Methods, systems, and apparatus, including computer programs encoded on computer storage media, for agent trajectory prediction using anchor trajectories.

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.

The disclosure relates to a method for preventing a collision of a vehicle with another road user carrying a mobile electronic device. The method comprises receiving identification data from the mobile electronic device at a receiving unit of the vehicle. Thereafter, a position of the mobile electronic device is calculated based on a signal strength of the signal carrying the received identification data. Subsequently, a collision risk of the road user carrying the mobile electronic device and the vehicle is determined. If a collision risk is determined, a warning activity for a user of the vehicle is triggered. The disclosure additionally relates to a collision warning system comprising a receiving unit for receiving identification data from a mobile electronic device and a data processing device. The data processing device is communicatively coupled to the receiving unit. Moreover, the data processing device comprises means for calculating a position of the mobile electronic device, for determining the collision risk, and for triggering a warning activity for a user of the vehicle. Furthermore, a vehicle having such a collision warning system is presented.

The present disclosure relates generally to autonomous vehicles, and more specifically to techniques for representing trajectories of objects such as traffic participants (e.g., vehicles, pedestrians, cyclists) in a computationally efficient manner (e.g., for multi-object tracking by autonomous vehicles). An exemplary method for generating a control signal for controlling a vehicle includes: obtaining a parametric representation of a trajectory of a single object in the same environment as the vehicle; updating the parametric representation of the single-object trajectory based on data received by one or more sensors of the vehicle within a framework of multi-object and multi-hypothesis tracker; and generating the control signal for controlling the vehicle based on the updated trajectory of the object.

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.

Provided is a driving assistance device capable of ensuring safety by allowing an opposite lane to be always seen from a time of entering an intersection to a start of executing a right/left turn and enabling the right/left turn at the intersection without missing a right/left turn execution opportunity. An own vehicle C is caused to stand by at a standby position Cb which allows detecting a following vehicle D of the own vehicle C by a rear side sensor and determining whether or not an oncoming right/left-turn standby vehicle E is capable of a right/left turn. In a case where it is determined that the oncoming right/left-turn standby vehicle E crosses the intersection and turns right or left, and the own vehicle C is capable of crossing the intersection and turning right or left, a right/left turn of the own vehicle C is started.

A vehicle control device includes a first control unit that executes, when an abnormality of a driver of a vehicle is detected, stop control, a second control unit that executes, when the vehicle is determined to have a risk of collision, deceleration control, a determination unit that identifies an object around the vehicle as a target candidate of the collision and determines whether or not there is the risk of the collision with the identified target candidate, and a setting unit that sets, when the abnormality is detected, an operation mode of the deceleration control to a special mode from a normal mode, the normal mode provided for cases in which the abnormality is undetected. The determination unit expands a range for identifying the object around the vehicle as the target candidate of the collision in the special mode as compared with the range in the normal mode.

An electronic device includes a transmission antenna that transmits a transmission wave, a reception antenna that receives a reflected wave that is the transmission wave having been reflected, and a control unit that detects an object that reflects the transmission wave, based on a transmission signal transmitted as the transmission wave and a reception signal received as the reflected wave. The control unit performs control to detect, as a target, an object having a motion characteristic of a motion of an arm of a person, among objects located around the electronic device.

The present technology relates to a vehicle that enables to improve designability while avoiding deterioration of safety and functionality of the vehicle.

The vehicle includes: a front line extending in a vehicle width direction on a front surface of a body; and a headlight arranged on left and right of the front surface, divided vertically by the front line, and configured to output a low beam from a portion above the front line and output a high beam from a portion below the front line. The present technology can be applied to, for example, a vehicle.

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.