A method for controlling operation of a motor vehicle includes an electronic controller receiving, e.g., from a vehicle-mounted sensor array, sensor data with dynamics information for a target vehicle and, using the received sensor data, predicting a lane assignment for the target vehicle on a road segment proximate the host vehicle. The electronic controller also receives map data with roadway information for the road segment; the controller fuses the sensor and map data to construct a polynomial overlay for a host lane of the road segment across which travels the host vehicle. A piecewise linearized road map of the host lane is constructed and combined with the predicted lane assignment and polynomial overlay to calculate a lane assignment for the target vehicle. The controller then transmits one or more command signals to a resident vehicle system to execute one or more control operations using the target vehicle's calculated lane assignment.

A method for providing a positive decision signal for a vehicle which is about to perform a traffic scenario action. The method includes receiving information about at least one surrounding road user, which information is indicative of distance to the surrounding road user with respect to the vehicle and at least one of speed and acceleration of the surrounding road user; calculating a value based on the received information; providing the positive decision signal to perform the traffic scenario action when the calculated value is fulfilling a predetermined condition. The value is calculated based on an assumption that the surrounding road user will react on the traffic scenario action by changing its acceleration.

A method for determining a risk boundary in response to the plurality of indications of hard braking events wherein the risk boundary is indicative of a plurality of speed flow pairs at which a risk of a hard braking event is below a threshold value, determining, at a road segment level, a set of speed flow pairs of average speed and vehicle count and a plurality of indications of hard braking events , determining a host vehicle speed, and performing at least one of reducing the host vehicle speed and increasing a host vehicle following distance in response to the host vehicle speed exceeding the risk boundary for the vehicle flow density.

An apparatus, including: an interface configured to receive raw images of one or more objects across a timeseries of frames corresponding to a movement event from a perspective of a vehicle of interest (Vol); and processing circuitry that is configured to: track a change in intensity or direction information represented in motion vectors (MVs) generated based on the raw images; generate, based on the change in the intensity or direction information, a weight of an influence vector representing a Vol influence on the movement event; and transmit the weight of the influence vector and an identity of the movement event to an assessment system that is configured to utilize the weight of the influence vector in an assessment of the Vol.

A dynamic platoon formation method under a mixed autonomous vehicles flow is provided. The method implements dynamic platooning by taking into account a fact that a traffic flow is a mixture of HDVs and CAVs. The dynamic platoon formation method includes: selecting lanes as candidate lanes in turn; constructing a decision tree from a current moment to a moment of platoon formation according to the following process: constructing a decision space for each CAV, generating a compatible decision set, selecting and executing a compatible decision, and updating location and speed information of all vehicles; and selecting, according to a predetermined index (including TTP and DTP), an optimal decision sequence as a decision sequence corresponding to the candidate lane.

An unmanned device acquires sensing data of surrounding obstacles; determines, for each obstacle, at least one predicted track of the obstacle in a future period of time based on the sensing data; determines, for each moment in the future period of time and according to the predicted track corresponding to the obstacle, a collision probability that a collision with the obstacle occurs at each position in a target region at the moment; and determines a global collision probability that the collision with the obstacle occurs in the entire target region at the moment. According to the global collision probability corresponding to each obstacle at each moment, the unmanned device controls the unmanned device in the future period of time.

An apparatus for controlling an autonomous vehicle disclosure may include a processor and a memory configured to be operatively connected to the processor and to store at least one code performed in the processor, wherein the memory may store a code that, when executed by the processor, causes the processor to control the autonomous vehicle to travel on the basis of a distance from a preceding vehicle in a travel lane in which the autonomous vehicle travels or a preset speed, determine a risk level of a lane change on the basis of a speed of the autonomous vehicle, a speed of a side vehicle traveling in a target lane of a lane change, and a distance between the autonomous vehicle and the side vehicle upon occurrence of a lane change request, and perform longitudinal or lateral control for the lane change on the basis of the risk level.

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, that determine yield behavior for an autonomous vehicle. An agent that is in a vicinity of an autonomous vehicle can be identified. An obtained crossing intent prediction characterizes a predicted likelihood that the agent intends to cross a roadway during a future time period. First features of the agent and of the autonomous vehicle are obtained. An input that includes the first features and the crossing intent prediction is processed using a machine learning model to generate an intent yielding score that represents a likelihood that the autonomous vehicle should perform a yielding behavior due to the intent of the agent to cross the roadway. From at least the intent yielding score, an intent yield behavior signal is determined and indicates whether the autonomous vehicle should perform the yielding behavior prior to reaching the first crossing region.

Provided is a system and method that can control a merge of an autonomous vehicle when other vehicles are present on the road. In one example, the method may include iteratively estimating a series of values associated with one or more vehicles in an adjacent lane with respect to an ego vehicle, identifying a trend associated with the one or more vehicles from the iteratively estimated series of values, determining merge intentions of the one or more vehicles with respect to the ego vehicle based on the identified trend over time, verifying the merge intentions against a simulated change in the trend, selecting a merge position of the ego vehicle with respect to the one or more vehicles within the lane based on the verified merge intentions, and executing an instruction to cause the ego vehicle to perform a merge operation based on the selected merge position.

Example methods and systems for controlling speeds of a vehicle may generally determine a target vehicle acceleration using an autonomy control module of the vehicle. The target vehicle acceleration may be determined based upon at least one of a target vehicle following distance, a target vehicle following speed, or a target vehicle speed. The determined vehicle acceleration may be mapped to a level of vehicle torque using a vehicle dynamics module of the vehicle. Additionally, the level of vehicle torque may be applied to one or more wheels of the vehicle.