In one embodiment, a method includes determining an operational status of a vehicle including a radar antenna. The operational status is related to autonomous-driving operations of the vehicle in an environment. The method includes determining an expected amount of signaling resources associated with the radar antenna to be utilized by the vehicle while the vehicle performs the autonomous-driving operations, based at least on the operational status of the vehicle and the environment. The method includes determining to switch one or more of the signaling resources associated with the radar antenna from a first mode to a second mode based on the expected amount of signaling resources to be utilized by the radar antenna while the vehicle performs the autonomous-driving operations. The method includes causing the one or more of the signaling resources associated with the radar antenna to switch from the first mode to the second mode.

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.

A plurality of images can be acquired from a plurality of sensors and a plurality of flattened patches can be extracted from the plurality of images. An image location in the plurality of images and a sensor type token identifying a type of sensor used to acquire an image in the plurality of images from which the respective flattened patch was acquired can be added to each of the plurality of flattened patches. The flattened patches can be concatenated into a flat tensor and add a task token indicating a processing task to the flat tensor, wherein the flat tensor is a one-dimensional array that includes two or more types of data. The flat tensor can be input to a first deep neural network that includes a plurality of encoder layers and a plurality of decoder layers and outputs transformer output. The transformer output can be input to a second deep neural network that determines an object prediction indicated by the token and the object predictions can be output.

Methods and systems are disclosed for cross-validating a second sensor with a first sensor. Cross-validating the second sensor may include obtaining sensor readings from the first sensor and comparing the sensor readings from the first sensor with sensor readings obtained from the second sensor. In particular, the comparison of the sensor readings may include comparing state information about a vehicle detected by the first sensor and the second sensor. In addition, comparing the sensor readings may include obtaining a first image from the first sensor, obtaining a second image from the second sensor, and then comparing various characteristics of the images. One characteristic that may be compared are object labels applied to the vehicle detected by the first and second sensor. The first and second sensors may be different types of sensors.

A method and apparatus with vehicle radar control is disclosed. An apparatus with vehicle radar control includes a radio frequency (RF) transceiver including a transmitting antenna array and a receiving antenna array, and at least one processor configured to collect environmental information of the vehicle, determine a radar mode of the vehicle based on the collected environmental information, generate one or more control signal configured to control one or more of the transmitting antenna array and the receiving antenna array based on the determined radar mode, and provide the generated one or more control signals to the RF transceiver, wherein one or more of the transmitting antenna array and the receiving antenna array operate according to the one or more generated control signals.

A device for detecting a rear collision of a vehicle, the device including a first sensor unit that is disposed on one side of a back of the vehicle and detects a target vehicle positioned behind the vehicle to generate first sensing data, a second sensor unit that is disposed on the other side of the back of the vehicle and detects the target vehicle to generate second sensing data, an ultrasonic sensor that is mounted on the back of the vehicle, and detects a proximity of the target vehicle to generate third sensing data, and a controller that determines a relative speed and a relative distance with the target vehicle using the first sensing data and the second sensing data, determines the proximity of the target vehicle using the third sensing data, and determines an output of a command of unfolding an airbag outwardly mounted on the back of the vehicle and an output of a command of controlling a vehicle headrest.

Disclosed are techniques for improving an advanced driver-assistance system (ADAS) using a secure channel area. In one embodiment, a method is disclosed comprising establishing a secure channel area extending from at least one side of a first vehicle; detecting a presence of a second vehicle in the secure channel area; establishing a secure connection with the second vehicle upon detecting the presence; exchanging messages between the first vehicle and the second vehicle, the messages including a position and speed of a sending vehicle; taking control of a position and speed of the first vehicle based on the contents of the messages; and releasing control of the position and speed of the first vehicle upon detecting that the secure connection was released.

An image processing device 10 includes an image interface 18, a memory 19, and a controller 20. The image interface 18 acquires a captured image. The positions of specific feature points in a world coordinate system and reference positions of the specific feature points are stored in the memory 19. The controller 20 detects the specific feature points in the captured image. In a case where discrepancy between the position in the captured image and the reference position is found with regard to a predetermined percentage or more of the specific feature points, the controller 20 recalculates a calibration parameter.

The subject matter of this specification can be implemented in, among other things, systems and methods of optical sensing that utilize optimized processing of multiple sensing channels for efficient and reliable scanning of environments. The optical sensing includes multiple optical communication lines that include coupling portions configured to facilitate efficient collection of various received beams. The optical sensing system further includes multiple light detectors configured to process collected beams and produce data representative of a velocity of an object that generated the received beam and/or a distance to that object.

Generally, the present disclosure relates to a forward deployed sensor system or, in a specific embodiment, a forward deployed radar (FDR) system. The forward deployed sensor system includes a radar system and may also include other types of sensors such as optical sensors, acoustic sensors including sonar, and electromagnetic sensors. Further, the forward deployed sensor system may also include a communication system such as a full spectrum receiver/transmitter, a ship to ship relay transponder, a satellite communication system, and global positioning system (GPS) capability. The forward deployed sensor system is able to detect objects in the air, on the sea, and underwater, and communicate such detection to a ship, submarine, aircraft, satellite, or other remote location. Such systems may be used to augment the protection of shipping lanes by military or security forces to allow for peaceful commerce and utility of the sea by all nations.