A deployment of sensors transmit radio frequency (RF) signals into an area of interest. The radar maps are generated from the reflected signals, including a static radar map and a dynamic radar map. Multipath and radar sidelobes are removed from the radar maps using a neural network to produce a density map. The neural network can be trained in two phases: a training phase that uses training data from a training site and a transfer learning phase that uses training data from the area of interest.

Embodiments described herein may provide a method for using vehicle sensor data to identify where road work exists within a road network. Methods may include: receiving probe data and sensor data from a plurality of probe apparatuses traveling along a sequence of road segments; identifying, from the sensor data, one or more indicators of a beginning of a road work area; identifying, from the sensor data, binary indicators of the presence of road work or a lack of presence of road work along the sequence of road segments; and determining, based on the one or more indicators of a beginning of a road work area and the binary indicators of the presence of road work or the lack of road work along the sequence of road segments, a probability of road work occurring along one or more road segments of the sequence of road segments.

Example techniques are described for determining a dynamic traffic-management plan based on factors such as predicted increases in traffic and known-structurally-deficient transportation infrastructure. Traffic patterns may be re-routed, particularly during high-congestion events, to reduce or avoid excessive weight loads travelling across weakened sections of roads, bridges, or the like.

A method determines support points for estimating a progression of roadside development of a road. The method determines a position of a first support point in the surroundings of a vehicle; determines a plurality of regions in a travel direction and/or counter to the travel direction of the vehicle on the basis of the position of the first support point; and determines support points of roadside development for each of the determined regions in the travel direction, counter to the travel direction and left and right of the vehicle.

Systems and methods are provided to determine traffic configuration parameters, such as location and speed, that are correlated with optimal traffic flow specific to particular road regions. In a specific embodiment, the disclosure is directed to a vehicle positioning system which utilizes a multi-client server application model configured to perform predictive analysis based upon data collected from a plurality of data streams, infrastructure elements, and vehicles. In a particular implementation, roadways may be partitioned into road regions which may be associated with vehicle configuration templates. Vehicle configuration templates may define instructions for automated vehicle driving parameters within a particular road region. In a specific embodiment, the vehicle positioning system may invoke transition sequences based upon real-time traffic data to modify a given traffic configuration.

A server collects data that represents a status of a non-connected vehicle that does not exchange cooperative awareness messages (CAMs) with the server. The server synthesizes values of fields in a CAM for the non-connected vehicle based on the data. The server also incorporates information in the CAM to indicate that the CAM represents the non-connected vehicle. The server transmits the CAM to one or more connected vehicles. A connected vehicle receives a CAM that includes information indicating whether the CAM represents a non-connected vehicle. The connected vehicle determines a degree of uncertainty associated with values of fields in the CAM based on the information. The connected vehicle selectively takes an action based on the degree of uncertainty.

This application provides an information transmission method and apparatus, relates to interference processing of a cooperative radar, and is applicable to internet of vehicles scenarios, such as a vehicle-to-everything V2X scenario, a long term evolution-vehicle LTE-V scenario, and a vehicle-to-vehicle V2V scenario. Therefore, information related to a transmit power can be transmitted through an interface of a radar detection apparatus, and a capability of an automated driving system or an advanced driver assistant system ADAS is improved, to adapt to a variable driving environment. The information transmission method includes: A first detection apparatus receives indication information, where the indication information is used to indicate the first detection apparatus to determine a first transmit power used for transmitting a radar signal; and the first detection apparatus determines the first transmit power based on the indication information.

A vehicle control apparatus acquires a position of an other vehicle that is present ahead of the own vehicle in its traveling direction, and uses the acquired position of the other vehicle to calculate a movement locus, which is a past path of the other vehicle. The vehicle control device calculates a lateral movement amount of the movement locus in a predetermined range in the traveling direction of the own vehicle, the lateral movement amount being an amount of change in position in a lateral direction which is a direction intersecting the traveling direction, and calculates an average value of the lateral movement amounts. The vehicle control apparatus excludes, from the movement loci, a movement locus having a lateral movement amount whose difference from the average value is larger than a predetermined value, and calculates the predicted path based on remaining movement loci.

Provided are a system and a method for providing service to recommend customized exercise used for autonomous vehicle. The system and method are configured to determine whether the user does the exercise safely and freely during autonomous control of the vehicle through the monitoring system in the autonomous vehicle and recommend the customized exercise used for the autonomous vehicle according to a situation of the user and the situation of the autonomous control and to determine whether the user does the exercise safely and freely during the autonomous control of the vehicle through the monitoring system in the autonomous vehicle and recommend the autonomous vehicle in which the user can do the recommended exercise according to the situation of the user and the situation of the autonomous control.

A method, apparatus and computer program product are provided to determine lane status confidence indicators of lane status predictions such as closures and/or shifting. Lane statuses and corresponding confidence indicators are determined based on probe data, such as probe data collected from vehicle and/or mobile devices traveling along a road segment. Probe data may be partitioned into clusters and compared to partitioned subsets of the probe data. Cluster stability for the segment and corresponding lane status confidence indicators can be determined based on the comparison. Accordingly, determinations of whether to transmit predicted lane statuses to another system, service, and/or user device may be made.