Abstract: A computer-implemented method that, when executed by data processing hardware, causes the data processing hardware to perform operations comprising detecting a request to enable an automated driving system of a vehicle while the vehicle is at rest or at a low speed, determining an intended path of the vehicle, determining an initial steering angle, calculating a desired steering angle based on the intended path, calculating a commanded steering angle, evaluating the initial steering angle versus the desired steering angle, and either (i) initiating a driving maneuver with the initial steering angle, or (ii) adjusting the steering angle based on the commanded steering angle before initiating the driving maneuver.

Abstract: A computer-implemented method that, when executed by data processing hardware, causes the data processing hardware to perform operations comprising, determining vehicle pose data of a host vehicle, maintaining an odometry buffer with the vehicle pose data, detecting a steering maneuver of the host vehicle, determining a relative pose of the host vehicle upon initiation of the steering maneuver using the odometry buffer, formulating an odometry path for aligning a heading of the host vehicle with a heading of the relative pose, and completing the steering maneuver according to the odometry path.

Abstract: Vehicles and related systems and methods are provided for predicting a future maneuver expected to be executed by a driver. One method involves determining a combined probability for a potential maneuver based on constituent probabilities corresponding to different vehicle state variables, and environmental conditions using one or more tuning parameters specific to the respective combination of maneuver and vehicle state variable, determining an adjusted probability for the potential maneuver based at least in part on the combined probability and one or more contextual state variables corresponding to a current operating context for the vehicle, adapted to driver behavior and driving style, identifying the potential maneuver as an expected maneuver to be executed by a driver of the vehicle based on the adjusted probability, and automatically initiating one or more actions in a manner that is influenced by the expected maneuver.

Abstract: A turn signal indication system includes: at least one turn signal indicator; an adaptive maneuver prediction module configured to predict that a host vehicle is to make a turn or change lanes; an activation decision module configured to, based on the prediction, determine whether the host vehicle is to make a left turn, make a right turn, change to a left lane, or change to a right lane; and a signal indication module configured to automatically activate the at least one turn signal indicator based on the determination of whether the host vehicle is to make a left turn, make a right turn, change to a left lane, or change to a right lane.

Abstract: Methods and systems are provided that include one or more sensors configured to obtain sensor data pertaining to both a driver of a vehicle and an environment surrounding the vehicle; and a processor that is coupled to the one or more sensors and that is configured to at least facilitate determining, using the sensor data, when the driver is incapacitated; determining, using the sensor data, when a threat is detected for the vehicle; and taking action to avoid the threat when the driver is incapacitated and the threat is detected, via instructions provided by the processor.

Abstract: A vehicle system includes a control module configured to receive data from a sensor indicative of a torque applied to the steering wheel, calculate a torque rate based on the applied torque, calculate a left evasive steering probability of the driver intending to steer the vehicle to the left and a right evasive steering probability of the driver intending to steer the vehicle to the right based on the torque, the torque rate, and a modeled relationship between the torque and the torque rate, compare the left evasive steering probability and the right evasive steering probability to determine a directional steering intent of the driver, and transmit a signal to a threat avoidance steering control module for controlling the vehicle. Other example vehicle systems and methods for predicting driver intent in evasive steering maneuvers are also disclosed.

Abstract: Vehicles and related systems and methods are provided for predicting a future maneuver expected to be executed by a driver. One method involves determining a combined probability for a potential maneuver based on constituent probabilities corresponding to different vehicle state variables, and environmental conditions using one or more tuning parameters specific to the respective combination of maneuver and vehicle state variable, determining an adjusted probability for the potential maneuver based at least in part on the combined probability and one or more contextual state variables corresponding to a current operating context for the vehicle, adapted to driver behavior and driving style, identifying the potential maneuver as an expected maneuver to be executed by a driver of the vehicle based on the adjusted probability, and automatically initiating one or more actions in a manner that is influenced by the expected maneuver.

Abstract: Methods and systems are provided that include one or more sensors configured to obtain sensor data pertaining to both a driver of a vehicle and an environment surrounding the vehicle; and a processor that is coupled to the one or more sensors and that is configured to at least facilitate determining, using the sensor data, when the driver is incapacitated; determining, using the sensor data, when a threat is detected for the vehicle; and taking action to avoid the threat when the driver is incapacitated and the threat is detected, via instructions provided by the processor.

Abstract: A method for probabilistic autonomous vehicle control includes receiving a plurality of unfiltered sensor signals from a sensor system of a vehicle and determining, in real time, a failure probability of the sensor system using the unfiltered sensor signals. The method further includes determining a failure probability of a plurality of estimation signals at each time step using the failure probability of the sensor system and determining a failure probability of a plurality of Advanced Driver Assistance System (ADAS) subfunction commands using the failure probability of the plurality of estimation signals at each time step. Further, the method includes determining remedial actions for the plurality of ADAS subfunction commands based on the failure probability of the plurality of ADAS subfunction commands.

Abstract: A system includes a computing device. The computing device includes a processor and a memory, the memory including instructions such that the processor is configured to: transform vehicle dynamics data to corresponding roadway geometry data, calculate a rationalization value based on a difference between the roadway geometry data and map-based roadway geometry data, and determine whether to modify a vehicle state based on the rationalization value.

Abstract: Systems and methods for controlling a vehicle. The systems and methods receive static object detection data from a perception system. The static object detection data includes a first representation of a static object at a current time and a second representation of the static object at an earlier time.

Abstract: A system includes a computing device. The computing device includes a processor and a memory, the memory including instructions such that the processor is configured to: transform vehicle dynamics data to corresponding roadway geometry data, calculate a rationalization value based on a difference between the roadway geometry data and map-based roadway geometry data, and determine whether to modify a vehicle state based on the rationalization value.