Systems and methods for personalizing adaptive cruise control in a vehicle are disclosed herein. One embodiment collects vehicle-following-behavior data associated with a particular driver; trains a Gaussian Process (GP) Regression model using the collected vehicle-following-behavior data to produce a set of adaptive-cruise-control (ACC) parameters pertaining to the particular driver, the set of ACC parameters modeling learned vehicle-following behavior of the particular driver; generates an acceleration command for the vehicle based, at least in part, on the set of ACC parameters; applies a predictive safety filter to the acceleration command to produce a certified acceleration command that has been vetted for safety; and controls acceleration of the vehicle automatically in accordance with the certified acceleration command to regulate a following distance between a lead vehicle and the vehicle in accordance with the learned vehicle-following behavior of the particular driver.

Systems and methods for personalizing adaptive cruise control in a vehicle are disclosed herein. One embodiment collects vehicle-following-behavior data associated with a particular driver; trains a Gaussian Process (GP) Regression model using the collected vehicle-following-behavior data to produce a set of adaptive-cruise-control (ACC) parameters pertaining to the particular driver, the set of ACC parameters modeling learned vehicle-following behavior of the particular driver; generates an acceleration command for the vehicle based, at least in part, on the set of ACC parameters; applies a predictive safety filter to the acceleration command to produce a certified acceleration command that has been vetted for safety; and controls acceleration of the vehicle automatically in accordance with the certified acceleration command to regulate a following distance between a lead vehicle and the vehicle in accordance with the learned vehicle-following behavior of the particular driver.

A method of operating an autonomous vehicle includes determining, by the autonomous vehicle, whether a target is in an intended maneuver zone around the autonomous vehicle; generating, by the autonomous vehicle, a signal in response to determining that the target is within the intended maneuver zone around the autonomous vehicle; determining, by the autonomous vehicle and based on perception information acquired by the autonomous vehicle, whether the target has left the intended maneuver zone around the autonomous vehicle; and determining, by the autonomous vehicle, that it is safe to perform the intended maneuver in response to determining, by the autonomous vehicle, that the target is not in the intended maneuver zone or in response to determining, by the autonomous vehicle, that the target has left the intended maneuver zone.

A method of operating an autonomous vehicle includes determining, by the autonomous vehicle, whether a target is in an intended maneuver zone around the autonomous vehicle; generating, by the autonomous vehicle, a signal in response to determining that the target is within the intended maneuver zone around the autonomous vehicle; determining, by the autonomous vehicle and based on perception information acquired by the autonomous vehicle, whether the target has left the intended maneuver zone around the autonomous vehicle; and determining, by the autonomous vehicle, that it is safe to perform the intended maneuver in response to determining, by the autonomous vehicle, that the target is not in the intended maneuver zone or in response to determining, by the autonomous vehicle, that the target has left the intended maneuver zone.

This application discloses a method and an apparatus for detecting a complexity of a traveling scenario of a vehicle, comprising: obtaining a travelling speed of the vehicle and a travelling speed of a target vehicle; determining, based on the traveling speed of the vehicle and the traveling speed of the target vehicle, a dynamic complexity of a traveling scenario in which the vehicle is located; determining static information of each static factor in the traveling scenario in which the vehicle is currently located; obtaining, based on the static information of each static factor, a static complexity of the traveling scenario in which the vehicle is located; and obtaining, based on the dynamic complexity and the static complexity, a comprehensive complexity of the traveling scenario in which the vehicle is located.

This application discloses a method and an apparatus for detecting a complexity of a traveling scenario of a vehicle, comprising: obtaining a travelling speed of the vehicle and a travelling speed of a target vehicle; determining, based on the traveling speed of the vehicle and the traveling speed of the target vehicle, a dynamic complexity of a traveling scenario in which the vehicle is located; determining static information of each static factor in the traveling scenario in which the vehicle is currently located; obtaining, based on the static information of each static factor, a static complexity of the traveling scenario in which the vehicle is located; and obtaining, based on the dynamic complexity and the static complexity, a comprehensive complexity of the traveling scenario in which the vehicle is located.

An adaptive cruise control system includes an information acquisition unit having a main detector and a secondary detector, a control unit, and an execution unit. The main detector detects an object located ahead of the vehicle. The control unit determines whether control of the vehicle is required depending on an actual value determined by the main detector and the threshold value of a system property characterizing the driving environment. The execution unit controls the vehicle. The secondary detector is arranged such that its field of view for detecting an object located at an angle ahead of the vehicle covers the boundary of the main detector's field of view, and which is oriented outwards in relation to the forward direction of the vehicle. The secondary detector sends an indication signal to adjust the threshold value of the system property when an object is detected.

A hybrid deterministic override to cloud based probabilistic advanced driver assistance systems. Under default driving conditions, an ego vehicle is controlled by a probabilistic controller in a cloud. An overall gap between the ego vehicle and a leading vehicle is divided into an emergency collision gap and a driver specified gap. The vehicle sensors monitor the overall gap. When the gap between the ego vehicle and the leading vehicle is less than or equal to the emergency collision gap, a deterministic controller of the ego vehicle overrides the cloud based probabilistic controller to control the braking and acceleration of the ego vehicle.

A hybrid deterministic override to cloud based probabilistic advanced driver assistance systems. Under default driving conditions, an ego vehicle is controlled by a probabilistic controller in a cloud. An overall gap between the ego vehicle and a leading vehicle is divided into an emergency collision gap and a driver specified gap. The vehicle sensors monitor the overall gap. When the gap between the ego vehicle and the leading vehicle is less than or equal to the emergency collision gap, a deterministic controller of the ego vehicle overrides the cloud based probabilistic controller to control the braking and acceleration of the ego vehicle.

In one embodiment, when an autonomous driving vehicle (ADV) is parked, the ADV can determine, based on criteria, whether to operate in an open-space mode or an on-lane mode. The criteria can include whether the ADV is within a threshold distance and threshold heading relative to a vehicle lane. If the criteria are not satisfied, then the ADV can enter the open-space mode. While in the open-space mode, the ADV can maneuver it is within the threshold distance and the threshold heading relative to the vehicle lane. In response to the criteria being satisfied, the ADV can enter and operate in the on-lane mode for the ADV to resume along the vehicle lane.