While operating a host vehicle in a lane, a target vehicle is detected entering the lane in front of the vehicle. A trajectory of the target vehicle is predicted based on sensor data. Upon determining that the target vehicle will pass through the lane based on the predicted trajectory, the host vehicle is operated based on determining a presence or an absence of a lead vehicle. Upon determining that the target vehicle will remain in the lane based on the predicted trajectory, the host vehicle is operated with the target vehicle as the lead vehicle.

While operating a host vehicle in a lane, a target vehicle is detected entering the lane in front of the vehicle. A trajectory of the target vehicle is predicted based on sensor data. Upon determining that the target vehicle will pass through the lane based on the predicted trajectory, the host vehicle is operated based on determining a presence or an absence of a lead vehicle. Upon determining that the target vehicle will remain in the lane based on the predicted trajectory, the host vehicle is operated with the target vehicle as the lead vehicle.

Methods and systems for determining risk associated with operation of autonomous vehicles using autonomous communication are provided. According to certain aspects, autonomous operation features associated with a vehicle may be determined, including features associated with autonomous communication between vehicles or with infrastructure. This information may be used to determine risk levels for a plurality of features, which may be based upon test data regarding the features or actual loss data. Expected use levels and autonomous communication levels may further be determined and used with the risk levels to determine a total risk level associated with operation of the vehicle. The autonomous communication levels may indicate the types of communications, the levels of communication with other vehicles or infrastructure, or the frequency of autonomous communication. The total risk level may be used to determine or adjust aspects of an insurance policy associated with the vehicle.

Methods and systems for determining risk associated with operation of autonomous vehicles using autonomous communication are provided. According to certain aspects, autonomous operation features associated with a vehicle may be determined, including features associated with autonomous communication between vehicles or with infrastructure. This information may be used to determine risk levels for a plurality of features, which may be based upon test data regarding the features or actual loss data. Expected use levels and autonomous communication levels may further be determined and used with the risk levels to determine a total risk level associated with operation of the vehicle. The autonomous communication levels may indicate the types of communications, the levels of communication with other vehicles or infrastructure, or the frequency of autonomous communication. The total risk level may be used to determine or adjust aspects of an insurance policy associated with the vehicle.

A cruise control device 10 includes a cutting-in/deviation determination unit 12 for performing cutting-in determination and deviation determination of another vehicle. The cutting-in/deviation determination unit 12 calculates a lateral position that is a position in a vehicle width direction of a forward vehicle 51 traveling ahead of an own vehicle 50, and determines the forward vehicle 51 traveling on an adjacent lane 64 to be a cutting-in vehicle into an own lane 63 and determines the forward vehicle 51 traveling on the own lane 63 to be a deviating vehicle from the own lane 63 on the basis of the calculated lateral position. The cutting-in/deviation determination unit 12 determines whether or not the own vehicle 50 is in a predetermined own vehicle turning state that is either one of a state before starting a turn or a state of turning, and determines permission of performing of cutting-in determination and deviation determination of the other vehicle on the basis of the determination result.

A cruise control device 10 includes a cutting-in/deviation determination unit 12 for performing cutting-in determination and deviation determination of another vehicle. The cutting-in/deviation determination unit 12 calculates a lateral position that is a position in a vehicle width direction of a forward vehicle 51 traveling ahead of an own vehicle 50, and determines the forward vehicle 51 traveling on an adjacent lane 64 to be a cutting-in vehicle into an own lane 63 and determines the forward vehicle 51 traveling on the own lane 63 to be a deviating vehicle from the own lane 63 on the basis of the calculated lateral position. The cutting-in/deviation determination unit 12 determines whether or not the own vehicle 50 is in a predetermined own vehicle turning state that is either one of a state before starting a turn or a state of turning, and determines permission of performing of cutting-in determination and deviation determination of the other vehicle on the basis of the determination result.

A driver assistance method for an ego vehicle (EGO) travelling in a traffic lane, includes: identifying traffic surrounding the ego vehicle in the same traffic lane as the ego vehicle and in adjacent parallel lanes travelling in the same direction; determining a virtual barycentric target, including calculating a position of the virtual barycentric target, a speed of the virtual barycentric target, and an acceleration of the virtual barycentric target; calculating a longitudinal speed setpoint of the ego vehicle, an acceleration setpoint, and a torque setpoint, the longitudinal speed setpoint being a function of the position of the virtual barycentric target, the speed of the virtual barycentric target, and the acceleration of the virtual barycentric target.

A driver assistance method for an ego vehicle (EGO) travelling in a traffic lane, includes: identifying traffic surrounding the ego vehicle in the same traffic lane as the ego vehicle and in adjacent parallel lanes travelling in the same direction; determining a virtual barycentric target, including calculating a position of the virtual barycentric target, a speed of the virtual barycentric target, and an acceleration of the virtual barycentric target; calculating a longitudinal speed setpoint of the ego vehicle, an acceleration setpoint, and a torque setpoint, the longitudinal speed setpoint being a function of the position of the virtual barycentric target, the speed of the virtual barycentric target, and the acceleration of the virtual barycentric target.

A vehicle configured to operate in an autonomous mode can obtain sensor data from one or more sensors observing one or more aspects of an environment of the vehicle. At least one aspect of the environment of the vehicle that is not observed by the one or more sensors could be inferred based on the sensor data. The vehicle could be controlled in the autonomous mode based on the at least one inferred aspect of the environment of the vehicle.

A vehicle configured to operate in an autonomous mode can obtain sensor data from one or more sensors observing one or more aspects of an environment of the vehicle. At least one aspect of the environment of the vehicle that is not observed by the one or more sensors could be inferred based on the sensor data. The vehicle could be controlled in the autonomous mode based on the at least one inferred aspect of the environment of the vehicle.