A method for controlling vehicle system of a vehicle is disclosed. The method comprises determining an expected path of the vehicle, determining a vehicle trajectory for the determined expected path, and determining at least one required control parameter value of a driver assistance system based on the determined vehicle trajectory. Further, the method comprises comparing the at least one required control parameter value to a predefined threshold scheme associated with the driver assistance system, and sending a signal to a Human Machine Interface, HMI, of the vehicle based on the comparison. Then, the method comprises receiving a feedback signal originating from a user of the vehicle, and controlling the driver assistance system based on the comparison and the received feedback signal.

Systems and methods are provided for vehicle navigation. In one implementation, a system may comprise an interface to obtain sensing data of an environment of the host vehicle. The processing device may be configured to determine a planned navigational action; identify, a target vehicle in the environment of the host vehicle; predict a distance between the host vehicle and the target vehicle if the planned navigational action was taken; determine a current host vehicle stopping distance based on a braking capability, acceleration capability, and speed of the host vehicle; determine a current target vehicle braking distance based on a speed and braking capability of the target vehicle; and implement the planned navigational action when the predicted distance of the planned navigational action is greater than a minimum safe longitudinal distance calculated based on the current host vehicle stopping distance and the current target vehicle braking distance.

Among other things, techniques are described for screening and monitoring the health of a vehicle user including receiving sensor data produced by a sensor at the vehicle, processing the sensor data to determine at least one health condition of the user of the vehicle, and in response to determining the at least one health condition, executing a vehicle function selected from a plurality of vehicle functions based on the at least one health condition.

A roadmanship system comprises a computational device and a vehicle comprising a plurality of sensors and a vehicle control system in communication with the computational device and the plurality of sensors. The computational device can be configured to: (i) receive driving data from a group of vehicles; (ii) calculate a regression curve based on the driving data; (iii) calculate a threshold value of an engineering parameter based on the regression curve and a predetermined roadmanship level; and (iv) output the threshold value to the vehicle control system. The vehicle control system can be configured to: (a) receive the threshold value from the computational device; (b) receive operational information associated with at least one of the vehicle and a driving environment surrounding the vehicle from the plurality of sensors; and (c) cause the vehicle to perform a vehicle maneuver based on the threshold value and the operational information.

A method for receiving autonomous vehicle (AV) driving path data associated with a driving path in a roadway of a geographic location. The driving path data associated with a trajectory for an AV in a roadway and trajectory points in a trajectory of the driving path in the roadway for determining at least one feature of the roadway positioned a lateral distance from a first trajectory of the one or more trajectories of the driving path of an AV based on the map data. The method includes receiving map data associated with a map of a geographic location, determining a driving path for an AV in a roadway, generating driving path information based on a trajectory point in a trajectory of the driving path, and providing driving path data associated with the driving path to an AV for controlling the AV on the roadway.

A navigation system for a host vehicle may include at least one processor programmed to receive, from a camera, a plurality of images representative of an environment of the host vehicle. The processor may also be programmed to analyze at least one of the plurality of images to identify navigational state information associated with the host vehicle; determine a plurality of potential trajectories for the host vehicle based on the navigational state information; perform a preliminary analysis relative to each of the plurality of potential trajectories and assign to each of the plurality of potential trajectories, based on the preliminary analysis, at least one indicator of relative ranking; select, based on the at least one indicator of relative ranking assigned to each of the plurality of potential trajectories, a subset of the plurality of potential trajectories, wherein the subset of the plurality of potential trajectories includes fewer potential trajectories than the plurality of potential trajectories; perform a secondary analysis relative to the subset of the plurality of potential trajectories, and based on the secondary analysis, select one of the subset of the plurality of potential trajectories as a planned trajectory for the host vehicle; determine one or more navigational actions for the host vehicle based on the planned trajectory selected from among the subset of the plurality of potential trajectories; and cause at least one adjustment of a navigational actuator of the host vehicle to implement the one or more navigational actions for the host vehicle.

A vehicle control apparatus, which controls an execution of an autonomous driving of a vehicle, is configured to: acquire a current area of the vehicle; acquire a driving characteristic information item indicating a numerical value or a degree of a driving characteristic to be used in an autonomous driving of the vehicle; and set the driving characteristic to be used in the autonomous driving of the vehicle with the acquired driving characteristic information item. The driving characteristic to be used in the autonomous driving is set with the area characteristic information item, which is the driving characteristic information item corresponding to the acquired current area.

An apparatus for setting a planned trajectory includes a processor configured to identify a travel lane on which a vehicle is traveling, detect, as a specific section, a section lacking at least one of left and right lane lines of the travel lane of the vehicle in a planned travel section, set at least one candidate of a planned trajectory to be traveled by the vehicle in the specific section, the at least one candidate including a candidate based on an existing one of the lane lines or a road edge, and set, of the set candidate, a candidate having a minimum variation in curvature or a minimum offset distance in a direction perpendicular to a travel direction of the vehicle at parts connected to the planned trajectory in sections ahead of and behind the specific section as the planned trajectory of the specific section.

In one embodiment, a computing system of a vehicle may receive vehicle driving data associated with a vehicle driving in an environment and detected environment data associated with the environment. The system may generate a reference trajectory of the vehicle driving in the environment based on the vehicle driving data. The system may determine driving constraints associated with the environment based on the detected environmental data. The system may generate a trajectory of the vehicle based on the driving constraints. The system may determine a difference in at least one parameter associated with the trajectory relative to at least one corresponding parameter associated with the reference trajectory. The system may adjust weight values associated with cost functions of the trajectory based on the difference between the at least one parameter associated with the trajectory and the corresponding parameter associated with the reference trajectory.

A reconstruction method for a secure environment envelope of a smart vehicle based on the driving behavior of a vehicle in front, starting from the simulation of the behavior of a real driver pre-estimating the potential collision risk of the drive area in front, introducing a prediction regarding the driving behavior of the vehicle in front to the environment sensing link of the smart vehicle, reconstructing, on the basis of the prediction result regarding the driving behavior of the vehicle in front, a secure environment envelope of the smart vehicle. The method uses a signal as an observed value, such as the trajectory point sequence of the vehicle in front, the indicators of the vehicle in front, the smart vehicle speed, the relative longitudinal speed of the smart vehicle and the vehicle in front, etc., and predicts the driving behavior of the vehicle in front by means of a hidden markov model (HMM); the method corrects, on the basis of the prediction result about the driving behavior of the vehicle in front, the transverse spacing and the longitudinal spacing between the smart vehicle and the vehicle in front, realizes the reconstruction of a secure environment envelope of a smart vehicle, and further realizes the pre-estimation regarding the potential collision risk of the smart vehicle in the safe drive area, and improves the security of the smart vehicle.