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.

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.

One embodiment of the present disclosure relates to a vehicle radar apparatus and a method of controlling the same. The radar apparatus according to the present embodiment may include an antenna unit including Nt transmitting antennas and Nr receiving antennas, wherein one of the Nt transmitting antennas is vertically offset from the other transmitting antennas, or one of the Nr receiving antennas is vertically offset from the other receiving antennas, a transceiver configured to control the Nt transmitting antennas to transmit a phase shift transmission signal having N different phase shift values (an) and control the Nr receiving antennas to receive a reflected signal reflected from a target, and a signal processor configured to determine a height (h) of the target based on a discrete phase shift value (a.sub.max) that is a phase shift value having the greatest reception power among N phase shift values.

Aspects of the present disclosure involve systems, methods, and devices for fault detection in a Lidar system. A fault detection system obtains incoming Lidar data output by a Lidar system during operation of an AV system. The incoming Lidar data includes one or more data points corresponding to a fault detection target on an exterior of a vehicle of the AV system. The fault detection system accesses historical Lidar data that is based on data previously output by the Lidar system. The historical Lidar data corresponds to the fault detection target. The fault detection system performs a comparison of the incoming Lidar data with the historical Lidar data to identify any differences between the two sets of data. The fault detection system detects a fault condition occurring at the Lidar system based on the comparison.

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.