Systems and methods are provided for navigating a host vehicle. A navigation system for the host vehicle may include at least one processor programmed to receive images representative of an environment of the host vehicle; analyze at least one of the images to identify navigational state information associated with the host vehicle; determine a plurality of first potential navigational actions for the host vehicle based on the navigational state information; determine respective future states for the plurality of first potential navigational actions; determine a plurality of second potential navigational actions for the host vehicle based on the determined respective future states; select, based on the plurality of second potential navigational actions, one of the plurality of first potential navigational actions; and cause an adjustment of a navigational actuator of the host vehicle to implement the selected one of the plurality of first potential navigational actions.

The technology relates to controlling a vehicle in an autonomous driving mode in accordance with behavior predictions for other road users in the vehicle's vicinity. In particular, the vehicle's onboard computing system may predict whether another road user will perform a continuing lane driving operation, such as going straight in a turn-only lane. Sensor data from detected/observed objects in the vehicle's nearby environment may be evaluated in view of one or more possible behaviors for different types of objects. In addition, roadway features, in particular whether lane segments are connected in a roadgraph, are also evaluated to determine probabilities of whether other road users may make an improper continuing lane driving operation. This is used to generate more accurate behavior predictions, which the vehicle can use to take alternative (e.g., corrective) driving actions.

A method and a device for operating an assistance system of a vehicle involves detecting laterally static and laterally dynamic objects, which the vehicle is to drive past, as lateral boundary objects. A respective lateral distance of the vehicle from the respective lateral boundary object is detected. A speed of the respective laterally dynamic object is determined and at least the respectively laterally dynamic object is classified according to its type. A set of characteristic curves is stored in a control unit of the vehicle, the characteristic curves of the set being assigned in each case to an environmental situation predetermined depending on lateral boundary objects. It is predetermined by a respective characteristic curve for the respective environmental situation at what maximum speed the vehicle is to drive past a lateral boundary object at different lateral distances from the latter.

Methods and systems for autonomous vehicle recharging or refueling are disclosed. Autonomous electric vehicles may be automatically recharged by routing the vehicles to available charging stations when not in operation, according to methods described herein. A charge level of the battery of an autonomous electric vehicle may be monitored until it reaches a recharging threshold, at which point an on-board computer may generate a predicted use profile for the vehicle. Based upon the predicted use profile, a time and location for the vehicle to recharge may be determined. In some embodiments, the vehicle may be controlled to automatically travel to a charging station, recharge the battery, and return to its starting location in order to recharge when not in use.

A method and system for predicting a near future path for a vehicle. For predicting the near future path sensor data and vehicle driving data is collected. Road data is collected indicative of a roadway on the presently occupied road for the vehicle. The sensor data and the vehicle driving data is pre-processed to provide object data comprising a time series of previous positions, headings, and velocities of each of the objects relative the vehicle. The object data, the vehicle driving data, and the road data is processed in a deep neural network to predict the near future path for the vehicle. The invention also relates to a vehicle comprising the system.

A system uses a machine learning based model to determine attributes describing states of mind and behavior of traffic entities in video frames captured by an autonomous vehicle. The system classifies video frames according to traffic scenarios depicted, where each scenario is associated with a filter based on vehicle attributes, traffic attributes, and road attributes. The system identifies a set of video frames associated with ground truth scenarios for validating the accuracy of the machine learning based model and predicts attributes of traffic entities in the video frames. The system analyzes video frames captured after the set of video frames to determine actual attributes of the traffic entities. Based on a comparison of the predicted attributes and actual attributes, the system determines a likelihood of the machine learning based model making accurate predictions and uses the likelihood to generate a navigation action table for controlling the autonomous vehicle.

Systems, methods and/or computer program products for improving autonomous vehicle operation by enabling communication between the autonomous vehicles and BCI systems publishing signals from nearby pedestrians. Wearable BCI devices worn by pedestrians analyze brainwave signals and classify the brainwave signals in order to filter out signals that are unrelated to crossing the street or the directionality of travel by the pedestrian. BCI devices publish, or broadcast brain wave signals related to crossing the street or directionality of travel to the surrounding area where autonomous vehicle receive and process the brainwave signals being published. The autonomous vehicles predict movements of nearby pedestrians based on the intended direction of travel signified by the collected brainwave signals, and the autonomous vehicles select driving actions in response to the anticipated movements of nearby pedestrians.

A driving support apparatus assists driving of a vehicle by performing control of the vehicle corresponding to a blind spot. A driving support apparatus stores risk estimation information for calculating a risk estimation value indicating a height of a risk of a blind spot, and determines a plurality of blind spots as one conglomerate risk when the plurality of blind spots are detected in front of a vehicle and satisfy a predetermined condition. When the conglomerate risk exists, the driving support apparatus calculates a risk estimation value indicating the height of the risk in each of the plurality of blind spots on the basis of the risk estimation information, and determines a control end position for ending the control of the vehicle corresponding to the blind spot and a target passing speed of the control end position of the vehicle on the basis of the risk estimation value.

One variation of a method for communicating state, intent, and context of an autonomous vehicle includes: at a first time, displaying a first icon representing a current state of a vehicle on a rear-facing visual display arranged on the vehicle; navigating toward an intersection; at a second time, detecting a state of the intersection ahead of the vehicle; rendering a second icon representing the state of the intersection at the second time on the rear-facing visual display; detecting a change in the state of the intersection at a third time succeeding the second time; selecting a next navigation action for the vehicle responsive to the change in the state of the intersection at the third time; prior to executing the next navigation action, rendering a third icon representing the next navigation action on the rear-facing visual display; and autonomously executing the next navigation action.

A method helps to protect an occupant of a vehicle (10) equipped with an automated driving system (200) and a vehicle safety system (100) by detecting low impact crash events (99) with the vehicle (10). The method includes utilizing automated driving sensors (220, 230, 240, 250, 260) of the automated driving system (200) to identify possible low impact collision risks. The method also includes utilizing vehicle safety system sensors (110, 115, 120, 125, 130) of the vehicle safety system to determine a low impact collision resulting from the identified possible low impact collision. A vehicle safety system (100) includes an airbag controller unit (150) configured to implement the method to determine low impact crash events with the vehicle (10).