Disclosed is a solution to the use of a speed limiter in a motorcycle that has a high capacity for acceleration, including a method for limiting the speed of a motorcycle using precise parameters of speed, acceleration and required engine torque and a single control button to manage the smart activation and deactivation of the limiter and to input new speed limits V.sub.lim in a simple way. It thus allows the use of a speed-limiting system in a motorcycle that has a high power/weight ratio by avoiding the sources of interference with the riding and the effects liable to jeopardize its stability.

A method for controlling autonomous driving in an autonomous vehicle includes detecting a situation in which autonomous driving is impossible while the vehicle operates in an autonomous driving mode, outputting a control-right handover request warning alarm and then activating a minimal risk maneuver driving mode, determining a human driver gaze validity based on the detected situation, determining a human driver intervention validity upon determination that the human driver gaze is valid, and determining control-right handover of the autonomous vehicle based on the human driver intervention validity. Thus, the control-right may be reliably transferred from a system to a human driver.

Systems and methods for virtual vehicle parking assistance are disclosed herein. An example method includes determining a current vehicle position and vehicle dimensions of a vehicle, determining parking space dimensions of a parking space, receiving a desired parking position for the vehicle through an augmented reality interface, the augmented reality interface including a three-dimensional vehicle model based on the vehicle dimensions, the augmented reality interface being configured to allow a user to virtually place the three-dimensional vehicle model in the parking space to determine the desired parking position, determining a virtual parking procedure for the vehicle based on the desired parking position selected by the user and the parking space dimensions of a parking space and causing the vehicle to autonomously park based on the virtual parking procedure.

A mobile ecosystem includes an autonomous control system in a vehicle. The autonomous control system receives user information from at least one of a user device or the vehicle and identifies a trajectory plan and a vehicle configuration based on the user information. The trajectory plan identifies a departure time for the vehicle to depart a starting location, an arrival time for the vehicle to arrive at a destination location, and a travel route for the vehicle to traverse at least partially between the starting location and the destination location. The trajectory plan may identify an entry time for the user to enter the vehicle. The vehicle configuration identifies travels settings for components in at least one of dynamic systems or interior systems of the vehicle. The autonomous control system causes the components to be configured according to the travel settings and causes the vehicle to traverse the travel route.

A mirror position registration control apparatus includes a shift position detector that detects a shift position of a vehicle, a mirror surface position detector that detects a change in a mirror surface position in the vehicle, a mirror surface position storage that stores a set mirror surface position; an information display that displays information for a driver who drives the vehicle, a registration operation receiver that receives a registration operation for the mirror surface position inputted by the driver, and a processor that, in a case where the mirror surface position is changed with the shift position set at a reverse position, displays a registration screen for the changed mirror surface position on the information display and stores the changed mirror surface position in the mirror surface position storage on the basis of the registration operation when the shift position is changed from the reverse position to a parking position.

An object of the present invention is to realize lane deviation warning/control at an intersection, on a multilane road and in an environment where no lane dividing lines are present and GNSS accuracy is low. A driving assistance device includes a recording unit that records a plurality of past traveling positions of a vehicle and position estimation information relating to the past traveling positions, a relative position estimation unit that estimates a relative position of the vehicle from an output of a sensor that detects a surrounding of the vehicle, adds a history of the relative position to the past traveling position, and adds the output of the sensor, which is used to estimate the relative position, to the position estimation information, a position estimation unit that estimates a position of the vehicle with respect to the past traveling positions from the output of the sensor and the position estimation information of the recording unit, and adds a history of the estimated position to the past traveling position, a deviation determination unit that determines deviation from the plurality of past traveling positions by using a distribution of the plurality of past traveling positions, the position of the vehicle with respect to the plurality of past traveling positions, and a predetermined reference value, and a control unit that controls the vehicle to cancel a warning or deviation, when the deviation is determined.

A data acquisition unit to acquire data; an inference unit to infer a first inference result by inputting the data acquired to a first machine learning model that outputs the first inference result by using the data as an input; a similarity calculation unit to calculate a similarity between the data acquired and a second inference result on the basis of the second inference result and the data acquired, the second inference result being inferred by inputting the data acquired to a second machine learning model that outputs the second inference result by using the data as an input; a determination unit to determine whether or not to output the first inference result by comparing the similarity calculated with a threshold for inference result determination; and an output unit to output the first inference result when the determination unit determines to output the first inference result are included.

A parking assistance system for assisting parking of a vehicle includes: an autonomous driving vehicle configured to perform automatic driving from a drop off area in a parking lot to a parking position upon receiving a parking start instruction; a management device configured to manage entering and exiting of the autonomous driving vehicle; and a fixed terminal device installed in the drop off area, the fixed terminal device being operable by a user of the autonomous driving vehicle. The management device includes an intention acquiring unit configured to acquire a parking intention input by the user; an inspection executing unit configured to execute an inspection on the autonomous driving vehicle; an operation requesting unit configured to request the user to perform a predetermined operation through the fixed terminal device; and an instruction transmitting unit configured to transmit the parking start instruction to the autonomous driving vehicle.

An electronic control device provided in a vehicle that mounts an in-vehicle system is provided. The electronic control device may detect an abnormality that occurs in the in-vehicle system. The electronic control device may detect an operation related to a lane change. The electronic control device may store a travel plan for performing an autonomous driving of the vehicle. The electronic control device may acquire at least one of a subject vehicle information item on the vehicle, a surrounding information item on a surrounding environment of the vehicle, and a driver information item on a driver.

Methods and systems for training an autonomous vehicle (AV) motion planning model are disclosed. The system receives a log of data representing objects detected by an AV over time. The system identifies a group of sample times in the log. Each sample time represents a time at which the AV made a choice in response to a state of an object. For each of the sample times, the system will generate candidate trajectories for the AV, and it will output the candidate trajectories on a display. The system will receive a label with a rating for each candidate trajectory. The system will then save, to a data set, each of the candidate trajectories in association with its label and the data from the log for its corresponding sample time. The system may then apply the data set to an AV motion planning model to train the model.