A control unit for controlling a driving function of a vehicle is designed to automatically guide the vehicle longitudinally and/or transversely. The control unit is designed to determine that the driver of the vehicle is presently activating or deactivating, and/or intends to activate or deactivate, the driving function. In response thereto, the control unit is additionally designed to cause a manual control intervention produced by the driver of the vehicle in the longitudinal and/or transversal guidance of the vehicle to be at least partly compensated for and/or suppressed prior to the point in time of the activation or deactivation of the driving function in order to adapt the drive behavior of the vehicle during the transition between the manual longitudinal and/or transversal guidance and the automatic longitudinal and/or transversal guidance.

A method for deactivating an automated driving mode of a vehicle involves automatically terminating the automated driving mode if, due to a manual steering torque acting on the steering wheel of the vehicle, a system steering torque generated by a control system of an assistance system for automated driving mode in terms of amount is exceeded by a predetermined first value if at least one hand of a vehicle user is detected on the steering wheel of the vehicle, is exceeded by a predetermined second value if neither of the vehicle user's hands is detected on the steering wheel, or is exceeded by a predetermined third value if it is determined that the vehicle user is distracted from the driving situation, or if it is determined that there is a lateral collision risk for the vehicle and the manual steering torque of the vehicle user is acting in the direction of the collision risk.

A vehicle collects data of a plurality of to-be-detected barrier gate crossbars around the vehicle by using a sensor mounted on the vehicle, and transmits the data of the plurality of to-be-detected barrier gate crossbars to a processor; the processor determines data of a target barrier gate crossbar from the data of the plurality of to-be-detected barrier gate crossbars based on a pose of the target barrier gate crossbar, where the target barrier gate crossbar is a barrier gate crossbar of a lane on which the vehicle is located; and the processor determines a status of the target barrier gate crossbar based on the data of the target barrier gate crossbar, and controls, based on the status of the target barrier gate crossbar, the autonomous driving vehicle to pass through the target barrier gate crossbar.

Connected and automated vehicles (CAVs) have shown the potential to improve safety, increase road throughput, and optimize energy efficiency and emissions in several complicated traffic scenarios. This invention describes a mixed-integer programming (MIP) optimization method for global multi-vehicle decision making and motion planning of CAVs in a highly dynamic environment that consists of multiple human-driven, i.e., conventional or manual, vehicles and multiple conflict zones, such as merging points and intersections. The proposed approach ensures safety, high throughput and energy efficiency by solving a global multi-vehicle constrained optimization problem. The solution provides a feasible and optimal time schedule through road segments and conflict zones for the automated vehicles, by using information from the position, velocity, and destination of the manual vehicles, which cannot be directly controlled. Despite MIP having combinatorial complexity, the proposed formulation remains feasible for real-time implementation in the infrastructure, such as in mobile edge computers (MECs).

According to various embodiments, systems, methods, and media for evaluating an open space planner in an autonomous vehicle are disclosed. In one embodiment, an exemplary method includes receiving, at a profiling application, a record file recorded by the ADV while driving in an open space using the open space planner, and a configuration file specifying parameters of the ADV; extracting planning messages and prediction messages from the record file, each extracted message being associated with the open space planner. The method further includes generating features from the planning message and the prediction messages in view of the specified parameters of the ADV; and calculating statistical metrics from the features. The statistical metrics are then provided to an automatic tuning framework for tuning the open space planner.

According to various embodiments, systems, methods, and media for evaluating an open space planner in an autonomous vehicle are disclosed. In one embodiment, an exemplary method includes receiving, at a profiling application, a record file recorded by the ADV while driving in an open space using the open space planner, and a configuration file specifying parameters of the ADV; extracting planning messages and prediction messages from the record file, each extracted message being associated with the open space planner. The method further includes generating features from the planning message and the prediction messages in view of the specified parameters of the ADV; and calculating statistical metrics from the features. The statistical metrics are then provided to an automatic tuning framework for tuning the open space planner.

A method includes receiving first data from one or more vehicles in a geographic area, the first data indicating operation of an active safety system by at least one of the one or more vehicles, receiving second data indicating driving performance of the one or more vehicles, determining whether the driving performance of the one or more vehicles in the geographic area is improved or diminished by the use of the active safety system based on the first data and the second data, and transmitting a signal to a vehicle approaching the geographic area to cause the vehicle approaching the geographic area to activate or deactivate the active safety system based on the determination.

Systems and methods for an On-Demand Autonomy (ODA) service. The system includes a set of functional modules enabled for ODA service activities disposed in a follower vehicle (Fv) in communication with an ODA server that includes a user request module configured to receive request information from the Fv from the ODA server, and to process and communicate the request information to the schedule module; the schedule module configured to coordinate an arrival time information with the ODA server for pickup of the Fv based on the request information, and communicate the arrival time information to the schedule execution module; the schedule execution module configured to direct the Fv to a pickup point based on the arrival time information, and communicate the pickup point to the indication module; and the indication module configured to provide alerts to vehicles in the vicinity of the pickup of the Fv via the ODA service.

One or more outputs from a planning module of an ADV are received. Data of a driving environment of the ADV is received. A performance of the planning module is evaluated by determining a score of the performance of the planning module based on the data of the driving environment and the one or more outputs from the planning module. Whether the one or more outputs from the planning module violates at least one of a set of safety rules is determined. The score is determined being larger than a predetermined threshold in response to determining that the one or more outputs from the planning module violate at least one of the set of safety rules. Otherwise, the score is determined based on a machine learning model. The planning module is modified by tuning a set of parameters of the planning module based on the score.

A system and method of detecting and mitigating an erratic vehicle by a host vehicle. The method includes gathering sensor information on a calibratable external region surrounding the host vehicle; analyzing the sensor information to detect a target vehicle traveling in a lane and a movement of the target vehicle in the lane; determining whether the movement of the target vehicle in the lane is erratic; if erratic then designating target vehicle as erratic vehicle; assigning a risk score to the erratic vehicle; and implementing a predetermined mitigating action correlating to the assigned risk score to the erratic vehicle. The mitigating action includes one or more of: warning an operator of the host vehicle, warning a vehicle proximal to the host vehicle, and taking at least partial control of the host vehicle to further distance the host vehicle apart from the erratic vehicle.