Provided is a method for evaluating a performance of an autonomous driving algorithm performed by one or more processors, including determining a first parameter set and a second parameter set which are associated with a driving of an ego vehicle in which the autonomous driving algorithm is applied and a driving of a surrounding vehicle, based on a collision scenario between the ego vehicle and the surrounding vehicle, generating a plurality of cases associated with the collision scenario based on the first parameter set and the second parameter set, and performing a simulation for each of the plurality of cases using the autonomous driving algorithm, in which each parameter of the first parameter set has a fixed value, and each parameter of the second parameter set has a predetermined sweeping range.

A driving assistance apparatus of a vehicle includes a processor configured to calculate a risk sensitivity index on risk avoidance of a driver of the vehicle with respect to a target existing around the vehicle, diagnose a change in the calculated risk sensitivity index, and notify the driver of information on the diagnosed change in the risk sensitivity index.

A mobile object control device according to an embodiment includes a recognizer that recognizes a surroundings situation of a mobile object, and an unavoidable contact determiner that determines whether or not contact between the mobile object and an object likely to come into contact with the mobile object is unavoidable on the basis of a recognition result of the recognizer when there is the object around the mobile object, and the unavoidable contact determiner limits a recognition range of the recognizer to a predetermined range including the object when the recognition result of the recognizer satisfies a predetermined condition, and determines whether or not the contact between the mobile object and the object is unavoidable.

Systems and methods use cameras to provide autonomous navigation features. In one implementation, a method for navigating a user vehicle may include acquiring, using at least one image capture device, a plurality of images of an area in a vicinity of the user vehicle; determining from the plurality of images a first lane constraint on a first side of the user vehicle and a second lane constraint on a second side of the user vehicle opposite to the first side of the user vehicle; enabling the user vehicle to pass a target vehicle if the target vehicle is determined to be in a lane different from the lane in which the user vehicle is traveling; and causing the user vehicle to abort the pass before completion of the pass, if the target vehicle is determined to be entering the lane in which the user vehicle is traveling.

A vehicular control system includes a plurality of sensors disposed at a vehicle and sensing exterior of the vehicle. An electronic control unit (ECU) includes a processor that processes sensor data captured by the sensors. The vehicular control system, responsive at least in part to processing at the ECU of captured sensor data as the vehicle travels in a traffic lane of a road, detects another vehicle that is rearward of the equipped vehicle and traveling along an adjacent traffic lane. The vehicular control system detects a leading vehicle ahead of the equipped vehicle and traveling in the same traffic lane as the equipped vehicle. The vehicular control system, responsive to determination of a space along the other traffic lane ahead of the detected other vehicle, controls the equipped vehicle to maneuver into the adjacent traffic lane to pass the detected leading vehicle ahead of the detected other vehicle.

A system and method for determining a predicted trajectory of a human-driven host vehicle as the human-driven host vehicle approaches a traffic signal. The method includes: obtaining a host vehicle-traffic light distance d.sub.x and a longitudinal host vehicle speed v.sub.x that are each taken when the human-driven host vehicle approaches the traffic signal; obtaining a traffic light signal phase P.sub.t and an traffic light signal timing T.sub.t; obtaining a time of day TOD; providing the host vehicle-traffic light distance d.sub.x, the longitudinal host vehicle speed v.sub.x, the traffic light signal phase P.sub.t, the traffic light signal timing T.sub.t, and the time of day TOD as input into an artificial intelligence (AI) vehicle trajectory prediction application, wherein the AI vehicle trajectory prediction application implements an AI vehicle trajectory prediction model; and determining the predicted trajectory of the human-driven host vehicle using the AI vehicle trajectory prediction application.

A mobile object control method including: recognizing physical objects near a mobile object and a route shape; generating a target trajectory based on a result of the recognition and cause the mobile object to travel autonomously along the target trajectory; and determining that an abnormality has occurred in a control system for causing the mobile object to travel autonomously by performing the recognition when a time period from a timing when a degree of deviation between a reference target trajectory determined by the route shape and serving as a reference for generating the target trajectory and a position of the mobile object is greater than or equal to a predetermined degree to a timing when the degree of deviation is less than the predetermined degree is greater than or equal to a first predetermined time period and output a determination result.

An embodiment method of controlling a moving object includes checking profile information of a user who rides in the moving object or status information of the user, checking a degree of risk based on the profile information or the status information of the user, setting an operation mode of the moving object based on the degree of risk, and controlling movement of the moving object based on the operation mode.

A fault diagnosis method and a fault diagnosis apparatus for a vehicle speed measurement apparatus. The method includes: obtaining a first vehicle speed measured by the speed measurement apparatus; obtaining reference information of a static reference object at N moments, where N is an integer greater than 1, and the reference information includes information about a location relationship of the static reference object relative to a vehicle in which the speed measurement apparatus is located at each of the N moments; calculating a second vehicle speed based on the reference information; and determining, based on the first vehicle speed and the second vehicle speed, whether the speed measurement apparatus is faulty.

An unmanned vehicle 20 is a vehicle that drives an electric motor by electric power generated in a power generator to travel by driving of the electric motor and includes a position sensor 240 that detects a position of the unmanned vehicle 20, a speed sensor 250 that detects a speed of the unmanned vehicle 20, and a vehicle control device 220 that controls the unmanned vehicle 20. The vehicle control device 220 calculates a work progression of a loading operation to the unmanned vehicle 20 by a loading machine 30 or a work progression of a preceding vehicle based on the position of the unmanned vehicle 20 detected by the position sensor 240 and the speed of the unmanned vehicle 20 detected by the speed sensor 250, calculates a period from a predicted time at which the calculated work progression exceeds a predetermined proportion until a predicted time at which the unmanned vehicle 20 starts acceleration as an acceleration preparation time, and drives the power generator to generate electricity during the calculated acceleration preparation time.