Provided is a vehicle control system capable of grouping objects detected by a plurality of sensors with high accuracy in a short time. A vehicle control system 100 includes an information integration device 120 that groups first detection information and second detection information of a first sensor 111 and a second sensor 112 and outputs integrated detection information, and a vehicle control device 130 that controls a vehicle on the basis of the integrated detection information. An arithmetic device 121 of the information integration device 120 stores first time-series information of the first detection information and second time-series information of the second detection information in a storage device 122, calculates a correction parameter of the first detection information by grouping the first time-series information and the second time-series information when the first sensor 111 and the second sensor 112 detect the same object, calculates correction information obtained by correcting the first detection information using the correction parameter, and outputs the integrated detection information by instantaneous value grouping using the correction information and the second detection information.

The present disclosure relates to a route providing device which is provided in a vehicle communicating with a server and provides a vehicle route, the route providing device including: a communication unit which receives EHP information including at least one among visual field information for autonomous driving and an optimal route at the traffic-lane level, which are generated from the server; a main EHR which receives EHP information from different servers through the communication unit; and a sub-EHR for transmitting data processed by the main EHR to at least one among electric parts provided in the vehicle.

Embodiments of the disclosed systems and methods provide techniques for dynamically allocating processing tasks between in-vehicle and remote processing resources. In various embodiments, aspects of the disclosed systems and methods may advantageously use relatively low latency edge cloud and/or cloud processing resources accessed via higher speed wireless networks to enhance processing resources available to a vehicle for use in a variety of control and/or operation decisions. Consistent with various disclosed embodiments, processing tasks may be dynamically allocated based on relative impact and/or importance to safe vehicle operation, network latency between a vehicle and remote processing resources, available network bandwidth between a vehicle and remote processing resources, network traffic, processing complexity, processing resource availability, and/or the like.

A controller receives sensor data during a ride and provides it to a server system. A passenger further provides feedback concerning the ride in the form of some or all of an overall rating, flagging of ride anomalies, and flagging of road anomalies. The sensor data and feedback are input to a training algorithm, such as a deep reinforcement learning algorithm, which updates an artificial intelligence (AI) model. The updated model is then propagated to controllers of one or more autonomous vehicle which then perform autonomous navigation and collision avoidance using the updated AI model.

The cloud includes a server and a storage device. The storage device includes a road surface information map. When a first sampling distance is equal to or longer than a first distance threshold, the server performs re-sampling to interpolate data in such a manner that sampling positions located at a second sampling distance and unsprung mass member displacements of the respective sampling positions exist so as to produce re-sampled data-for-producing-map. The server stores a sub-sectional unsprung mass displacement in a storage area corresponding to a sub-section of the road surface information map, based on the re-sampled data-for-producing-map.

An alarm device configured to generate an alarm to a driver inside a vehicle, includes processing circuitry configured to generate delay time information based on a first reference level and at least a portion of sound source signals that are generated by a plurality of microphones in the vehicle based on a sound generated from outside of the vehicle. The processing circuitry is further configured to generate position parameters based on a second reference level and at least a portion of the delay time information. The processing circuitry is further configured to generate, based on the position parameters, candidate position information representing candidate positions on which the sound source is expected to be located, and generate final position information based on a third reference level and the candidate position information.

Various embodiments include methods and systems for autonomous driving systems for using map data in performing an autonomous driving function. Various embodiments may include accessing map data regarding an object or feature in the vicinity of the vehicle, accessing confidence information associated with the map data, and using the confidence information in performing an autonomous or semi-autonomous driving action by the processor. The confidence information may be stored in the map database or in a separate data structure accessible by the processor. Methods of generating map safety and confidence information may include receiving information regarding a map object or feature including a measure of confidence in the information, using the received measure of confidence to generate safety and confidence information regarding the object or feature, and storing the safety and confidence information for access by vehicle autonomous and semi-autonomous driving systems.

A method may include obtaining one or more inputs in which each of the inputs describes at least one of: a state of an autonomous vehicle (AV) or a state of an object; and identifying a prediction context of the AV based on the inputs. The method may also include determining a relevancy of each object of a plurality of objects to the AV in relation to the prediction context; and outputting a set of relevant objects based on the relevancy determination for each of the plurality of objects. Another method may include obtaining a set of objects designated as relevant to operation of an AV; selecting a trajectory prediction approach for a given object based on context of the AV and characteristics of the given object; predicting a trajectory of the given object using the selected trajectory prediction approach; and outputting the given object and the predicted trajectory.

The cloud includes a server and a storage device. The storage device includes a road surface information map. When a first sampling distance is equal to or longer than a first distance threshold, the server performs re-sampling to interpolate data in such a manner that sampling positions located at a second sampling distance and unsprung mass member displacements of the respective sampling positions exist so as to produce re-sampled data-for-producing-map. The server stores a sub-sectional unsprung mass displacement in a storage area corresponding to a sub-section of the road surface information map, based on the re-sampled data-for-producing-map.

The present invention discloses a method, apparatus, device and computer-readable medium for trajectory compression, and relates to the technical field of computers. One specific implementation of the method comprises: screening, among trajectory points of a traveling trajectory, reserved trajectory points of the traveling trajectory according to deviation degrees of the trajectory points from roads; determining, in conjunction with traveling periods of the reserved trajectory points, the reserved trajectory points as key trajectory points, the traveling period being a time interval from a previous trajectory point of the reserved trajectory point to a next trajectory point of the reserved trajectory point; adding the starting point of the traveling trajectory, the key trajectory points and the ending point of the traveling trajectory to a compressed trajectory so as to compress the traveling trajectory. This implementation can reduce the time complexity of compression of trajectory data, and thereby decrease the time consumption for compression.