Methods and systems for autonomous and semi-autonomous vehicle control, routing, and automatic feature adjustment are disclosed. Sensors associated with autonomous operation features may be utilized to search an area for missing persons, stolen vehicles, or similar persons or items of interest. Sensor data associated with the features may be automatically collected and analyzed to passively search for missing persons or vehicles without vehicle operator involvement. Search criteria may be determined by a remote server and communicated to a plurality of vehicles within a search area. In response to which, sensor data may be collected and analyzed by the vehicles. When sensor data generated by a vehicle matches the search criteria, the vehicle may communicate the information to the remote server.

An impairment analysis (“IA”) computer system for alerting a first driver of a first vehicle to a driving hazard posed by a second vehicle operated by a second driver is provided. The IA computer system is associated with the first vehicle, and includes at least one processor in communication with at least one memory device. The at least one processor is programmed to: (i) receive second vehicle data including second driver data and second vehicle condition data, where the second vehicle data is collected by a plurality of sensors included on the first vehicle; (ii) analyze the second vehicle data by applying a baseline model to the second vehicle data; (iii) determine that the second vehicle poses a driving hazard to the first vehicle based upon the analysis; and/or (iv) generate an alert signal based upon the determination that the second vehicle poses a driving hazard to the first vehicle.

A system comprises a computer including a processor and a memory. The memory includes instructions such that the processor is programmed to: estimate a slip condition corresponding to at least one vehicle wheel; and generate, via an explicit Nonlinear Model Predictive Control (NMPC) module, control data for operating the at least one vehicle wheel based on the estimated slip condition. The explicit Nonlinear Model Predictive Control (NMPC) module includes a feedforward control module that is configured to generate adjustment data based on the estimated slip condition, wherein the adjustment data modifies the control data.

A system comprises a computer including a processor and a memory. The memory includes instructions such that the processor is programmed to: estimate a slip condition corresponding to at least one vehicle wheel; and generate, via an explicit Nonlinear Model Predictive Control (NMPC) module, control data for operating the at least one vehicle wheel based on the estimated slip condition. The explicit Nonlinear Model Predictive Control (NMPC) module includes a feedforward control module that is configured to generate adjustment data based on the estimated slip condition, wherein the adjustment data modifies the control data.

A driving force control apparatus includes: a sensor that collects information associated with a state of a vehicle, a driving device that provides a driving force to a drive wheel of the vehicle, and a processor electrically connected with the sensor and the driving device. In particular, the processor calculates a required driving force of a driver and a limit driving force of the vehicle based on at least a portion of information collected by means of the sensor, in a situation where the vehicle is turning. The processor further controls the driving device such that the required driving force does not exceed the limit driving force.

A driving force control apparatus includes: a sensor that collects information associated with a state of a vehicle, a driving device that provides a driving force to a drive wheel of the vehicle, and a processor electrically connected with the sensor and the driving device. In particular, the processor calculates a required driving force of a driver and a limit driving force of the vehicle based on at least a portion of information collected by means of the sensor, in a situation where the vehicle is turning. The processor further controls the driving device such that the required driving force does not exceed the limit driving force.

A vehicle driving assistance apparatus executes a collision avoidance control to avoid a collision of an own vehicle with an object ahead of the own vehicle by forcibly braking and stopping the own vehicle before the object when a collision condition that the own vehicle collides with the object, becomes satisfied. The vehicle driving assistance apparatus acquires information on a situation behind the own vehicle as rearward detection information when the own vehicle tows a trailer, acquires an angle between a moving vector of the own vehicle and a moving vector of the trailer as a towing angle. and keep the collision avoidance control unexecuted even when the collision condition becomes satisfied when a forbiddance condition that the towing angle is equal to or greater than a predetermined towing angle, is satisfied.

A vehicle driving assistance apparatus executes a collision avoidance control to avoid a collision of an own vehicle with an object ahead of the own vehicle by forcibly braking and stopping the own vehicle before the object when a collision condition that the own vehicle collides with the object, becomes satisfied. The vehicle driving assistance apparatus acquires information on a situation behind the own vehicle as rearward detection information when the own vehicle tows a trailer, acquires an angle between a moving vector of the own vehicle and a moving vector of the trailer as a towing angle. and keep the collision avoidance control unexecuted even when the collision condition becomes satisfied when a forbiddance condition that the towing angle is equal to or greater than a predetermined towing angle, is satisfied.

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.

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.