Abstract: 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.

Abstract: A method includes, based on comparing a velocity, lane selection and path selection of a target vehicle with respect to a host vehicle, determining a following risk assessment for the target vehicle relative to the host vehicle, determining the target vehicle is following the host vehicle based on the following risk assessment being above a threshold, and then transmitting data identifying the target vehicle to a remote computer and actuating vehicle components to operate the host vehicle to a location specified by the remote computer.

Abstract: The present disclosure relates to systems and methods for determining traffic information of a region. The method may include determining a first region and a second region. The method may also include obtaining a set of links associated with the first region and the second region. The method may also include obtaining a plurality of driving routes of a plurality of vehicles in the first region and the second region in a predetermined time period. The method may also include selecting one or more driving routes that traverse a first boundary of the first region and a second boundary of the second region based on the set of links associated with the first region and the second region. The method may also include determining traffic information of the first region based on information related to the one or more selected driving routes.

Abstract: A travel control apparatus is configured to control a travel of a vehicle so as to travel along a target path. The travel control apparatus is configured to perform: acquiring a change schedule information of a traffic light installed over each of a plurality of merge lanes that merge with a main line and configured to be able to change an indication form between a first indication form permitting the vehicle to merge with the main line and a second indication form instructing the vehicle to stop before a stop line; determining a merge lane on which the vehicle travels among the plurality of merge lanes, based on the change schedule information of a traffic light located in a travel direction of the vehicle; and generating a target path of the vehicle leading to the stop line of the merge lane.

Abstract: Assisting a driver in a vehicle, wherein the vehicle has an environment sensor system for acquiring environment data of an environment surrounding the vehicle and a controller for determining a current traffic status from the acquired environment data. A current overtaking maneuver status is determined, wherein each overtaking maneuver status includes one of a plurality of successive situations during an overtaking maneuver, making available at least one assistance information item on the basis of the determined current traffic status, wherein the assistance information is made available in accordance with the determined current overtaking maneuver status, and outputting the assistance information which is made available.

Abstract: A method of providing dynamic and variable learning for an ego vehicle by determining and using most-trustworthy inputs includes determining, based on ambient conditions of an environment of the ego vehicle, a level of trustworthiness of sensor values obtained from one or more ego vehicle sensors. The method also includes determining a level of confidence in an accuracy of an output of a subsystem of the ego vehicle. The output of the subsystem is based on the sensor values. The level of confidence is based on the level of trustworthiness of the sensor values.

Abstract: The invention relates to a method for determining a lane change for a driver assistance system (100) of a vehicle (1), which method comprises calculating (220) a probability that other vehicles (2) are driving at a higher speed in a lane (L2) which is adjacent to a current lane (L1) of the vehicle (1), applying (240) a hysteresis to the calculated probability based on a driving parameter dependent on a last lane change, and issuing (250) a command to change lanes depending on the probability. The invention further relates to a driver assistance system and a vehicle which can carry out such a method.

Abstract: A method of surveying roads includes generating a dynamic flight plan for a drone using a vehicle traveling on a road as a target. The dynamic flight plan includes instructions for movement of the drone. The method includes controlling the drone as a function of position of the vehicle based on the dynamic flight plan. The method includes maintaining, based on the controlling, line of sight with the drone while the drone with an onboard camera follows the vehicle and captures images of the road being traveled by the vehicle using the onboard camera.

Abstract: Vehicle controller circuitry for a subject vehicle to determine vehicle constraints including predicted values for vehicle speed of at least one other vehicle and predicted values of for vehicle position of the at least one other vehicle relative to the subject vehicle, wherein the vehicle constraints are determined at predetermined time steps (k) over a time horizon (Th).

Abstract: A vehicle control device includes a merging controller configured to generate a first plan for changing a lane of the self-vehicle to in front of or behind a first vehicle, based on a relative relationship between a position and a speed of the self-vehicle and the first vehicle, and allow the self-vehicle to change lanes to the first main lane based on the first plan when it is estimated that a third vehicle is able to change lanes to a second main lane adjacent to the first main lane without interfering with a fourth vehicle, based on a relative relationship between a position and a speed of the first vehicle or a second vehicle present behind the self-vehicle and traveling and the fourth vehicle in a case where it is assumed that the self-vehicle has changed lanes to the first main lane based on the first plan.

Abstract: Techniques for predicting safety metrics associated with near-miss conditions for a vehicle, such as an autonomous vehicle, are discussed herein. For instance, a training system identifies an object in an environment and determines a trajectory for the object. The training system may receive a trajectory for a vehicle and associate the trajectory for the object and the trajectory for the vehicle with an event involving the object and the vehicle. In examples, the training system determines a parameter associated with motion of the vehicle as indicated by the trajectory of the vehicle relative to the trajectory of the object, and the event. Then, the training system may determine a safety metric associated with the event that indicates whether the vehicle came within a threshold of a collision with the object during a time period associated with the event.

