An autonomous vehicle (AV) includes features that allows the AV to comply with applicable regulations and statues for performing safe driving operation. An example method for operating the AV includes determining a trajectory related information of a vehicle operating on a roadway on which the AV is operating; receiving sensor data of a first area that includes the vehicle; determining an additional trajectory related information for the AV by comparing the trajectory related information of the vehicle to a current trajectory related information of the AV, wherein the additional trajectory related information is based on a category to which the vehicle belongs, and wherein the additional trajectory related information allows the AV to maintain at least a distance between the AV and the vehicle; and causing the AV to operate in accordance with the additional trajectory related information.

Real-time decision-making for a vehicle using belief state determination is described. Operational environment data is received while the vehicle is traversing a vehicle transportation network, where the data includes data associated with an external object. An operational environment monitor establishes an observation that relates the object to a distinct vehicle operation scenario. A belief state model of the monitor computes a belief state for the observation directly from the operational environment data. The monitor provides the computed belief state to a decision component implementing a policy that maps a respective belief state for the object within the distinct vehicle operation scenario to a respective candidate vehicle control action. A candidate vehicle control action is received from the policy of the decision component, and a vehicle control action is selected for traversing the vehicle transportation from any available candidate vehicle control actions.

A control system (10) is suitable for use in one's own motor vehicle (12) and is configured and intended to use the environmental data provided to determine a position and a speed of a first motor vehicle (28) which is traveling directly ahead of one's own motor vehicle (12) in a first Lane (36), wherein one's own motor vehicle (12) is in the said lane (36). Furthermore, the control system is at least configured and intended to determine a position and a speed of a second motor vehicle (30) which is traveling in a lane (38) adjacent to the first lane (36) from the environmental data provided. Furthermore, the control system is configured and intended to detect from the environmental data provided whether there is a zipper situation. The control system is configured and intended to increase a target distance of one's own motor vehicle (12) to the first motor vehicle, if an amount of a relative speed of the second motor vehicle (30) relative to one's own motor vehicle (12) or relative to the first motor vehicle (28) is less than a predetermined first value, if the second motor vehicle (30) is located between one's own motor vehicle (12) and the first motor vehicle (28) in a longitudinal direction which extends along the adjacent lane (38), and if it was detected that the zipper situation applies.

The invention relates to a method for lane-changing prediction of a target vehicle, the method including: receiving a velocity and a position of the target vehicle; respectively obtaining, based on the velocity and the position of the target vehicle, a first lane-changing probability and a second lane-changing probability of the target vehicle by using a first machine learning model and a second machine learning model; and determining a possibility of lane changing of the target vehicle based on the first lane-changing probability and the second lane-changing probability, the first machine learning model and the second machine learning model being pre-trained and being different from each other. The invention further relates to a device for lane-changing prediction of a target vehicle, a computer storage medium, and a vehicle.

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium that generates path prediction data for agents in the vicinity of an autonomous vehicle using one or more machine learning models. One of the methods includes identifying an agent in a vicinity of an autonomous vehicle navigating through an environment and determining that the agent is within a vicinity of a crossing zone across a roadway. The crossing zone can be a marked crossing zone or an unmarked crossing zone. For example, the crossing zone can be an unmarked crossing zone that has been identified based on previous observations of agents crossing the roadway. In response to determining that the agent is within a vicinity of a crossing zone: (i) features of the agent and of the crossing zone can be obtained; (ii) a first input that includes the features can be processed using a first machine learning model that is configured to generate a first crossing prediction that characterizes future crossing behavior of the agent, and (iii) a predicted path for the agent for crossing the roadway can be determined from at least the first crossing prediction.

Embodiments of this application disclose a vehicle-based data processing method performed by a computer device. The method includes: determining at least two predicted offsets of a first vehicle, a first traveling state of the first vehicle, and a second traveling state of a second vehicle; determining, according to the first traveling state and the second traveling state, first lane change payoffs of the predicted offsets when the second vehicle is in a yielding prediction state, and determining second lane change payoffs when the second vehicle is in a non-yielding prediction state; and determining a predicted yielding probability of the second vehicle, generating target lane change payoffs of the predicted offsets according to the predicted yielding probability and the first lane change payoffs and the second lane change payoffs of the predicted offsets, and determining a predicted offset having a maximum target lane change payoff as a target predicted offset.

A vehicle behavior prediction device includes a target vehicle detection unit configured to detect a target vehicle existing in a road region, a distance acquisition unit configured to acquire a front distance that is a distance between the target vehicle and an obstacle existing in front of the target vehicle, a turning radius estimation unit configured to estimate a turning radius of the target vehicle, and an entry prediction unit configured to predict whether or not the target vehicle is able to enter a host lane while avoiding an obstacle.

A method for lane departure assistance, the method may include obtaining sensed information about an environment of the first vehicle, by one of more vehicle sensors of a first vehicle; wherein the environment of the first vehicle comprises a passing lane and a current lane in which the first vehicle is positioned; determining, by a computer of the first vehicle, whether the first vehicle can successfully and lawfully bypass a second vehicle; wherein the determining is executed before starting the bypass of the second vehicle and is based on the sensed information, one or more driving laws, and one or more first vehicle mechanical parameters; generating a driver perceivable indicator that is indicative of the determining; sensing that the driver initiates a bypass of the second vehicle; evaluating, following the start of the bypass of the second vehicle and during at least a majority of the bypass, whether the first vehicle can successfully and lawfully complete the bypass of the second vehicle; and generating another driver perceivable indicator that is indicative of the evaluating.

While operating a vehicle, a candidate marker is detected via first image data from a first image sensor. Upon failing to identify the candidate marker, vehicle exterior lighting is actuated to illuminate the candidate marker. Then the candidate marker is determined to be one of a real marker or a projected marker based on determining whether the candidate marker is detected via second image data from the first image sensor. Upon determining the candidate marker is the real marker, the vehicle is operated based on the real marker.

Provided herein is a system and method of a vehicle that communicates an intended action of the vehicle. The system comprises one or more sensors; one or more processors; and a memory storing instructions that, when executed by the one or more processors, causes the system to perform capturing data from the one or more sensors of another vehicle or a road condition; determining an intended action of the vehicle based on the captured data; simulating the intended action of the vehicle on a map; communicating, within the vehicle, the intended action of the vehicle; and navigating the vehicle based on the intended action of the vehicle.