Provided is a vehicle control device configured to execute specific control while at the same time sequentially switching the specific control that defines an operation to be executed by each of a plurality of surrounding environment acquisition devices configured to acquire information on a surrounding environment of a vehicle, a recognition/determination ECU configured to generate a path on which the vehicle is to travel, and an integrated control ECU configured to control the vehicle based on the generated path along with an elapse of time since detection of an anomaly, when the anomaly is detected in any one of the plurality of surrounding environment acquisition devices, the recognition/determination ECU, and the integrated control ECU.

An autonomous driving control device includes: a plurality of control devices that each have a first storage unit and a processing unit; a load control unit; a second storage unit; and a failure detection unit. The programs include autonomous driving programs and other programs. When a failure is detected, the load control unit unloads at least some of the other programs from the first storage unit of a normal control device, and loads the autonomous driving program corresponding to that loaded in the first storage unit of a failed control device from the second storage unit into the first storage unit of the normal control device.

A vehicle includes a sensor circuit configured to detect an obstacle in a first region which is located on the predetermined traveling route and in a second region which is adjacent to the first region on the predetermined traveling route, the second region being farther than the first region. The vehicle enters the first region in a case where: there is no obstacle in the first region; and there is no obstacle in the second region, and does not enter the first region and stops before the first region in a case where: there is no obstacle in the first region; and there is an obstacle in the second region.

An apparatus and a method for controlling a velocity of an autonomous driving vehicle is provided. The method includes steps of: obtaining information of an environment surrounding the vehicle when an obstacle is detected to be on a planning path of the vehicle; obtaining an initial reference velocity profile of the vehicle; determining a safety factor based on the initial reference velocity profile, the information of the environment and information of the vehicle, wherein the safety factor at least comprises a safety distance between the vehicle and the obstacle for the vehicle to follow the obstacle; determining an optimized reference velocity profile based on the information of the environment, the information of the vehicle and the safety factor; and performing the step of determining the safety factor by using the optimized reference velocity profile as the initial reference velocity profile and the step of determining the optimized reference velocity iteratively.

A driving risk assessment and control decision-making method for an autonomous vehicle includes: detecting the surrounding state of the vehicle multiple times to generate multiple sensing signals; quantifying the sensing signals to generate multiple sensing values and calculating a sensing average value of the sensing values; calculating a sensing error value between each sensing value and the sensing average value, a sensing error average value of sensing error values and a sensing error variation value; integrating the sensing error average value, the sensing error variation value and a sensor systematic error average value and a sensor systematic error variation value to generate a sensing signal correction value; combining the sensing values and the sensing signal correction value to generate multiple sensing signal reference values; judging whether a stability of the sensing signal reference values falls within a preset range; generating a control mechanism based on the judgement.

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.

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for selecting a pull-over location using machine learning. One of the methods includes obtaining data specifying a target pull-over location for an autonomous vehicle travelling on a roadway. A plurality of candidate pull-over locations in a vicinity of the target pull-over location are identified. For each candidate pull-over location, an input that includes features of the candidate pull-over location is processed using a machine learning model to generate a respective likelihood score representing a predicted likelihood that the candidate pull-over location is an optimal location. The features of the candidate pull-over location include one or more features that compare the candidate pull-over location to the target pull-over location. Using the respective likelihood scores, one of the candidate pull-over locations is selected as an actual pull-over location for the autonomous vehicle.

The present disclosure provides an autonomous driving vehicle detection method and apparatus, an electronic device and a readable storage medium, and relates to the field of computer technologies, and in particular, to the field of artificial intelligence technologies such as intelligent transportation and autonomous driving technologies. A specific implementation solution involves: acquiring passengers' ride information on an autonomous driving vehicle; determining a vehicle component change state of the autonomous driving vehicle according to the ride information of the autonomous driving vehicle; wherein the vehicle component change state includes a door change state of the autonomous driving vehicle and a seat belt change state of the autonomous driving vehicle; and determining a seat load of the autonomous driving vehicle according to the vehicle component change state of the autonomous driving vehicle.

Systems and methods described herein are directed toward executing an intervention based on monitored physiological and/or environmental parameters according to some embodiments. In some embodiments, the system is configured to execute the intervention if one or more parameters are outside a pre-determined threshold. In some embodiments, an intervention can include and audible message. In some embodiments, the intervention can include taking control of a vehicle. In some embodiments, if there is no response from a user, the system will automatically contact a first responder and/or will automatically route the vehicle to an emergency facility. In some embodiments, the system includes one or more wearable monitoring devices that send signals to the system about physiological and/or environmental parameters. In some embodiments, the system uses artificial intelligence to determine if an abnormal condition exists.

The present disclosure provides an autonomous driving vehicle control method and apparatus, an electronic device and a readable storage medium, and relates to the field of computer technologies, and in particular, to the field of artificial intelligence technologies such as intelligent transportation and autonomous driving technologies. A specific implementation solution involves: constructing a communication link between an autonomous driving vehicle and a cloud; reporting, based on the communication link, emergency information provided by a passenger of the autonomous driving vehicle to the cloud; receiving control information of the autonomous driving vehicle fed back by the cloud, the control information of the autonomous driving vehicle being sent by the cloud in response to a trigger operation of an agent of the cloud based on the emergency information; and controlling the driving of the autonomous driving vehicle according to the control information of the autonomous driving vehicle.