The present disclosure relates to a vehicle radar device, a controlling method thereof, and radar system. A radar device according to an embodiment includes a transceiver being controlled to transmit the transmission signal in an operating frequency band according to a selection mode among a plurality of frequency band modes and to receive the reception signal through the receiving antenna, and a mode selector dynamically determining one of the plurality of frequency band modes as the selection mode based on at least one of a target distance to the target and a maximum detection distance for each frequency band. According to embodiments of the present disclosure, the distance resolution of the radar can be optimized by dynamically varying the frequency bandwidth linked with the maximum detection distance according to a target distance under specific driving conditions.

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 system for estimating a trajectory of a vehicle includes vehicle state sensors detecting a state of movement of the vehicle and environment sensors monitoring an environment of the vehicle. A free space module determines a free space corridor based on the detected state of movement of the vehicle and the monitored environment of the vehicle. A goal point determination module determines at least one goal point for the trajectory to be estimated based on the free space corridor. A trajectory planning module estimates a reference trajectory based on the at least one goal point and calculates a deviation between an actual trajectory and the reference trajectory of the vehicle. A trajectory control module minimizes the deviation between the actual trajectory and the reference trajectory of the vehicle and outputs optimal control parameters for the movement of the vehicle based on the minimized deviation.

Generally, the present disclosure is directed to systems and methods for streaming processing within one or more systems of an autonomy computing system. When an update for a particular object or region of interest is received by a given system, the system can control transmission of data associated with the update as well as a determination of other aspects by the given system. For example, the system can determine based on a received update for a particular aspect and a priority classification and/or interaction classification determined for that aspect whether data associated with the update should be transmitted to a subsequent system before waiting for other updates to arrive.

An FMCW radar transmission and reception apparatus radiates, via a transmission antenna, a beat frequency signal of a frequency modulation continuous wave (FMCW) and then receives, via a reception antenna, a reflected signal obtained from the radiated frequency modulation continuous wave (FMCW) signal that is reflected by a target and returns, wherein the frequency of a beat signal of a frequency modulation continuous wave (FMCW) radar can be effectively adjusted by configuring a plurality of phase locked loops (PLLs) used in a transmitter and a receiver, and using the same reference oscillation signal for the plurality of PLLs.

Provided are methods for automatically adjusting a vehicle seating area based on the characteristics of a passenger. In an example method, a seat adjustment system of a vehicle receives sensor data representing at least one measurement of a user exterior to the vehicle, determines at least one characteristic of the based on the sensor data, determines at least one modification to a seating area of the vehicle based on the at least one characteristic of the user, and causes the seating area to be adjusted in accordance with the at least one modification. Systems and computer program products are also provided.

Techniques and apparatuses are described that implement motion classification using low-level detections. In particular, a radar system identifies fused detections associated with an object and determines whether the fused detections indicate that the object is moving. If it is determined to be moving or moving perpendicular to the host vehicle, a current motion counter or perpendicular motion counter is incremented, respectively. A current motion flag and/or a perpendicular motion flag are set as true if the current motion counter or the perpendicular motion counter has a value greater than a threshold value, respectively. In response to setting either flag as true, the radar system increments a historical motion counter as true. The host vehicle is then operated based on the current motion flag, the perpendicular motion flag, and the historical motion counter. In this way, the radar system introduces hysteresis to improve the reliability and stability of motion classification.

A method may include obtaining first sensor data from a first sensor system and second sensor data from a second sensor system. The first and the second sensor systems may capture sensor data from a total measurable world. The method may include identifying a first object included in the first sensor data and a second object included in the second sensor data and determining first parameters corresponding to the first object and second parameters corresponding to the second object. The first parameters may be compared with the second parameters and whether the first object and the second object are a same object may be determined based on the comparing the first parameters and the second parameters. Responsive to determining that the first object and the second object are the same object, a set of objects representative of objects in the total measurable world including the same object may be generated.

Example embodiments described herein involve determining three dimensional data representative of an environment for an autonomous vehicle using radar. An example embodiment involves receiving radar reflection signals at a radar unit coupled to a vehicle and determining an azimuth angle and a distance for surfaces in the environment causing the radar reflection signals. The embodiment further involves determining an elevation angle for the surfaces causing the radar reflection signals based on phase information of the radar reflection signals and controlling the vehicle based at least in part on the azimuth angle, the distance, and the elevation angle for the surfaces causing the plurality of radar reflection signals. In some instances, the radar unit is configured to receive radar reflection signals using a staggered linear array with one or multiple radiating elements offset in the array.