A system for navigating a vehicle may include a processor programmed to receive an output provided by a vehicle sensor, and determine a navigational maneuver for the vehicle along a road segment based on the output provided by the vehicle sensor. The processor may also be programmed to determine a yaw rate command and a speed command for implementing the navigational maneuver. The processor may also be programmed to determine a first vehicle steering angle based on the yaw rate and speed commands using a first control subsystem, and determine a second vehicle steering angle based on the yaw rate and speed commands using a second control subsystem. The processor may further be programmed to determine an overall steering command for the vehicle based on a combination of the first and second steering angles, and cause an actuator associated with the vehicle to implement the overall steering command.

A vehicle control device includes: a first detection unit that detects a traveling state of a host vehicle; a merging detection unit that detects that the host vehicle approaches within a predetermined area of a merging point when the host vehicle travels on the merging road toward the merging point at which a main road joins with the merging road; a second detection unit that detects a speed of a lane flow by another vehicle that travels on the main road toward the merging point; a position detection unit that obtains a position of a pre-merging point as a virtual point on the main road reaching the merging point when the host vehicle reaches the merging point; and a display control unit that controls a display device to display the position of the host vehicle and the pre-merging point.

In a driving assistance apparatus, an object detecting unit detects an object that is present in a periphery of an own vehicle based on an image captured by an imaging apparatus provided in the own vehicle. An avoidance control unit performs collision avoidance control for avoiding a collision between the detected object and the own vehicle when a collision between the object and the own vehicle is likely. A light distribution control unit switches irradiated light of an irradiation apparatus provided in the own vehicle between high beam and low beam based on a predetermined switching condition. The light distribution control unit performs switching suppression control to suppress switching of the irradiated light from high beam to low beam while the avoidance control unit is performing collision avoidance control in a case where the irradiated light is set to high beam.

A method of processing data for an autonomous vehicle, an electronic device, a storage medium, and an autonomous vehicle are provided. The method includes: acquiring sensor data for the autonomous vehicle, wherein the sensor data includes inertial measurement data, LiDAR data, and visual image data; determining a first constraint factor for the inertial measurement data according to the inertial measurement data and the visual image data; determining a second constraint factor for the LiDAR data according to the inertial measurement data and the LiDAR data; determining a third constraint factor for the visual image data according to the inertial measurement data, the visual image data and the LiDAR data; and processing the sensor data based on the first constraint factor, the second constraint factor and the third constraint factor, so as to obtain positioning data for positioning the autonomous vehicle.

Techniques associated with generating simulation scenarios for simulating a vehicle controller are discussed herein. Log data may include sensor data captured by sensors of a vehicle. The log data may represent objects in an environment. Objects may be associated with a region of a discretized representation of the environment relative to the vehicle. Specific states of objects (relative position in a region type, velocity, classification, size, etc.) may represent an instance of an occupation. Log data can be aggregated based on similar region type and/or object state. A statistical model over object states can be determined for each region type and can later be sampled to determine simulation parameters. A simulation scenario can be generated based on the simulation parameters, and a vehicle controller can be evaluated based on the simulation scenario.

A vehicle system includes an electronic control unit. The electronic control unit is configured to execute a first program, a second program, and a third program. The first program is configured to recognize an object present around a vehicle, the second program is configured to store information related to the recognized object as time-series map data, and the third program is configured to predict a future position of the object based on the stored time-series map data. The first program and the third program are configured to be (i) first, individually optimized based on first training data corresponding to output of the first program and second training data corresponding to output of the third program, and (ii) then, collectively optimized based on the second training data corresponding to the output of the third program.

A vehicle control device of the embodiments includes a recognizer, and a driving controller, in which the driving controller includes a lane change controller that changes lanes from a host vehicle traveling lane in which a host vehicle travels to an adjacent lane adjacent to the host vehicle traveling lane, the lane change controller includes a first operation of changing lanes according to a request from an occupant of the host vehicle and a second operation of changing lanes on the basis of a result of recognition, and, when changing lanes is stopped at the time of execution of the first operation, causes the host vehicle to continue traveling in the host vehicle traveling lane and prohibits a lane change by the second operation within a first period including a distance or time from a first time at which changing lanes is stopped.

A vehicular control system includes a plurality of electronic control units (ECUs), each providing a respective quantity of computational units representative of an amount of processing power of the respective ECU. The ECUs operate a vehicle in a nominal autonomous operational mode when a sum of the quantity of computational units exceeds a threshold. The system, while the ECUs operate the vehicle in the nominal autonomous operational mode, and responsive to detecting a failure of one of the ECUs, determines whether a sum of the quantity of computational units of the remaining ECUs that do not have a failure exceeds the threshold. The ECUs, responsive to the system determining that the sum of the quantity of computational units of the remaining ECUs fails to exceed the threshold, switches from operating the vehicle in the nominal autonomous operational mode to operating the vehicle in a degraded autonomous operational mode.

Systems, methods, and devices are disclosed for predicting behaviors of objects (vehicles, bicycles, pedestrians, etc.) at a location. A model descriptive of a possible object behavior can be received by an autonomous vehicle, where the model provides conditional predictions about a future behavior of an object based on a position of the object in a lane. The autonomous vehicle can detect the position of a specific object in the lane, and the model can then be applied to determine probabilities of a future behavior of the specific object.

This route prediction device includes: an information acquisition circuitry to acquire a position and a speed of an own vehicle, positions and speeds of surrounding vehicles traveling around the own vehicle, and map information around the own vehicle; a cut-in determinator to determine whether or not the surrounding vehicle will cut in onto a traveling lane of the own vehicle, on the basis of an inducing factor of inducing cut-in of another vehicle; an assumptive vehicle setting circuitry to determine a traveling position, on a road, of an assumptive vehicle assumed to influence traveling of a cut-in vehicle determined to cut in, among the surrounding vehicles, using road information obtained from the map information; and a route prediction circuitry to predict a traveling route of one of the surrounding vehicles, on the basis of the traveling position of the assumptive vehicle, and so forth.