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.

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.

Vehicles and methods for tracking an object and controlling a vehicle based on the tracked object. A Radar-Doppler (RD) map is received from the radar sensing system of the vehicle and relative acceleration of an object with respect to the vehicle is detected based on the RD map so as to provide acceleration data. A current frame of detected object data is received from a sensing system of the vehicle. When the relative acceleration has been detected, a tracking algorithm is adapted to reduce the influence of the predictive motion model or the historical state of the object and the object is tracked using the adapted tracking algorithm so as to provide adapted estimated object data based on the object tracking. One or more vehicle actuators are controlled based on the adapted estimated object data.

A map construction method and a related apparatus are provided. The method includes: obtaining, based on manual driving track data and/or an obstacle grid map, road information, intersection information, and lane information of a region through which a vehicle has traveled; obtaining road traffic direction information based on the manual driving track data and the road information, and obtaining lane traffic direction information based on the lane information and the road traffic direction information; obtaining intersection entry and exit point information based on the intersection information and the lane traffic direction information; and performing, based on the intersection entry and exit point information, an operation of generating a virtual topology center line to obtain an autonomous driving map of the region through which the vehicle has traveled, where the virtual topology center line is a traveling boundary line of the vehicle in an intersection region.

An autonomous vehicle (AV) includes a radar sensor system and a computing system that computes velocities of an object in a driving environment of the AV based upon radar data that is representative of radar returns received by the radar sensor system. The AV can be configured to compute a first velocity of the object based upon first radar data that is representative of the radar return from a first time to a second time. The AV can further be configured to compute a second velocity of the object based upon second radar data that includes at least a portion of the first radar data and further includes additional radar data representative of a radar return received subsequent to the second time. The AV can further be configured to control one of a propulsion system, a steering system, or a braking system to effectuate motion of the AV based upon the computed velocities.

Examples relating to vehicle velocity calculation using radar technology are described. An example method performed by a computing system may involve, while a vehicle is moving on a road, receiving, from two or more radar sensors mounted at different locations on the vehicle, radar data representative of an environment of the vehicle. The method may involve, based on the data, detecting at least one scatterer in the environment. The method may involve making a determination of a likelihood that the at least one scatterer is stationary with respect to the vehicle. The method may involve, based on the determination being that the likelihood is at least equal to a predefined confidence threshold, calculating a velocity of the vehicle based on the data from the sensors. The calculated velocity may include an angular and linear velocity. Further, the method may involve controlling the vehicle based on the calculated velocity.

A method can be used for adaptive parking of a vehicle. A parking area is determined around a programmed destination of the vehicle. The parking area has more than one available parking spot for the vehicle. Parking data is acquired via a wireless communication network. The parking data for each parked vehicle includes a parking position and an individual sensing coverage of an environment sensor system of the respective parked vehicle scanning the traffic environment within the parking area. Available parking spots are ranked based on a calculated overall sensing coverage and a recommended parking spot is determined among the available parking spots based on overall sensing coverage of the traffic environment in the parking area.

According to various embodiments, systems and methods described in the disclosure combine mapped features with point cloud features to improve object detection precision of an autonomous driving vehicle (ADV). The map features and the point cloud features can be extracted from a perception area of the ADV within a particular angle view at each driving cycle based on a position of the ADV. The map features and the point cloud features can be concatenated and provided to a neutral network for object detections.