A vehicle drive assist apparatus for avoiding collision of a vehicle with a recognized object recognizes a surrounding environment around the vehicle; acquires feature information of a three-dimensional object in the surrounding environment; sets a traveling path of the vehicle based on the surrounding environment; recognizes an aerial object based on the feature information; identify a type of the aerial object based on the feature information; determines whether the aerial object has a possibility of hindering traveling of the vehicle; performs steering control based on a control signal; continues normal traveling control when the aerial object does not have the hindrance possibility; estimates a falling point of the aerial object when the aerial object has the hindrance possibility; when the falling point is on the traveling path of the vehicle, sets a new traveling path to steer around the falling point and executes traveling control along the new traveling path.

Various technologies described herein pertain to routing autonomous vehicles based upon spatiotemporal factors. A computing system receives an origin location and a destination location of an autonomous vehicle. The computing system identifies a route for the autonomous vehicle to follow from the origin location to the destination location based upon output of a spatiotemporal statistical model. The spatiotemporal statistical model is generated based upon historical data from autonomous vehicles when the autonomous vehicles undergo operation-influencing events. The spatiotemporal statistical model takes, as input, a location, a time, and a direction of travel of the autonomous vehicle. The spatiotemporal statistical model outputs a score that is indicative of a likelihood that the autonomous vehicle will undergo an operation-influencing event due to the autonomous vehicle encountering a spatiotemporal factor along a candidate route. The autonomous vehicle then follows the route from the origin location to the destination location.

A drive mode switch control device acquires operation information. The drive mode switch control device switches a drive state among at least an autonomous drive state, a manual drive state, and a coordination drive state. The operation detection unit detects a first operation and a second operation based on the operation information when the drive state is not in the manual drive state. The second operation is the drive operation different from the first operation and input after the input of the first operation. The drive mode switch control device switches the drive state from the autonomous drive state to the coordination drive state based on a detection determination of the first operation. The drive mode switch control device switches the drive state from the coordination drive state to the manual drive state based on a detection determination of the first operation.

Disclosed are distributed computing systems and methods for controlling multiple autonomous control modules and subsystems in an autonomous vehicle. In some aspects of the disclosed technology, a computing architecture for an autonomous vehicle includes distributing the complexity of autonomous vehicle operation, thereby avoiding the use of a single high-performance computing system and enabling off-the-shelf components to be use more readily and reducing system failure rates.

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.

A computer-implemented method of evaluating the performance of a target planner for an ego robot in a real or simulated scenario, the method comprising: receiving evaluation data for evaluating the performance of the target planner in the scenario, the evaluation data generated by applying the target planner at incrementing planning steps, in order to compute a series of ego plans that respond to changes in the scenario, the series of ego plans being implemented in the scenario to cause changes in an ego state the evaluation data comprising: the ego plan computed by the target planner at one of the planning steps, and a scenario state at a time instant of the scenario, wherein the evaluation data is used to evaluate the target planner by: computing a reference plan for said time instant based on the scenario state, the scenario state including the ego state at that time instant as caused by implementing one or more preceding ego plans of the series of ego plans computed by the target planner, and computing at least one evaluation score for comparing the ego plan with the reference plan.

The present invention relates to a method for providing a route stipulation for a route system of a vehicle, comprising the following steps: providing a plurality of detected trajectories of further vehicles in a route section to be used, ascertaining a trajectory stipulation from the detected trajectories, ascertaining a deviation zone from the detected trajectories, wherein the deviation zone is determined on the basis of a deviation of at least individual detected trajectories from the trajectory stipulation, determining the route stipulation at least on the basis of the trajectory stipulation and the deviation zone.

A method for finding at least one trigger for human intervention in a control of a vehicle, the method may include receiving, from a plurality of vehicles, and by an I/O module of a computerized system, visual information acquired during situations that are suspected as situations that require human intervention in the control of at least one of the plurality of vehicles; determining, based at least on the visual information, the at least one trigger for human intervention; and transmitting to one or more of the plurality of vehicles, the at least one trigger.

To implement a configuration to calculate a manual driving recoverable time required for a driver who is executing automatic driving in order to achieve a requested recovery ratio (RRR) for each road section, and issue a manual driving recovery request notification on the basis of the calculated time. A data processing unit is included, which calculates a manual driving recoverable time required for a driver who is executing automatic driving in order to achieve a predefined requested recovery ratio (RRR) from automatic driving to manual driving and determines notification timing of a manual driving recovery request notification on the basis of the calculated time. The data processing unit acquires the requested recovery ratio (RRR) for each road section set as ancillary information of a local dynamic map (LDM), and calculates the manual driving recoverable time for each road section scheduled to travel, using learning data for each driver.

According to an embodiment, an information processing device includes a memory and processing circuitry. The processing circuitry is configured to acquire a map defining a target in a coordinate space in which a direction along a traveling direction of a moving object is one of coordinate axes, and approximate a movement route of the moving object with a specified shape area along a coordinate axis in the coordinate space, the specified shape area being a basic unit of collision determination.