A driving support ECU initializes a target trajectory calculation parameter at a start of LCA; calculates, based on the target trajectory calculation parameter, a target trajectory function representing a target lateral position which is a target position of an own vehicle in a lane width direction in accordance with an elapsed time t from the start of LCA; successively calculates a target lateral movement state amount of the own vehicle based on the target trajectory function and the elapsed time t; calculates a target yaw state amount of the own vehicle based on the target lateral movement state amount and a vehicle speed; and calculates a target control amount based on the target yaw state amount and the target lateral position.

Techniques are discussed for generating and optimizing a trajectory using closed-form numerical integration in route-relative coordinates. A decision planner component of an autonomous vehicle, for example, can receive or generate a reference trajectory, which may correspond to an ideal route for an autonomous vehicle to traverse through an environment, such as a center of a road segment. Lateral dynamics (e.g., steering angles, curvature values of trajectory segments) and longitudinal dynamics (e.g., velocity and acceleration) can be represented in a single algorithm such that optimizing the reference trajectory (e.g., based on loss functions or costs) can substantially simultaneously optimize the lateral dynamics and longitudinal dynamics in a single convergence operation. In some cases, the trajectory can be used to control the autonomous vehicle to traverse an environment.

A system for controlling an autonomous vehicle includes a memory configured to store parameters of a g-g plot defining admissible space of values of longitudinal and lateral accelerations. The g-g plot parameters define a mapping between user driving preferences and constrained control of the autonomous vehicle. The g-g plot parameters include a maximum forward acceleration, a maximum backward acceleration, a maximum lateral acceleration and a shape parameter defining profile of curves connecting maximum values of forward, backward, and lateral accelerations. The system accepts a comfort level as a feedback from a passenger of the vehicle, determines a dominant parameter corresponding to the feedback, updates the dominant parameter of the g-g plot based on the comfort level indicated in the feedback, and controls the vehicle to maintain dynamics of the vehicle within the admissible space defined by the parameters of the updated g-g plot.

A method for controlling a vehicle based on sensor data having variable sensor parameter settings includes receiving sensor data generated by a vehicle sensor while the sensor is configured with a first sensor parameter setting. The method also includes receiving an indicator specifying the first sensor parameter setting, and selecting, based on the received indicator, one of a plurality of neural networks of a perception component, each neural network having been trained using training data corresponding to a different sensor parameter setting. The method also includes generating signals descriptive of a current state of the environment using the selected neural network and based on the received sensor data. The method further includes generating driving decisions based on the signals descriptive of the current state of the environment, and causing one or more operational subsystems of the vehicle to maneuver the vehicle in accordance with the generated driving decisions.

A vehicle travel control device executes vehicle travel control such that a vehicle follows a target trajectory. An automated driving control device generates a first target trajectory that is the target trajectory for automated driving of the vehicle. The vehicle travel control device further determines whether or not an activation condition of travel assist control is satisfied. When the activation condition is satisfied, the vehicle travel control device generates a second target trajectory that is the target trajectory for the travel assist control. When the second target trajectory is generated during the automated driving, the vehicle travel control device determines whether or not a cancellation condition is satisfied. When the cancellation condition is satisfied, the vehicle travel control device cancels both the first target trajectory and the second target trajectory, and decelerates the vehicle.

System and techniques for passenger discomfort measurement during vehicle maneuver are described herein. A set of biomechanical measurements of a passenger in a vehicle may be obtained. A subset from the set of biomechanical measurements with members that correspond to distress in the passenger are selected and a search of vehicle actions performed that correspond to the subset of biomechanical measurements in time. Then, modification to future application of the vehicle action is made to reduce distress in passengers.

Aspects of the disclosure relate to controlling a first vehicle in an autonomous driving mode. While doing so, a second vehicle may be identified. This vehicle may be determined to be a tailgating vehicle. An initial allowable discomfort value representing expected discomfort of an occupant of the first vehicle and expected discomfort of an occupant of the second vehicle may be identified. Determining a speed profile for a future trajectory of the first vehicle that meets the value may be attempted based on a set of factors corresponding to a reaction of the tailgating vehicle. When a speed profile that meets the value cannot be determined, the value may be adjusted until a speed profile that meets the value is determined. The speed profile that meets an adjusted value is used to control the first vehicle in the autonomous driving mode.

A number of illustrative variations may include a method or product for alerting or refocusing an inattentive driver.

A system and method for providing accurate trajectory following for automated vehicles in dynamic environments that include receiving image data and LiDAR data associated with a dynamic environment of a vehicle. The system and method also include processing a planned trajectory of the vehicle that is based on an analysis of the image data and LiDAR data. The system and method further include communicating control signals associated with following the planned trajectory to autonomously control the vehicle to follow the planned trajectory to navigate within the dynamic environment to reach a goal.

A vehicle control system includes: a first switch that accepts an operation of an occupant of a vehicle; and a performance part that performs, when the first switch is operated, travel assist in a node in which an assist degree is the largest at that time point.