Abstract: A control method of automatic driving imported “Smart Gains” model can bypass the problem caused by high levels of complexity that currently trouble automatic driving control systems. The knowledge generated by a Gaussian process machine learning model with the maximum probability can carry out the closed-loop control of automatic driving with a given trajectory, can solve the nonlinear adjustment problem of the actuators of the automatic driving vehicle, as well as the optimization control problem of the randomness of the control object. This feature can also make the automatic driving vehicle run smoothly, save energy, be comfortable and fast, and achieve automatic driving above Level 4.

Abstract: A vehicle control device recognizes a second self-position, which is obtained by correcting a first self-position, and an orientation of a vehicle on a road, on which the vehicle is traveling, based on a situation around the vehicle, the first self-position, and map information, determines a steering control mode and a speed control mode of the vehicle based on the second self-position and the orientation of the vehicle, and performs automated driving by controlling the vehicle based on the determined control modes. When the vehicle is scheduled to advance from a first lane to a second lane and it is not possible to recognize a target associated with a road indicating an end point of a merging section, a determiner determines a steering angle control mode for searching for the target based on the second self-position and the orientation of the vehicle.

Abstract: A vehicle control method includes recognizing a periphery of a vehicle, a first vehicle and a second vehicle present in the periphery, setting a target area between the first vehicle and the second vehicle based on a reference position of a first type of the first vehicle present in a lane of a lane change destination and a reference position of the first type of the second vehicle behind the first vehicle and present in the lane, and controlling the vehicle to enter the target area set based on a reference position of a second type of the first vehicle and a reference position of the second type of the second vehicle.

Abstract: The present disclosure relates to an overtaking estimating system for estimation of a minimum overtaking speed of a vehicle. The overtaking estimating system determines in view of a host vehicle a remaining distance of an overtaking lane contiguous to a driving lane of the host vehicle. The system further determines a delta distance between the host vehicle and a preceding vehicle positioned in the host vehicle driving lane. Moreover, the system determines a delta time for the host vehicle to reach the preceding vehicle. The system further determines, based on the delta distance, the delta time and a determined host vehicle speed, a speed of the preceding vehicle. Furthermore, the system determines, based on the remaining distance, the delta distance, the preceding vehicle speed, and an overtaking-affecting parameter, a minimum overtaking speed of the host vehicle for overtaking the preceding vehicle in the remaining distance of the overtaking lane.

Abstract: In one embodiment, a computer-implemented method for optimizing a controller of an autonomous driving vehicle (ADV) includes obtaining several samples, each sample having a set of parameters, iteratively performing, until a predetermined condition is satisfied: determining, for each sample, a score according to a configuration of the controller based on the set of parameters of the sample, applying a machine learning model to the samples and corresponding scores to determine a mean function and a variance function, producing a new sample as a minimum of a function of the mean function and the variance function with respect to an input space of the set of parameters, adding the new sample to the several samples, and outputting the new sample as an optimal sample, where parameters of the optimal sample are utilized to configure the controller to autonomously drive the ADV.

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.