Embodiments herein can determine an optimal route for an autonomous electric vehicle. The system may score viable routes between the start and end locations of a trip using a numeric or other scale that denotes how viable the route is for autonomy. The score is adjusted using a variety of factors where a learning process leverages both offline and online data. The scored routes are not based simply on the shortest distance between the start and end points but determine the best route based on the driving context for the vehicle and the user.

Among other things, equipment is located at an intersection of a transportation network. The equipment includes an input to receive data from a sensor oriented to monitor ground transportation entities at or near the intersection. A wireless communication device sends to a device of one of the ground transportation entities, a warning about a dangerous situation at or near the intersection, there is a processor and a storage for instructions executable by the processor to perform actions including the following. A machine learning model is stored that can predict behavior of ground transportation entities at or near the intersection at a current time. The machine learning model is based on training data about previous motion and related behavior of ground transportation entities at or near the intersection. Current motion data received from the sensor about ground transportation entities at or near the intersection is applied to the machine learning model to predict imminent behaviors of the ground transportation entities. An imminent dangerous situation for one or more of the ground transportation entities at or near the intersection is inferred from the predicted imminent behaviors. The wireless communication device sends the warning about the dangerous situation to the device of one of the ground transportation entities.

A system includes one or more video capture devices and a processor coupled to each video capture device. Each processor is operable to direct its respective video capture device to obtain an image of a monitored area and process the image to identify objects of interest represented in the image. The processor is also operable to generate bounding perimeter virtual objects for the identified objects of interest, each bounding perimeter virtual object surrounding at least part of its respective object of interest. The processor is further operable to determine danger zones for the identified objects of interest based on the bounding perimeter virtual objects. The processor is further operable to determine at least one near-miss condition based at least in part on an actual or predicted overlap of danger zones for multiple objects of interest, and may optionally generate an alert at least partially in response to the near-miss condition.

The present invention relates to a method for alerting drivers and/or autonomous vehicles of high risk scenarios. The method includes obtaining positional data of a vehicle, where the positional data is indicative of geographical position and heading of the vehicle. The method further includes obtaining environmental data of the vehicle, where the environmental data is indicative of state of the surrounding environment of the vehicle. The method includes determining, by means of trained model, accident intensity for upcoming road portion for the vehicle, the trained model being configured to determine accident intensity associated with the upcoming road portion based on the obtained environmental data and the obtained positional data. Then, if the determined accident intensity exceeds threshold, the method comprises transmitting signal indicating approaching high risk scenario to a Human-Machine-Interface, HMI, of the vehicle and/or to a control system of the vehicle.

Methods and systems for managing traversals of one or more vehicles through a traffic intersection are proposed. In one example, a method comprises: receiving a first message including one or more motion characteristics of one or more vehicles approaching the traffic intersection; forming a motion group; grouping a subset of the one or more vehicles into the motion group based on the one or more motion characteristics; assigning a leader vehicle for the motion group; determining a group maneuver target for the motion group to traverse through the traffic intersection; and transmitting, to the leader vehicle of the motion group, a second message including the group maneuver target for the motion group, to enable the leader vehicle of the motion group to control a motion of each member vehicle of the motion group based on the group maneuver target.

The methods, devices, and systems discussed herein involve a mobile wireless communication device (WCD) receiving a first discontinuous reception (DRX) cycle configuration from a network. In some examples, the first DRX cycle configuration is a U E-specific DRX cycle configuration. In other examples, the first DRX cycle configuration is a default DRX cycle configuration. The WCD also receives a second DRX cycle configuration that is different from the first DRX cycle configuration from the network. In some examples, the second DRX cycle configuration is compatible with a sidelink DRX cycle configuration of a D2D-capable device. Upon entering a first area that is near a potential safety scenario, such as an intersection, the WCD receives a signal containing an indicator. In response to receiving the signal, the WCD utilizes the second DRX cycle configuration to detect one or more device-to-device (D2D) transmissions from a D2D-capable device.

A vehicle control device includes a communication unit configured to communicate with a plurality of autonomous driving vehicles configured to perform autonomous traveling, and a processor. The processor is configured to, when an abnormality occurs in or around at least one first autonomous driving vehicle among the plurality of autonomous driving vehicles, determine a travel instruction for controlling traveling of the first autonomous driving vehicle, transmit the travel instruction to the first autonomous driving vehicle via the communication unit, set a priority representing the degree of the priority in which an instruction operator is notified of the travel instruction in the order determined according to the content of the abnormality, notify any one of at least one instruction terminal of the determined travel instruction in the order of highest priority, and receive a result of checking the determined travel instruction from the instruction terminal.

An information processing apparatus includes a risk area identification unit configured to identify a risk area outside a moving object and a transmission control unit configured to perform control for transmitting risk area information representing the risk area identified by the risk area identification unit to a server configured to retain information related to the risk area, in which the risk area identification unit is configured to identify an area defined by a plurality of points as the risk area, and the risk area identification unit is configured to identify, based on a boundary between a first area in which the moving object is prohibited from moving and a second area in which the moving object is allowed to move, a shape of the risk area on a side of the first area.

A communication device includes a communication unit configured to receive first path information from a first vehicle and to receive second path information from a second vehicle, and a processor configured to determine a possibility of the first and second vehicles entering a predetermined area based on the first and second path information, and based on the possibility being greater than a reference, transmit a message for controlling at least one of the first vehicle or the second vehicle through the communication unit to allow the first and second vehicles to enter the predetermined area at different time points.

The methods, devices, and systems discussed herein periodically broadcast a signal containing an indicator to a first area that is near a potential safety scenario, such as an intersection. Upon entering the first area, a mobile wireless communication device (WCD) receives the broadcast signal. In response to receiving the broadcast signal, the WCD activates a collision-avoidance procedure. In some examples, the collision-avoidance procedure includes sensing device-to-device (D2D) resources in order to detect D2D transmissions from an approaching D2D-capable device (e.g., a vehicle). In other examples, the collision-avoidance procedure includes transmitting a periodic D2D safety message using D2D resources.