Systems and methods for adapting various autonomous vehicle settings to protect personal information based on the composition of passengers in a vehicle. In particular, sensitive user information is intelligently parsed out or generalized based on user preference and the presence of unknown passengers. In some examples, sensitive user information is routed to more secure channels such as a mobile device application or a personalized display. In some implementations, the position of passengers within a vehicle is determined to identify which displays are in each passenger's field of view. Additionally, pick-up and drop-off locations can be scrambled to nearby destinations to obscure a home address, work address, or other identifiable address information.

A vehicle includes: a traveling driving system configured to execute traveling of an own vehicle; a non-traveling-application providing unit configured to provide a passenger with an application other than the traveling of the own vehicle; and a control unit. The control unit includes: inquiry means for inquiring a usage application of the own vehicle to the passenger when it is detected that the passenger gets into the own vehicle, the usage application including a traveling application and the application other than traveling of the own vehicle; non-traveling-application providing unit activating means for activating the non-traveling-application providing unit in a case where the application other than traveling is selected in response to the inquiry by the inquiry means; and traveling driving system activating means for activating the traveling driving system in a case where the traveling application is selected in response to the inquiry by the inquiry means.

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.

Methods, systems, and non-transitory computer-readable media are configured to perform operations comprising determining a lead obstacle in front of an ego vehicle; determining an optimizable function associated with an optimized preferred following distance between the ego vehicle and the lead obstacle; and generating the optimized preferred following distance based on the optimizable function.

A controller and a method for controlling motion of a vehicle is provided. The method comprises acquiring motion information including a current state of the vehicle and a desired state of the vehicle, determining a combination of a steering angle of the wheels and motor forces for moving the vehicle from the current state into the desired state by using a first model of the motion of the vehicle and a second model of the motion of the chassis of the vehicle, determining a cost function of the motion of the vehicle, optimizing the cost function of the motion of the vehicle to compute a command signal for controlling the steering wheel and the plurality of electric motors, and controlling the steering angle of the wheels and the motor forces based on the command signal.

Among other things, techniques are described for notifying and explaining the action performed by an autonomous vehicle, including but not limited to: receiving a planned path of a vehicle, a state of the vehicle and environment data of an environment in which the vehicle is operating, receiving a deviation signal, determining whether the deviation signal was reported by a first system or a second system of the vehicle, in response selecting a first set of simulators or a second set of simulators for simulating the vehicle in the environment, simulating the vehicle in the environment using the selected first or second set of simulators, based on results of the simulating, generating a message and presenting the message to at least one occupant of the vehicle.

This application discloses an intelligent vehicle control method. An intelligent vehicle control system obtains a driving mode, a driving style model, and a target speed of an intelligent vehicle at a current moment, then determines a speed control instruction based on the driving mode and the driving style model, and sends the speed control instruction to an execution system of the intelligent vehicle. This provides an intelligent vehicle control method with high comfort and good experience.

Embodiments of the invention are intended to evaluate the performance of a planning module of the ADV in terms of decision consistency in addition to other metrics, such as comfort, latency, controllability, and safety. In one embodiment, an exemplary method includes receiving, at an autonomous driving simulation platform, a record file recorded by the ADV that was automatically driving on a road segment; simulating operations of a dynamic model of the ADV in the autonomous driving simulation platform during one or more driving scenarios on the road segment based on the record file. The method further includes performing a comparison between each planned trajectory generated by a planning module of the dynamic model after an initial period of time with each trajectory stored in a buffer; and modifying a performance score generated by a planning performance profiler in the autonomous driving simulation platform based on a result of the comparison.

Enclosed are embodiments for navigation with drivable area detection. In an embodiment, a method comprises: receiving a point cloud from a depth sensor, receiving image data from a camera; predicting at least one label indicating a drivable area by applying machine learning to the image data; labeling the point cloud using the at least one label; obtaining odometry information; generating a drivable area by registering the labeled point cloud and odometry information to a reference coordinate system; and controlling the vehicle to drive within the drivable area.

A method for training a style encoder of a neural network. Sensory input variables, which represent a movement of a system and surroundings of the system, are compressed to an abstract driving situation representation in at least one portion of a latent space of the neural network, using a trained situation encoder of the neural network. The sensory input variables are compressed to a driving style representation in at least one portion of the latent space, using the untrained style encoder. The driving style representation and the driving situation representation are decompressed from the latent space to output variables, using a style decoder of the neural network. A structure of the style encoder is changed to train the style encoder until the output variables of the style decoder represent the movement.