A method for operating at least one automated vehicle, including the steps: detecting road users by sensors with the aid of the at least one automated vehicle and/or with the aid of sensor systems in an infrastructure; ascertaining predicted traffic routes for the road users with the aid of a computing device based on defined criteria; transmitting control data corresponding to the predicted traffic route to the automated vehicle; and operating the automated vehicle according to the control data.

An information processing device according to the present disclosure includes: an acquisition unit that acquires a model having a structure of a neural network and input information input to the model; and a generation unit that generates basis information indicating a basis for an output of the model after the input information is input to the model based on state information indicating a state of the model after the input of the input information to the model.

Methods and systems for controlling navigation of a vehicle are disclosed. The system will first detect a URU within a threshold distance of a drivable area that a vehicle is traversing or will traverse. The system will then receive perception information relating to the URU, and use a plurality of features associated with each of a plurality of entry points on a drivable area boundary that the URU can use to enter the drivable area to determine a likelihood that the URU will enter the drivable area from that entry point. The system will then generate a trajectory of the URU using the plurality of entry points and the corresponding likelihoods, and control navigation of the vehicle while traversing the drivable area to avoid collision with the URU.

Aspects of the disclosure relate to testing predictions of an autonomous vehicle relating to another vehicle or object in a roadway. For instance, one or more processors may plan to maneuver the autonomous vehicle to complete an action and predict that the other vehicle will take a responsive action. The autonomous vehicle is then maneuvered towards completing the action in a way that would allow the autonomous vehicle to cancel completing the action without causing a collision between the first vehicle and the second vehicle, and in order to indicate to the second vehicle or a driver of the second vehicle that the first vehicle is attempting to complete the action. Thereafter, when the first vehicle is determined to be able to take the action, the action is completed by controlling the first vehicle autonomously using the determination of whether the second vehicle begins to take the particular responsive action.

A vehicle configured to operate in an autonomous mode can obtain sensor data from one or more sensors observing one or more aspects of an environment of the vehicle. At least one aspect of the environment of the vehicle that is not observed by the one or more sensors could be inferred based on the sensor data. The vehicle could be controlled in the autonomous mode based on the at least one inferred aspect of the environment of the vehicle.

Systems and methods for countering the usage of autonomous or semi-autonomous vehicles for ramming attacks on a roadway are disclosed. Digital representations of physical trajectories (e.g., roadway travel routes) across which vehicles are expected or permitted to travel are generated based at least on travel-related data (e.g., sensor readings) received from the vehicles over wireless networks. The disclosed systems and methods further generate digital representations of physical trajectories across which vehicles are not permitted to travel, such that the impermissible physical trajectories constitute a deviation from a safe travel route. Additional travel-related data is continuously received from the vehicles in real-time, and the additional data may be combined with non-vehicle data (e.g., pedestrian travel data) and compared to the generated digital representations of permissible and impermissible physical trajectories to determine if the vehicles' physical trajectory is indicative of a harmful impermissible physical trajectory, such as a vehicular ramming attack.

A vehicle control device that autonomously controls a vehicle so as not to cause rapid deceleration that leads to a deterioration in ride quality. The vehicle control device controls first and second deceleration, means that reduce a speed at a deceleration rate large than a deceleration rate of the first deceleration means. The vehicle control device includes a blind spot area detecting unit that detects a blind spot area of a sensor that recognizes an external environment, and a blind spot object estimating unit that estimates a blind spot object that is a virtual moving body hidden in the blind spot area. When a vehicle approaches the blind spot area at a speed reduced by the first deceleration means, the vehicle is decelerated by the second deceleration means when a type of a moving body detected by the sensor is different from a type of the blind spot object.

Systems and methods for situational behavior of an autonomous vehicle are disclosed. In one aspect, an autonomous vehicle includes at least one perception sensor configured to generate perception data indicative of at least one other vehicle on a roadway, a non-transitory computer readable medium, and a processor. The processor is configured to determine that the other vehicle is violating one or more rules of the roadway based on the perception data, tag the other vehicle as a non-compliant driver, and modify control of the autonomous vehicle in response to tagging the other vehicle as a non-compliant driver.

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.

A sensing system provided in a vehicle capable of running in an autonomous driving mode, includes: a LiDAR unit configured to acquire point group data indicating surrounding environment of the vehicle; and a LiDAR control module configured to identify information associated with a target object existing around the vehicle, based on the point group data acquired from the LiDAR unit. The LiDAR control module is configured to control the LiDAR unit so as to increase a scanning resolution of the LiDAR unit in a first angular area in a detection area of the LiDAR unit, wherein the first angular area is an area where the target object exists.