To provide a technology that makes it possible to recognize a target object quickly and accurately. An information processing apparatus according to the present technology includes a controller. The controller recognizes a target object on the basis of event information that is detected by an event-based sensor, and transmits a result of the recognition to a sensor apparatus that includes a sensor section that is capable of acquiring information regarding the target object.

A computer system obtains a plurality of road images captured by one or more cameras attached to one or more vehicles. The one or more vehicles execute a model that facilitates driving of the one or more vehicles. For each road image of the plurality of road images, the computer system determines, in the road image, a fraction of pixels having an ambiguous lane marker classification. Based on the fraction of pixels, the computer system determines whether the road image is an ambiguous image for lane marker classification. In accordance with a determination that the road image is an ambiguous image for lane marker classification, the computer system enables labeling of the image and adds the labeled image into a corpus of training images for retraining the model.

A method may include obtaining input information relating to an environment in which an autonomous vehicle (AV) operates, the input information describing at least one of: a state of the AV, an operation of the AV within the environment, a property of the environment, or an object included in the environment. The method may include identifying a first object in the vicinity of the AV based on the obtained input information. The method may include determining a first object rule corresponding to the first object, the first object rule indicating suggested driving behavior for interacting with the first object. The method may include determining a first decision that follows the first object rule and sending an instruction to a control system of the AV, the instruction describing a given operation of the AV responsive to the first object rule according to the first decision.

A user-generated graphical representation can be sent into a generative network to generate a synthetic image of an area including a road, the user-generated graphical representation including at least three different colors and each color from the at least three different colors representing a feature from a plurality of features. A determination can be made that a discrimination network fails to distinguish between the synthetic image and a sensor detected image. The synthetic image can be sent, in response to determining that the discrimination network fails to distinguish between the synthetic image and the sensor-detected image, into an object detector to generate a non-user-generated graphical representation. An objective function can be determined based on a comparison between the user-generated graphical representation and the non-user-generated graphical representation. A perception model can be trained using the synthetic image in response to determining that the objective function is within a predetermined acceptable range.

A vehicle control device has a first calculating unit calculating a first arrival time until a vehicle reaches a lane change position where a first lane along which the vehicle is traveling and a second lane merge, or the first lane and the second lane separates, a second calculating unit calculating a second arrival time until another vehicle traveling along the second lane reaches the lane change position, and a speed control unit controlling the speed of the vehicle such that the vehicle enables to make a lane change to the second lane ahead of the other vehicle when the first arrival time is shorter than the second arrival time, and controlling the speed of the vehicle such that the vehicle enables to make a lane change to the second lane behind the other vehicle when the first arrival time is equal to or greater than the second arrival time.

Specifications are input, comprising: a plurality of lanes in an environment for a controlled system; a plurality of lane maneuvers associated with the plurality of lanes; a plurality of lane subconditions associated with the controlled system; and a rule set comprising a plurality of rules, wherein a rule in the rule set specifies a rule condition and a rule action to take when the rule condition is satisfied, wherein the rule condition comprises a corresponding set of lane subconditions, and wherein the rule action comprises a corresponding lane maneuver. The controlled system is automatically navigated dynamically, at least in part by: monitoring the plurality of lane subconditions; evaluating rule conditions associated with the plurality of rules in the rule set to determine one or more rules whose corresponding rule conditions has been met; and executing one or more lane maneuvers that correspond to the one or more determined rules.

A method for operating a vehicle includes carrying out a lane-keeping control of the vehicle along a course of a lane travelled in by the vehicle. When lane markings are detected, the course of the lane is determined on a basis of detected lane markings. When lane markings are not detected, the course of the lane is determined in a mapped-based manner on a basis of data from a digital map where a rough localization of the vehicle in the digital map and a fine localization of the vehicle in the digital map is performed.

Systems and methods for distracted driving detection are described. A method includes receiving proximate vehicle data about a proximate vehicle proximate to the host vehicle. The method also includes estimating one or more baselines for a predetermined future time for the proximate vehicle from the proximate vehicle data. The method further includes comparing current kinematic data of the proximate vehicle data for the predetermined future time to the one or more baselines. The method includes generating distraction flags associated with the proximate vehicle based on the comparison. The method also includes controlling one or more vehicle systems of the host vehicle based on the generated distraction flags.

Path generation apparatus configured to generate target travel path of vehicle, includes: external sensor mounted on vehicle and configured to detect external situation centered on advancing direction of vehicle; and electronic control unit including processor and memory coupled to processor. Electronic control unit is configured to perform: recognizing travel lane where vehicle travels based on external situation detected by external sensor; deriving first function representing travel path along center of travel lane; calculating curvature radius of travel lane; determining offset amount to be offset inward in turning direction from center of travel lane based on curvature radius; and deriving second function representing target travel path of vehicle that has been offset based on first function and offset amount.

A vehicle driving assist device includes a traveling environment recognizer, an obstacle recognizer, an emergency collision, an oncoming moving body recognizer, a lateral position calculator, a risk degree calculator, and a preliminary collision avoidance controller. The obstacle recognizer recognizes, based on information recognized by a traveling environment recognizer, an obstacle on a vehicle's traveling path. An emergency collision avoidance controller performs emergency collision avoidance control for avoiding a collision with the obstacle. An oncoming moving body recognizer recognizes, an oncoming moving body in an oncoming lane. A lateral position calculator calculates a distance from a lane marker defining the oncoming lane to a reference position of the oncoming moving body. A risk degree calculator calculates a risk degree for the oncoming moving body. A preliminary collision avoidance controller recognizes the oncoming moving body as the obstacle, and performs preliminary control before the emergency collision avoidance control.