A driver assistance system according to an embodiment of present disclosure includes: a camera disposed on the vehicle to have an external field of view of a vehicle and configured to obtain image data; a radar disposed on the vehicle to have a field of sensing outside the vehicle and configured to obtain radar data; and a controller including a processor configured to process the image data and the radar data, and the controller is configured to determine whether a rear vehicle changes direction based on the image data obtained by the camera, determine whether there is a risk of collision with the rear vehicle based on the radar data when the rear vehicle does not change direction and control at least one of a steering system or a vehicle velocity control system of the vehicle to avoid the rear vehicle when it is determined that there is a risk of collision with the rear vehicle.

A system uses a machine learning based model to determine attributes describing states of mind and behavior of traffic entities in video frames captured by an autonomous vehicle. The system classifies video frames according to traffic scenarios depicted, where each scenario is associated with a filter based on vehicle attributes, traffic attributes, and road attributes. The system identifies a set of video frames associated with ground truth scenarios for validating the accuracy of the machine learning based model and predicts attributes of traffic entities in the video frames. The system analyzes video frames captured after the set of video frames to determine actual attributes of the traffic entities. Based on a comparison of the predicted attributes and actual attributes, the system determines a likelihood of the machine learning based model making accurate predictions and uses the likelihood to generate a navigation action table for controlling the autonomous vehicle.

A global multi-vehicle decision making system is disclosed for providing real-time motion planning and coordination of one or multiple connected and automated and/or semi-automated vehicles (CAVs) in an interconnected traffic network that includes one or multiple non-controlled vehicles (NCVs), one or multiple conflict zones and one or multiple conflict-free road segments. The system includes a receiver configured to acquire infrastructure sensing signals, at least one memory configured to store map and programs, and at least one processor configured to perform steps of formulating a global mixed-integer programming (MIP) problem using the infrastructure sensing signals, computing a motion plan for each CAV and each NCV in the traffic network by solving the global MIP problem, computing an optimal sequence of entering/exiting times and a sequence of average velocities for each CAV and each NCV, and computing a velocity profile and/or one or multiple planned stops for each CAV.

This application provides a collision detection method and related apparatus. An image taken by a photographing unit may be used to predict whether a collision with a to-be-detected target will occur. In a current collision prediction method, a type of the to-be-detected target needs to be determined first based on the image taken by the photographing unit, which requires consuming of a large amount of computing power. In the collision prediction method provided in this application, a change trend of a distance between the to-be-detected target and a vehicle in which the apparatus is located may be determined based on the distances between the to-be-detected target and the vehicle at different moments, to predict a collision between the to-be-detected target and the vehicle. This method can improve efficiency in collision prediction and reduce energy consumption in predicting collision.

Aspects and implementations of the present disclosure address shortcomings of the existing technology by enabling Doppler-assisted segmentation of points in a point cloud for efficient object identification and tracking in autonomous vehicle (AV) applications, by: obtaining, by a sensing system of the AV, a plurality of return points comprising one or more velocity values and one or more coordinates of a reflecting region that reflects a signal emitted by the sensing system, the one or more velocity values and the one or more coordinates obtained for the same instance of time, identifying that the set of the return points is associated with an object in an environment, and causing a driving path of the AV to be determined in view of the object.

Methods and systems for assessing, detecting, and responding to malfunctions involving components of autonomous vehicles and/or smart homes are described herein. A risk of malfunction and/or cyber-attack may be determined by collecting operating data from a plurality of autonomous vehicles and/or smart homes. The operating data may be analyzed to identify occurrences of a component malfunctioning. For each component, a risk associated with malfunctioning and/or cyber-attack may be determined based upon the identified occurrences. Based on the risks, at least one result associated with the malfunction and/or cyber-attack may be determined. A component profile may be generated based upon the determined risk and/or the impact of the determined results.

A computer, including a processor and a memory, the memory including instructions to be executed by the processor to, based on sensor data in a vehicle, determine a database that includes object data for a plurality of objects, including, for each object, an object identification, a measurement of one or more object attributes, and an uncertainty specifying a probability of correct object identification, for the object identification and the object attributes determined based on the sensor data, wherein the object attributes include an object size, an object shape and an object location. The instructions include further instructions to determine a map based on the database including the respective locations and corresponding uncertainties for the vehicle type and download the map to a vehicle based on the vehicle location and the vehicle type.

Systems and methods for connected vehicle and mobile device communications are provided herein. An example method includes determining a distracted condition for at least one of a driver or a pedestrian by evaluating actions occurring within in a vehicle of the driver or on a mobile device of the pedestrian; determining a distraction level for either the driver or the pedestrian based on the actions occurring within in the vehicle or on the mobile device; and providing an alert message to the mobile device or a human machine interface of the vehicle based on the distraction level, the alert message warning of a distracted condition of the pedestrian or the driver.

A method for assisting in turning a vehicle, the method may include detecting or estimating that the vehicle is about to turn to a certain direction or is turning to the certain direction; sensing a relevant portion of an environment of the vehicle to provide sensed information, wherein the relevant portion of the environment is positioned at a side of the vehicle that corresponds with the certain direction; applying an artificial intelligence process on the sensed information to (i) detect objects within the relevant portion of the environment and (ii) estimate expected movement patterns of the objects within a time frame that ends with an expected completion of the turn of the vehicle; determining, given an expected trajectory of the vehicle during the turn and the expected movement patterns of the objects, whether at least one of the objects is expected to cross the trajectory of the vehicle during the turn; and responding to an outcome of the determining.

A vehicle control method includes recognizing a vicinity of a vehicle, setting a risk index for a traffic participant, and controlling a vehicle-mounted instrument of the vehicle based on the risk index which is set by the setter, and setting a risk index for a position at which the traffic participant will be present in the future based on ease of entry of the traffic participant from a sidewalk to a roadway adjacent to the sidewalk in a region that the traffic participant traveling on the sidewalk will enter in the future, and increasing a risk index to be set on the roadway side as there is a greater tendency for the traffic participant to enter the roadway.