Abstract: System and methods are provided for predicting, reacting to, and avoiding loss of traction events, such as hydroplaning for autonomous vehicles. For example, the method predicts a risk of an area being subject to hydroplaning using real-time data and/or historical data. The method may store possible hydroplaning events in a geographic map along with a risk assessment. The method provides lane level hydroplaning risk predictions and avoidance mechanisms.

Abstract: A method includes receiving sensor data associated with one or more inputs associated with a road portion, determining a level of risk associated with each of the one or more inputs, determining an estimated amount of computing resources that each of a plurality of candidate computing methods will consume, and selecting one or more computing methods from the plurality of candidate computing methods to associate with the one or more inputs based on the levels of risk associated with the one or more inputs and the estimated amount of computing resources that the candidate computing methods will consume.

Abstract: The invention relates to a method for a control unit (10) for stopping a vehicle (1) when stopping at different types of stop positions (2, 3, 4), the method comprising the following steps: —(S1) the control unit receiving input about a specific stop position for the vehicle, associated with when the vehicle is about to stop during a stopping sequence, the input being indicative of whether the specific stop position requires a high-precision stop or if a stop with a lower precision can be used; and —(S2) when the stop position requires a high-precision stop, the control unit controlling the stopping sequence such that the vehicle stops with a first stopping precision level with respect to the specific stop position, and when a lower precision can be used for the stop, the control unit controlling the stopping sequence such that the vehicle stops with a second stopping precision level which is lower than the first precision level.

Abstract: Methods and systems for tracking driver behavior across a variety of vehicles are described herein. One or more first performance metrics which indicate performance of a first vehicle when driven by a user may be determined. One or more second performance metrics indicating performance of a second vehicle when driven by the user may be determined. The first vehicle and the second vehicle may be compared to determine a vehicle difference. The performance metrics may be compared. One or more third performance metrics that predict performance of a third vehicle, different from the first vehicle and the second vehicle, when driven by the user may be determined based on the vehicle difference and the comparison. Whether to provide the user access to the third vehicle may be determined based on the one or more third performance metrics.

Abstract: The present disclosure relates to a host vehicle and control method thereof, according to one aspect of the present disclosure, the purpose is to enable quick and accurate recognition of cut-in vehicles that change lanes from adjacent lanes to travel lanes. Disclosed are a control method of a host vehicle, the method including: obtaining a front image by photographing the front of the host vehicle; identifying left and right lines of a travel lane in which the host vehicle is traveling from the front image; determining that a vehicle cutting-in from an adjacent lane to the travel lane exists when the lengths of the left line and the right line of the travel lane are different from each other in the front image.

Abstract: A smart license plate vault securely holds small objects, such as a vehicle key, while resembling a license plate mounting platform. The smart license plate vault may be operated by authorized third parties via a keypad located on the smart license plate vault to accept access codes and other information. The smart license plate vault may include a storage compartment with a removable cover for providing access to the storage compartment and an electronic locking mechanism for locking and unlocking the vault, a vault cover plate sensor for detecting when the vault cover plate has been placed into a closed position, a storage compartment sensor for recording information regarding an interior of the storage compartment when the vault cover plate sensor detects that the vault cover plate has been placed into the closed position, and a transmitter for wirelessly transmitting the recorded information to a remote location.

Abstract: An apparatus and method for controlling a vehicle speed based on information about forward vehicles that travel in the same lane may be acquired using Vehicle to Everything (V2X) communications in a cooperative adaptive cruise control (CACC) system. The CACC system includes a communication unit receiving vehicle information from neighboring vehicles using V2V communications; an information collection unit collecting vehicle information of the neighboring vehicles and the subject vehicle using sensors; and a control unit determining a forward vehicle and a far-forward vehicle using the sensors, selecting first and second target vehicles for being followed by the subject vehicle based on the vehicle information of the forward vehicle and the far-forward vehicle and the vehicle information of the neighboring vehicles, and controlling the driving speed of the subject vehicle based on speed information of the first and second target vehicles.

Abstract: A mobile robot, capable of communicating with neighboring devices in a 5G communication environment and capable of efficient cleaning via machine learning based on such communication, comprises a main body configured to move in the movement space, a driving unit mounted on the main body to move the main body, a receiving unit configured to receive moving history information of a user robot that has been moved by a user in the movement space, a memory in which a computer-readable program and map information of the movement space are stored, and a control unit configured to communicate with the receiving unit, the memory, and the driving unit to control the main body, wherein the control unit establishes a moving area of the mobile robot based on the moving history information of the user robot received by the receiving unit.

Abstract: Systems and methods for basis path generation are provided. In particular, a computing system can obtain a target nominal path. The computing system can determine a current pose for an autonomous vehicle. The computing system can determine, based at least in part on the current pose of the autonomous vehicle and the target nominal path, a lane change region. The computing system can determine one or more merge points on the target nominal path. The computing system can, for each respective merge point in the one or more merge points, generate a candidate basis path from the current pose of the autonomous vehicle to the respective merge point. The computing system can generate a suitability classification for each candidate basis path. The computing system can select one or more candidate basis paths based on the suitability classification for each respective candidate basis path in the plurality of candidate basis paths.