A control apparatus includes a controller configured to, upon determining, based on behavior classification, that a behavior of a passenger detected from a captured image or a sound of inside of a vehicle is threatening, transmit a first instruction to increase tension of a seat belt worn by the passenger.

We generally describe a collision prevention system (100) comprising: a localization system (402) for determining positions of an autonomous vehicle (104) and a human (106); and a collision determination unit (404) coupled to or in communication with the localization system (402), wherein the collision determination unit (404) is configured to determine, based on the determined positions of the autonomous vehicle (104) and the human (106), whether a predefined condition for an anticipated collision of the autonomous vehicle (104) with the human (106) is met; wherein the collision prevention system (100) is configured to: lock the autonomous vehicle (104) if the predefined condition is met; alert the human (106) for whom the predefined condition for colliding with the autonomous vehicle (104) is met; and allow unlocking of the autonomous vehicle (104) to be performed or initialized by the alerted human (106) only.

Various aspects of a device and method to manage interaction with one or more control circuits in a vehicle and one or more wearable devices are disclosed herein. The device comprises one or more circuits configured to receive a first set of input values from the one or more wearable devices associated with a first user. The one or more wearable devices are communicatively coupled to the device used in the vehicle. A second set of input values is received from one or more vehicle sensors embedded in the vehicle. An operating mode of the device is determined based on the received first set of input values and the second set of input values. One or more functions of the vehicle are controlled based on the determined operating mode of the device.

An autonomous vehicle configured for active sensing may also be configured to weigh expected information gains from active-sensing actions against risk costs associated with the active-sensing actions. An example method involves: (a) receiving information from one or more sensors of an autonomous vehicle, (b) determining a risk-cost framework that indicates risk costs across a range of degrees to which an active-sensing action can be performed, wherein the active-sensing action comprises an action that is performable by the autonomous vehicle to potentially improve the information upon which at least one of the control processes for the autonomous vehicle is based, (c) determining an information-improvement expectation framework across the range of degrees to which the active-sensing action can be performed, and (d) applying the risk-cost framework and the information-improvement expectation framework to determine a degree to which the active-sensing action should be performed.

Embodiments of the present disclosure provide a control method of an unmanned vehicle and an unmanned vehicle, which have excellent safety. The control method of the unmanned vehicle includes: detecting vibration information and running attitude information of the unmanned vehicle; according to the vibration information, the running attitude information and a running status of the unmanned vehicle, determining a condition of the unmanned vehicle, wherein the running status of the unmanned vehicle includes a stop status and a driving status; and when the condition of the unmanned vehicle is abnormal, controlling the unmanned vehicle according to an abnormal condition coping strategy.

A path-adapting system for precautionary path planning of a host vehicle. The path-adapting system determines conditions of a liquid and/or solid loose material in the air and/or on the road surface in vicinity of the host vehicle, determines movement attributes in relation to the host vehicle of at least a first object in host vehicle surroundings, determines based on the conditions and movement attributes, at least a first estimated upcoming host vehicle position occurring at an estimated upcoming at least first time instance at which the at least first detected object is estimated to direct the material onto the host vehicle and/or the host vehicle is estimated to direct the material onto the at least first detected object, and determines a driving path, altering for the at least first time instance the at least first estimated upcoming host vehicle position to a modified host vehicle position.

Systems, devices, products, apparatuses, and/or methods for generating a driving path for an autonomous vehicle on a roadway by determining one or more prior probability distributions of one or more motion paths for one or more objects that have previously moved in a geographic location and/or for controlling travel of an autonomous vehicle on a roadway by predicting movement of a detected object according to one or more prior probability distributions of one or more motion paths for one or more objects that have previously moved in a geographic location.

The vehicle includes: a sensor part configured to acquire occupancy information of an surrounding area of the vehicle and a speed of the vehicle; a camera configured to acquire a surrounding image of the vehicle; and a controller configured to form map information based on the occupancy information according to movement of the vehicle, determine presence or absence of an obstacle around the vehicle based on the map information and the surrounding image, and control, in response to presence of the obstacle, the vehicle based on the presence of the obstacle and a possibility of collision of the vehicle derived from the speed of the vehicle and the map information.

Among other things, techniques are described for screening and monitoring the health of a vehicle user including receiving sensor data produced by a sensor at the vehicle, processing the sensor data to determine at least one health condition of the user of the vehicle, and in response to determining the at least one health condition, executing a vehicle function selected from a plurality of vehicle functions based on the at least one health condition.

Systems and methods of model or prediction algorithm selection are provided. An autonomous control system may include a perception component that, based on environmental inputs regarding an object(s), a vehicle's operating characteristics, etc., outputs a current state of the vehicle's surrounding environment. This in turn, is used as input to a prediction component comprising a plurality of prediction algorithms. The prediction component outputs a set of predictions regarding the trajectory of the object(s). Accordingly, for each object, a set of trajectories at specific timesteps may be generated by the different prediction algorithms which are input to a planner component. These trajectories may then be analyzed, compared, or otherwise processed to determine which trajectory regarding the object is most accurate. The prediction algorithm or model that produced the most accurate predicted trajectory may then be used for subsequent predictions/timesteps.