicative of a journey by a road user through the network of roads and being derived using geolocation transmissions from a communications device of the road user, train a classifier model based on the training data, apply the trained classifier model on road data corresponding to a road in the defined geographical area, for the trained classifier model to predict a direction of traffic flow on the road, and generate the route navigation data indicative of the predicted direction of the traffic flow on the road. A method for generating route navigation data is also provided.

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.

Techniques for determining that a first vehicle is associated with a reverse state, and controlling a second vehicle based on the reverse state, are described herein. In some examples, the first vehicle may provide an indication that the first vehicle will be executing a reverse maneuver, such as with reverse lights on the vehicle or by positioning at an angle relative to a road or parking space to allow for the reverse maneuver into a desired location. A planning system of the second vehicle (such as an autonomous vehicle) may receive sensor data and determine a variety of these indications to determine a probability that the vehicle is going to execute a reverse maneuver. The second vehicle can further determine a likely trajectory of the reverse maneuver and can provide appropriate accommodations (e.g., time and/or space) to allow the second vehicle to execute the maneuver safely and efficiently.