A method, including: acquiring a point cloud frame including point cloud data of a pedestrian; projecting the point cloud data of the pedestrian to a ground coordinate system to obtain projection point data of the pedestrian; determining a direction of a connection line between the two shoulders of the pedestrian, based on a location distribution of the projection point data; extracting a point cloud of a stable region from the point cloud data of the pedestrian based on the direction of the connection line between the two shoulders of the pedestrian, a form change range of the stable region when the pedestrian moves being smaller than form change ranges of other regions of the pedestrian; and determining movement information of the pedestrian based on a coordinate of a center point of the point cloud of the stable region in a plurality of consecutive point cloud frames.

This document describes techniques, apparatuses, and systems that can implement auction-based cooperative perception for autonomous and semi-autonomous driving systems. The described techniques, apparatuses, and systems cooperatively use perception systems of an autonomous or semi-autonomous vehicle (AV) in a fleet of connected AVs to provide perception data to the entire fleet. An AV of the fleet is selected to act as an auction system (e.g., an auctioneer) and sends an announcement offering perception tasks to the fleet for bidding. The AVs of the fleet determine whether they have the communication and computational capabilities to perform the tasks and, if so, submit bids to perform one or more tasks. The auctioneer awards the tasks, and the bid-winning AV(s) perform the tasks and update the fleet. In this way, the described techniques, apparatuses, and systems can provide perception services with increased coverage and quality, which can make autonomous and semi-autonomous driving systems safer.

The present invention provides an autonomous vehicle lane change monitoring method and lane change monitoring system, wherein the lane change monitoring system comprises at least a reference vehicle, a test road, a network communication device, and a control platform, wherein the reference vehicle and the to-be-tested vehicle traveling on the test road, wherein the control platform sends a control instruction to the reference vehicle and the to-be-tested vehicle through the network communication device for the to-be-tested vehicle to change lane on the test road when appropriate, the driving data of the to-be-tested vehicle is transmitted to the control platform through the network communication device, so as for the control platform to evaluate the lane change performance of the to-be-tested vehicle based on the driving data.

An autonomous vehicle includes a detection system for identifying the presence of authorities or emergency vehicles on a road, such as law enforcement vehicles, fire engines, fire trucks, police officers, first responders, traffic cones, flares, etc. The detection system may include audio and visual detection system dedicated to the detection of authorities. Additionally, systems that correlate the detected authorities and/or any audible or visual instructions given by the authorities to changes in the behavior of the autonomous vehicle. The autonomous vehicle may react to the detected authorities or emergency vehicles by determining a driving related operation to safely operate around the detected authorities or emergency vehicles.

A method for risk based processing, the method may include detecting, based on first sensed information sensed at a first period, a suspected risk within an environment of a vehicle; selecting, from reference information, a situation related subset of the reference information, wherein the situation related subset of the reference information is related to the situation; selecting, from reference information, a suspected risk related subset reference information, wherein the potential risk related subset of the reference information is related to the potential risk; and determining whether the suspected risk is an actual risk, based at least on part on the suspected risk related subset reference information.

Methods and systems for deploying autonomous vehicles to form a barricade in a coordinated response to an imminent threat are described. In one embodiment, a method for deploying autonomous vehicles to form a barricade is described. The method includes determining at least one location for a barricade and determining a plurality of autonomous vehicles that are available to form the barricade. The method also includes sending instructions to the plurality of autonomous vehicles to form the barricade at the at least one location. In response to the instructions, the plurality of autonomous vehicles are configured to move to the at least one location and form the barricade.

Systems and methods for controlling autonomous vehicle are provided. A method can include obtaining, by a computing system, data indicative of a plurality of objects in a surrounding environment of the autonomous vehicle. The method can further include determining, by the computing system, one or more clusters of the objects based at least in part on the data indicative of the plurality of objects. The method can further include determining, by the computing system, whether to enter an operation mode having one or more limited operational capabilities based at least in part on one or more properties of the one or more clusters. In response to determining that the operation mode is to be entered by the autonomous vehicle, the method can include controlling, by the computing system, the operation of the autonomous vehicle based at least in part on the one or more limited operational capabilities.

A method of operating a vehicle is provided. The method comprises by a control circuitry of the vehicle in combination with one or more sensors of the vehicle, that a vulnerable subject is travelling on a path which intersects with a path on which the vehicle is travelling, determining, by the control circuitry, that a collision between the vehicle and the vulnerable subject is likely, predicting, by the control circuitry, a force of the collision, selecting, by the control circuitry in accordance with the predicted force of the collision, one of a plurality of models each defining an operation of the vehicle in response to determining that the vehicle is unable to avoid the collision, wherein the selected model is selected on the basis that the control circuitry determines it to result in a least amount of injury for the vulnerable subject from among the plurality of models, and controlling, by the control circuitry, one or more functions of the vehicle in accordance with the selected model.

The present disclosure relates to a method of augmenting human perception of the surroundings, comprising: receiving a first set of data from a user device associated with the user, the first set of data comprising at least location information; receiving a second set of data from a local distributed database associated with the location information, the second set of data comprising a position of one or more entities; constructing a digital model representing the user and the one or more entities using the first and second set of data; projecting a respective course for the user and for the or each entity by inputting the digital model into a first machine learning algorithm (MLA) to infer one or more events; identifying at least one inferred event relevant to the user by inputting the one or more inferred events to the first MLA; and communicating the at least one relevant inferred event to the user.

A system comprises an autonomous vehicle and a control device associated with the autonomous vehicle. The autonomous vehicle comprises sensors. The control device accesses sensor data captured by the sensors. The control device identifies a vehicle of interest from the sensor data. The control device communicates information associated with the vehicle of interest to at least one of an oversight server and a third party.