Techniques are described for determining visibility conditions of an environment in which an autonomous vehicle is operated and performing driving related operations based on the visibility conditions. An example method of adjusting driving related operations of a vehicle includes determining, by a computer located in an autonomous vehicle, a visibility related condition of an environment in which the autonomous vehicle is operating, adjusting, based at least on the visibility related condition, a set of one or more values of one or more variables associated with a driving related operation of the autonomous vehicle, and causing the autonomous vehicle to be driven to a destination by causing the driving related operation of one or more devices located in the autonomous vehicle based on at least the set of one or more values.

This application discloses a method and an apparatus for detecting a complexity of a traveling scenario of a vehicle, comprising: obtaining a travelling speed of the vehicle and a travelling speed of a target vehicle; determining, based on the traveling speed of the vehicle and the traveling speed of the target vehicle, a dynamic complexity of a traveling scenario in which the vehicle is located; determining static information of each static factor in the traveling scenario in which the vehicle is currently located; obtaining, based on the static information of each static factor, a static complexity of the traveling scenario in which the vehicle is located; and obtaining, based on the dynamic complexity and the static complexity, a comprehensive complexity of the traveling scenario in which the vehicle is located.

Example data processing methods and apparatus are provided. One example method includes obtaining an image captured by an in-vehicle camera. A to-be-detected target in the image is determined. A feature region corresponding to the to-be-detected target in the image is further determined based on a location of the to-be-detected target in the image. A first parking state is determined based on the image and wheel speedometer information. A first homography matrix corresponding to the first parking state is determined from a prestored homography matrix set, where different parking states correspond to different homography matrices. Image information of the feature region is processed based on the first homography matrix to obtain a detection result.

An example operation includes one or more of detecting a living element in a transport, determining a dangerous condition exists in response to an internal temperature of the transport is outside of a threshold and a negative characteristic is associated with the living element, and positioning the transport to a location to reduce the dangerous condition.

A method for controlling a lower limit of a state-of-charge state of a power battery, and a vehicle are provided, the method includes: detecting, by a detection unit, a minimum ambient temperature and a minimum power battery temperature in at least one time period; evaluating, by an evaluation unit, a minimum power battery temperature in a preset time period after the at least one time period according to the minimum ambient temperature and the minimum power battery temperature in the at least one time period; determining, by a processing unit, a minimum state-of-charge value that meets a start-up power requirement of a vehicle engine at the minimum power battery temperature in the preset time period, and adjusting a lower limit value of the state-of-charge according to the minimum state-of-charge value, in order that the vehicle can be powered-up by a state-of-charge value of the power battery at low temperature environment.

Systems and methods are disclosed for estimating slipperiness of a road surface. This estimate may be obtained using an image sensor mounted on a vehicle. The estimated road slipperiness may be utilized when calculating a risk index for the road, or for an area including the road. If a predetermined threshold for slipperiness is exceeded, corrective actions may be taken. For instance, warnings may be generated to human drivers that are in control of driving vehicle, and autonomous vehicles may automatically adjust vehicle speed based upon road slipperiness detected.

A method for setting a tuning parameter for an Automated Driving System (ADS) of a vehicle is disclosed. A corresponding non-transitory computer-readable storage medium, vehicle control device and a vehicle comprising such a control device are also disclosed. The method comprises receiving environmental data from a perception system of the vehicle, said environmental data comprising a plurality of environmental parameters, determining, by means of a self-learning model, an environmental scenario based on the received environmental data; setting the tuning parameter for the ADS based on the self-learning model and the determined environmental scenario, the tuning parameter defining a dynamic parameter of the ADS, receiving at least one signal representative of a vehicle user feedback on the set tuning parameter, and updating the self-learning model for the set tuning parameter for the identified environmental scenario based on the received vehicle user feedback.

Methods, apparatuses, systems and computer program products for estimating individualized road condition information for a specific vehicle are disclosed. Generic road condition information is received, which is indicative of at least one condition of a road segment. Further, individualization information is received, which is representative of an estimating method to be performed on the generic road condition information to obtain individualized road condition information for the specific vehicle. Individualized road condition information are estimated for the vehicle, wherein the estimating method is applied to the received generic road condition information to obtain individualized road condition information.

An apparatus includes a processor configured to be disposed with a vehicle and a memory coupled to the processor. The memory stores instructions to cause the processor to receive, at least two of: radar data, camera data, lidar data, or sonar data. The sensor data is associated with a predefined region of a vicinity of the vehicle while the vehicle is traveling during a first time period. At least a portion of the vehicle is positioned within the predefined region during the first time period. The method also includes detecting that no other vehicle is present within the predefined region. An environment of the vehicle during the first time period is classified as one state from a set of states that includes at least one of dry, light rain, heavy rain, light snow, or heavy snow, based on at least two of the sensor data to produce an environment classification. An operational parameter of the vehicle based on the environment classification is modified.

A vehicle includes a detection system configured to acquire data regarding operation of the vehicle, and a robotic driving device configured to provide robotic control of the vehicle. The vehicle also includes a control system configured to determine whether the robotic driving device is activated, such that the vehicle is in robotic driving mode; receive a request by a prospective operator of the vehicle to deactivate the robotic driving device to initiate a manual driving mode; determine whether the prospective operator is impaired based on the data; and selectively grant or refuse the request based on the determination.