Methods and systems for autonomous and semi-autonomous vehicle control relating to anomalies are disclosed. Anomalous conditions with a vehicle operating environment, such as ice patches or flooded roads, may be identified and categorized using autonomous vehicle operating data, and corrective actions to mitigate the impact of such anomalies may be taken. Corrective actions may include maneuvering the vehicle in the area of the anomaly or rerouting the vehicle around the area of the anomaly. A vehicle encountering an anomaly may further communicate an alert to warn other nearby vehicles, including non-autonomous vehicles. Such communication may be limited to anomalies of certain types or severity, and duplicative communications may be suppressed. Vehicles receiving such alerts may take corrective actions or present information regarding the anomaly for operator response.

Methods and systems autonomously parking and retrieving vehicles are disclosed. Available parking spaces or parking facilities may be identified, and the vehicle may be navigated to an available space from a drop-off location without passengers. Special-purpose sensors, GPS data, or wireless signal triangulation may be used to identify vehicles and available parking spots. Upon a user request or a prediction of upcoming user demand, the vehicle may be retrieved autonomously from a parking space. Other vehicles may be autonomously moved to facilitate parking or retrieval.

Disclosed is a vehicular electronic device including a processor which, in the situation in which an autonomous vehicle generates a horn signal during autonomous driving, determines a horn-signal-transmitting entity and a horn-signal-receiving entity, selects at least one of a horn signal in an audible frequency band or a horn signal in an inaudible frequency band, and outputs the selected horn signal to the horn-signal-receiving entity.

A surroundings monitoring apparatus obtains the position of an object from a captured image of a region in a heading direction of a vehicle, and obtains a position obtainment accuracy. When the distance between the object and the vehicle becomes relatively short, the position obtainment accuracy increases. However, the distance between the object and the vehicle becomes shorter, the position obtainment accuracy may decrease. Therefore, if collision avoidance control is performed for an object selected on the basis of the position obtainment accuracy, there is a possibility that the collision avoidance control is not performed for an object which is most likely to collide with the vehicle. In view of this, the apparatus obtains, for each object, a required deceleration which is the magnitude of acceleration necessary for stoppage at a position before the object, and performs the collision avoidance control for an object which is the largest in the required deceleration.

A vehicle speed control device 1 includes a lane detection unit 120 configured to detect a road outside line of a lane on which a host vehicle V is traveling, a target detection unit 121 configured to detect a target that is present outside the road outside line, and a speed control unit 123 configured to control a speed of the host vehicle V such that the host vehicle V passes a lateral side of the target at a speed lower than a speed of the host vehicle V when the target has been detected. After the host vehicle V has passed the lateral side of the target, the speed control unit 123 accelerates the host vehicle V up to the speed when the target has been detected.

A method, computer program product, and computing system for receiving metric data that is based, at least in part, upon sensor data generated by various sensors of an autonomous vehicle; processing the metric data; and generating a temporal understanding with respect to the autonomous vehicle based, at least in part, upon the metric data.

A vehicle sharing system includes a vehicle having interior transceiver modules associated with different passenger seating areas and a vehicle computing system (VCS) including a processor and a memory in communication with the modules and programmed to detect occupancy status of each seating area based on signals from the modules and to communicate the occupancy statuses to a remote server to facilitate scheduling of ride-sharing passengers for a specified seating area of the vehicle. The reserved seating location may be used to align the seating location/door with a passenger during pick-up, adjust vehicle accessory settings associated with the reserved seating location, and activate a visual indicator to direct the passenger to the assigned/reserved seating location.

A collision avoidance support device comprises target detection unit, target type determination unit, relative position determination unit, target track prediction unit, and vehicle track prediction unit, obstacle determination unit. The vehicle track prediction unit is configured to enlarge said width of a vehicle predicted track compared with a case where an enlargement condition is not satisfied when the enlargement condition is satisfied. The enlargement condition is satisfied when the relative position determination unit detects that a target determined to be a pedestrian by the target type determination unit is positioned on a travel lane at least once.

Systems, apparatus and methods implemented in algorithms, hardware, software, firmware, logic, or circuitry may be configured to process data and sensory input to determine whether an object external to an autonomous vehicle (e.g., another vehicle, a pedestrian, road debris, a bicyclist, etc.) may be a potential collision threat to the autonomous vehicle. The autonomous vehicle may be configured to implement interior active safety systems to protect passengers of the autonomous vehicle during a collision with an object or during evasive maneuvers by the autonomous vehicle, for example. The interior active safety systems may be configured to provide passengers with notice of an impending collision and/or emergency maneuvers by the vehicle by tensioning seat belts prior to executing an evasive maneuver and/or prior to a predicted point of collision.

Systems and methods are provided for crowdsourcing a sparse map for autonomous vehicle navigation. In one implementation, a non-transitory computer-readable medium may include a sparse map for autonomous vehicle navigation along a road segment. The sparse map may include at least one line representation of a road surface feature extending along the road segment, each line representation representing a path along the road segment substantially corresponding with the road surface feature, and wherein the road surface feature is identified through image analysis of a plurality of images acquired as one or more vehicles traverse the road segment and a plurality of landmarks associated with the road segment.