A processor unit (3) is configured to execute an autonomous driving function of the motor vehicle (1) during a first instance such that the motor vehicle (1) travels autonomously based at least in part on the execution of the autonomous driving function. The processor unit (3) is further configured to store a driver intervention, the driver intervention being performed by a driver of the motor vehicle (1) during the first instance while the motor vehicle (1) travels autonomously based on the execution of the autonomous driving function. Additionally, the processor unit (3) is configured to execute the autonomous driving function during a second instance, subsequent to the first instance, based at least in part on the stored driver intervention such that the motor vehicle (1) travels autonomously based at least in part on the execution of the autonomous driving function according to the stored driver intervention.

A system and method operate an autonomous vehicle. A sensor senses a road and an object. A processor determines, in a Cartesian reference frame, a representation of the road and a source point representative of the object, samples a first waypoint and a second waypoint from the representation of the road, determines a linear projection of the source point to a line connecting the first waypoint and the second waypoint, determines a first estimate of a longitudinal component of the source point in a road-based reference frame based on the linear projection, the first estimate being on a curve representing the road between the first waypoint and the second waypoint, determines a second estimate of the longitudinal component from the first estimate, determines a coordinate of the source point in the road-based reference frame from the second estimate and operates the vehicle with respect to the object using the coordinate.

A method detects a risk of collision between a motor vehicle and a secondary object located in traffic lanes adjacent to the main traffic lane of the vehicle, in the event of a lane change by the vehicle, which involves detecting objects in a predetermined danger zone, and estimating a time-to-collision between the vehicle and a detected object. Detecting objects in a danger zone involves: calculating the actual distance between the vehicle and each object detected by the radar, the actual distance corresponding to the length of an arc between two points; determining a danger zone as a function of lines of the main traffic lane and a width of the main traffic line; and checking, for each object detected by the radar, whether its coordinates are inside the predetermined danger zone.

In one aspect, an operating method of an intelligence vehicle driving control system is provided that comprises: a collecting step of collecting big data including a wheel torque and a speed for every vehicle type and traffic information; a torque calculating step of learning the big data using a predetermined machine learning model and inputs a specific desired speed profile to the machine learning model to calculate a motor torque of a driving vehicle; and an optimal speed profile deriving step of calculating an energy consumption required to generate the calculated motor torque using a predetermined dynamic programming method and a reverse vehicle dynamic model and deriving an optimal speed profile in which the energy consumption is minimized.

Methods and systems are provided for controlling an autonomous vehicle. In one embodiment, a method includes: identifying, by a processor, at least one constraint on a longitudinal dimension of an upcoming road; defining, by the processor, constraint activation logic based on a type of the at least one constraint; performing, by the processor, the constraint activation logic to determine a state of the constraint to be at least one of active and inactive; when the state of the constraint is active, validating, by the processor, a motion plan of the autonomous vehicle based on the constraint; and selectively controlling the autonomous vehicle based on the validating of the motion plan.

To obtain a rider-assistance system capable of providing a rider of a straddle-type vehicle with a sense of comfort and safety during a turn, and a control method for such a rider-assistance system.

The present invention provides the rider-assistance system that assists with driving by the rider of the straddle-type vehicle and includes a controller. The controller includes: an object identification section that identifies an object approaching a side of the straddle-type vehicle on the basis of output of a communication device that wirelessly receives information output from infrastructure equipment or another vehicle; a body position information acquisition section that acquires position information of at least a part of a body of the rider on the turning straddle-type vehicle; a collision possibility determination section that determines a collision possibility of the rider with the object identified by the object identification section on the basis of the position information acquired by the body position information acquisition section; and a safety operation performing section that causes the rider-assistance system to perform safety operation in the case where the collision possibility determination section determines that the collision possibility is high.

Provided is a vehicle control device capable of preventing a delay in driver's bank angle operation during traveling of a straddle type vehicle on a curve and enhancing safety of the vehicle. The vehicle control device 100 is a device that is mounted on a two-wheeled motor vehicle and controls the vehicle to travel while following a preceding vehicle. The vehicle control device 100 includes a curvature acquisition unit 110 that acquires a curvature of a road in front of the vehicle and a driving force control unit 120 that limits a change amount of driving force of the vehicle per unit time based on the curvature acquired by the curvature acquisition unit 110.

Methods and systems for distributed detection of road conditions and damage are described. In one embodiment, a method for distributed detection of road conditions and damage is provided. The method includes receiving, from one or more mobile devices, a plurality of reports of anomalies associated with roads in a geographic area. The method also includes storing the received reports of anomalies in a database and comparing each report of an anomaly to stored reports of previous anomalies in the database. The method further includes determining whether each report of an anomaly indicates road damage or a temporary problem. The method includes generating a prioritized list of locations of anomalies associated with one or more roads that have been determined to have road damage that needs maintenance and/or repair.

A trajectory for a vehicle can be generated using a lateral offset bias. The vehicle, such as an autonomous vehicle (AV), may be directed to follow reference trajectory for through an environment. The AV may determine a segment associated with the reference trajectory based on curvatures of the reference trajectory, determine a lateral offset bias associated with the segment based at least in part on, for example, one or more of a speed or acceleration of the vehicle, and determine a candidate trajectory for the autonomous vehicle based at least in part on the lateral offset bias. The candidate trajectory may then be used to control the autonomous vehicle.

A device for stabilizing a vehicle after a collision against a lateral carriageway boundary, includes a lane recognition system, with which information relating to the course of the lane is determined or detected. A collision detection unit identifies a collision of the vehicle against the lateral lane carriageway boundary on the basis of signals from at least one sensor or on the basis of a driving state variable. The device also includes a steering actuator for steering a steering system and a brake actuator for controlling one or more wheel brakes. A target path determination unit determines a target path for the vehicle on the basis of the course of the lane determined or detected before or at the time of the collision. A controller guides the vehicle onto the target path and/or stabilizes the vehicle via a steering intervention and/or individual wheel brake interventions.