An approach is provided for identifying partitions associated with erratic pedestrian behaviors and their correlations to points of interest. For example, the approach involves receiving sensor data associated with a geographic area. The approach also involves based on the sensor data, determining pedestrian-behavior parameter(s) respectively for partition(s). Each respective partition of the partition(s) represents a respective subarea of the geographic area, a respective time period, or a combination thereof. The approach further involves identifying at least one erratic partition from the partition(s) based on determining that a respective pedestrian-behavior parameter associated with the at least one erratic partition deviates from a baseline pedestrian-behavior parameter by at least a threshold extent. The approach further involves determining a correlation of the at least one erratic partition to at least one map feature of a geographic database. The approach further involves providing the correlation as an output.

An object recognition apparatus includes at least one processor and at least one memory communicably coupled to the processor. The processor sets one or more object presence regions in each of which an object is likely to be present, on the basis of observation data of a distance sensor. The processor estimates an attribute of the object that is likely to be present in each of the one or more object presence regions. The processor sets, on the basis of the attribute of the object, a level of processing load to be spent on object recognition processing to be executed for each of the one or more object presence regions by using at least image data generated by an imaging device. The processor performs, for each of the one or more object presence regions, the object recognition processing corresponding to the set level of the processing load.

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.

The disclosure relates to a method for making sensor data more robust to adversarial perturbations, wherein sensor data are obtained from at least two sensors, wherein the sensor data obtained from the at least two sensors are replaced in each case piecewise by means of quilting, wherein the piecewise replacement is carried out in such a way that the respectively replaced sensor data from different sensors are plausible relative to one another, and wherein the sensor data replaced piecewise are output.

A control unit for controlling a driving function of a vehicle is designed to automatically guide the vehicle longitudinally and/or transversely. The control unit is designed to determine that the driver of the vehicle is presently activating or deactivating, and/or intends to activate or deactivate, the driving function. In response thereto, the control unit is additionally designed to cause a manual control intervention produced by the driver of the vehicle in the longitudinal and/or transversal guidance of the vehicle to be at least partly compensated for and/or suppressed prior to the point in time of the activation or deactivation of the driving function in order to adapt the drive behavior of the vehicle during the transition between the manual longitudinal and/or transversal guidance and the automatic longitudinal and/or transversal guidance.

Embodiments of the disclosure provide a warning method and apparatus for a driving risk, a computing device and a storage medium, and the method includes: obtaining dangerous driving behavior data of a driver in a first time period, and obtaining a correspondence between a quantity of occurrences of dangerous driving behaviors of one or more drivers and a quantity of an actual occurrence of dangerous scenarios to the one or more drivers while driving; predicting, based on a quantity of actual occurrences of the dangerous driving behaviors of the driver, indicated in the dangerous driving behavior data of the driver, and the correspondence, a target quantity of times the driver is predicted to encounter one or more dangerous scenarios in the first time period; and generating warning information based on the target quantity of times.

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.

A vehicle collects data of a plurality of to-be-detected barrier gate crossbars around the vehicle by using a sensor mounted on the vehicle, and transmits the data of the plurality of to-be-detected barrier gate crossbars to a processor; the processor determines data of a target barrier gate crossbar from the data of the plurality of to-be-detected barrier gate crossbars based on a pose of the target barrier gate crossbar, where the target barrier gate crossbar is a barrier gate crossbar of a lane on which the vehicle is located; and the processor determines a status of the target barrier gate crossbar based on the data of the target barrier gate crossbar, and controls, based on the status of the target barrier gate crossbar, the autonomous driving vehicle to pass through the target barrier gate crossbar.

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.

Connected and automated vehicles (CAVs) have shown the potential to improve safety, increase road throughput, and optimize energy efficiency and emissions in several complicated traffic scenarios. This invention describes a mixed-integer programming (MIP) optimization method for global multi-vehicle decision making and motion planning of CAVs in a highly dynamic environment that consists of multiple human-driven, i.e., conventional or manual, vehicles and multiple conflict zones, such as merging points and intersections. The proposed approach ensures safety, high throughput and energy efficiency by solving a global multi-vehicle constrained optimization problem. The solution provides a feasible and optimal time schedule through road segments and conflict zones for the automated vehicles, by using information from the position, velocity, and destination of the manual vehicles, which cannot be directly controlled. Despite MIP having combinatorial complexity, the proposed formulation remains feasible for real-time implementation in the infrastructure, such as in mobile edge computers (MECs).