Abstract: A genome-specific gene sequence fragment of female Acipenser Dabryanus and a method for identifying the sex of Acipenser Dabryanus are disclosed. The gene sequence fragment of Acipenser Dabryanus is used to identify the sex of Acipenser Dabryanus. The method includes: performing PCR amplification on the DNA of the Acipenser Dabryanus to be tested, and judging that the Acipenser Dabryanus to be tested is female when a resultant product from the amplification shows 3 bands on an agarose gel and the Acipenser Dabryanus to be tested is male when a resultant product from the amplification shows 2 bands on an agarose gel, alternatively, judging that the Acipenser Dabryanus to be tested is female when a resultant fragment from the amplification has a 1402 bp band and it is male if there is no 1402 bp band. The gene sequence fragment is used to quickly and efficiently identify the sex of Acipenser Dabryanus.

Abstract: Vehicles and systems and methods for providing rearview displays for vehicles are provided. An exemplary system is provided for a vehicle having a gate moveable between closed configuration and opened configurations. The system includes a camera mounted to the gate and configured to collect dynamic pixel images of an area behind the vehicle; a display screen configured to display the collected dynamic pixel images; and memory configured to include executable instructions and a processor configured to execute executable instructions that enable the processor to crop the collected dynamic pixel images to display a lower portion of the collected dynamic pixel images on the display screen when the gate is in the closed configuration, and to crop the collected dynamic pixel images to display an upper portion of the collected dynamic pixel images on the display screen when the gate is in the opened configuration.

Abstract: A RLP system for a host vehicle includes a memory and levels. The memory stores a RLP algorithm, which is a multi-agent collaborative DQN with PER algorithm. A first level includes a data processing module that provides sensor data, object location data, and state information of the host vehicle and other vehicles. A second level includes a coordinate location module that, based on the sensor data, the object location data, the state information, and a refined policy provided by the third level, generates an updated policy and a set of future coordinate locations implemented via the first level. A third level includes evaluation and target neural networks and a processor that executes instructions of the RLP algorithm for collaborative action planning between the host and other vehicles based on outputs of the evaluation and target networks and to generate the refined policy based on reward values associated with events.

Abstract: A RLP system for a host vehicle includes a memory and levels. The memory stores a RLP algorithm, which is a multi-agent collaborative DQN with PER algorithm. A first level includes a data processing module that provides sensor data, object location data, and state information of the host vehicle and other vehicles. A second level includes a coordinate location module that, based on the sensor data, the object location data, the state information, and a refined policy provided by the third level, generates an updated policy and a set of future coordinate locations implemented via the first level. A third level includes evaluation and target neural networks and a processor that executes instructions of the RLP algorithm for collaborative action planning between the host and other vehicles based on outputs of the evaluation and target networks and to generate the refined policy based on reward values associated with events.

Abstract: Systems and methods are disclosed and include a node storing a local table and a control module that collects heterogeneous sensor data, tracks objects in an environment of the node, and converts the heterogeneous sensor data into time series data. An object-relational mapping module formats the time series data for direct compatibility to data in a master database of a cloud-based server. A correlation module further formats the time series data to provide resultant data in a single format commonly used by multiple map sources. A first primitive extraction module extracts primitives from the resultant data. An edge computing module generates a first table of data corresponding to the primitives and compatible with a master table of the master database, detects a changed primitive, uploads the changed primitive to the cloud-based server, receives updated data based on the changed primitive, and updates the local table based on the updated data.

Abstract: An AI system is provided and includes a long short term memory and data processing, feature selection, behavior recognition, parameter, and driver assistance modules. The data processing module, at a host vehicle: receives training data associated with features; based on the training data, determines candidate features using a synchronous sliding window; and indexes and ranks the candidate features. The feature selection module selects ones of the candidate features having higher priority than other ones of the candidate features and filters the training data of the selected features to provide filtered data. The behavior recognition module determines a behavior of a remote vehicle based on the filtered data. The parameter module determines a parameter of the remote vehicle based on the determined behavior. The long short term memory predicts a value of the parameter. The driver assistance module assists in operation of the host vehicle based on the predicted value.