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.

A vehicle coordination system is provided for coordinating the trajectories of vehicles on a road network. The vehicle coordination system comprises a plurality of vehicles each having respective vehicle position tracking assemblies that are in communication with respective vehicle communication systems for transmitting vehicle state messages including positions of the vehicles. A task assignment allocator is provided that is arranged to generate task assignments for each of the plurality of vehicles, including destinations in the road network for the vehicles. A vehicle coordination assembly is in communication with the vehicle communication systems via a data network for receiving the vehicle state messages. The vehicle coordination assembly is configured to determine respective paths for each vehicle to arrive at their respective destinations and determine trajectory control commands for each vehicle to traverse their respective paths whilst optimizing a predetermined objective and avoiding active interactions of two or more of the vehicles occurring in any shared areas of the paths. The vehicle coordination assembly is configured to transmit the trajectory control commands to each vehicle. The predetermined objective may be an aggregate traversal time for the vehicles.

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.

A sensing system provided in a vehicle capable of running in an autonomous driving mode, includes: a LiDAR unit configured to acquire point group data indicating surrounding environment of the vehicle; and a LiDAR control module configured to identify information associated with a target object existing around the vehicle, based on the point group data acquired from the LiDAR unit. The LiDAR control module is configured to control the LiDAR unit so as to increase a scanning resolution of the LiDAR unit in a first angular area in a detection area of the LiDAR unit, wherein the first angular area is an area where the target object exists.

An end-to-end system for data generation, map creation using the generated data, and localization to the created map is disclosed. Mapstreams—or streams of sensor data, perception outputs from deep neural networks (DNNs), and/or relative trajectory data—corresponding to any number of drives by any number of vehicles may be generated and uploaded to the cloud. The mapstreams may be used to generate map data—and ultimately a fused high definition (HD) map—that represents data generated over a plurality of drives. When localizing to the fused HD map, individual localization results may be generated based on comparisons of real-time data from a sensor modality to map data corresponding to the same sensor modality. This process may be repeated for any number of sensor modalities and the results may be fused together to determine a final fused localization result.

Method for improving global positioning performance of a first road vehicle (10), the method comprising, by means of a data server (3, 4, 4″): acquiring data from onboard sensors (2a, 2b, 2c, 2d, 2e, 2f, 2g) arranged on the first road vehicle (10) and on at least two neighbouring road vehicles (10′, 10″, 10′″), the data comprising data on relative positions and data on heading angle and velocity of the road vehicles (10, 10′, 10″, 10′″), and acquiring global positioning data of at least two of the road vehicles (10, 10′, 10″, 10′″), processing (102) data comprising the global positioning data, the data, with corresponding timestamp, acquired from the onboard sensors (2a, 2b, 2c, 2d, 2e, 2f, 2g), and a motion model for each of the first road vehicle (10) and the at least two neighbouring road vehicles (10′, 10″, 10′″) using a data fusion algorithm, calculating adjusted global positioning data for the first road vehicle (10) and communicating (104) the adjusted global positioning data to a positioning system (6) of the first road vehicle (10).

Surface penetrating radar interrogates a region adjacent a pathway of the vehicle in response to activation by a user. An object detection system which is responsive to the radar transceiver is configured to recognize one or more spatial signatures of one or more detected objects in the region. A controller coupled to the radar transceiver and the object detection system is configured to (i) compare a respective spatial signature of at least one of the detected objects to a plurality of predetermined target signatures to detect an infrastructure asset, (ii) assess a perimeter around the detected infrastructure asset to estimate a severity of an obstruction blocking the infrastructure asset, and (iii) convey an alert message to the user when the estimated severity is greater than a threshold.

A radar system is disclosed that provides joint object detection and communication capabilities. The radar system includes a communication signal generator that provides a communication signal, a pre-distortion module that applies a pre-distortion to the communication signal to provide a pre-distorted communication signal, a linear frequency modulation (LFM) signal generator that provides a LFM signal, and a mixer that mixes the pre-distorted communication signal onto the LFM signal to provide a radar signal to be transmitted by the radar system. The radar system further includes an all-pass filter that filters a plurality of de-ramped reflected images of the radar signal to provide a filtered signal. Each de-ramped reflected image includes an associated image of the pre-distorted communication signal. The all-pass filter provides a linear group delay, and a non-linear phase response. The pre-distortion is an inverse of the non-linear phase response of the all-pass filter.

A system, a control unit, and a method for deciding a geofence event of a vehicle is disclosed. The control unit includes (i) a first road information generation module configured to generate first road information based on received map data and vehicle location data, the first road information including at least one or more candidate lanes based on a vehicle location, (ii) a second road information generation module configured to generate second road information based on received radar data, the second road information including at least a detected lane based on the radar data, (iii) a calculation module configured to perform integrated calculation on the first road information and the second road information to obtain a confidence level of each candidate lane, and determine a lane of the vehicle based on the calculated confidence level, and (iv) a decision module configured to decide, based on the determined lane, whether to trigger a geofence event, the geofence event including an event that the vehicle enters a geofence and an event that the vehicle exits a geofence.

Certain aspects provide a method for radar detection by an apparatus. The method including transmitting a radar waveform in transmission time intervals (TTIs) to perform detection of a target object. The method further includes varying the radar waveform across TTIs based on one or more radar transmission parameters.