Transportation systems have artificial intelligence including neural networks for recognition and classification of objects and behavior including natural language processing and computer vision systems. The transportation systems involve sets of complex chemical processes, mechanical systems, and interactions with behaviors of operators. System-level interactions and behaviors are classified, predicted and optimized using neural networks and other artificial intelligence systems through selective deployment, as well as hybrids and combinations of the artificial intelligence systems, neural networks, expert systems, cognitive systems, genetic algorithms and deep learning.

The techniques of this disclosure relate to a vehicle occupancy-monitoring system. The system includes a controller circuit that receives occupant data from an occupancy-monitoring sensor of a vehicle. The controller circuit determines an occupancy status of respective seats in a cabin of the vehicle based on the occupancy-monitoring sensor. The controller circuit indicates the occupancy status of the respective seats on a display located in a field of view of occupants of the vehicle. The display is integral to one of a roof light module, a door panel, or a headliner of the cabin. The system can improve passenger safety by alerting the operator and other occupants about the occupancy status of the passengers.

A control system, computer-readable storage medium and method of preventing occlusion of and minimizing shadows on the driver's face for driver monitoring. The system includes a steering wheel, a plurality of fiberscopes arranged evenly spaced around the steering wheel, and one or more video cameras arranged at remote ends of the plurality of fiberscopes. Distal ends of the fiberscopes emerge to a surface of the steering wheel through holes that are perpendicular to an axis of rotation of the steering wheel. Each of the distal ends of the fiberscopes includes a lens. The system includes a plurality of light sources and an electronic control unit connected to the one or more video cameras and the light sources.

An apparatus for controlling autonomous parking and method thereof are provided. The apparatus for controlling autonomous parking according to an exemplary embodiment of the present disclosure a sensor unit for acquiring information around a host vehicle, a storage for storing driver's tendency information upon autonomous parking, and a controller communicatively connected to the sensor unit. Here, the controller is configured to: search for an available parking space based on the acquired information around the vehicle, determine whether the available parking space is next to a pillar, determine whether a passenger exists on the side of the pillar when parking in the available parking space, set a parking target position based on the driver's tendency information when the passenger gets off and set a parking longitudinal offset so that a door and a side mirror of the vehicle do not interfere with the pillar, and perform autonomous parking by varying the parking target position according to the offset set above.

A method of visualizing output data of a traffic planning method for controlling a plurality of vehicles (v1, v2, . . . ), wherein each vehicle occupies one node in a shared set of planning nodes (wp1, wp2, . . . ) and is movable to other nodes along predefined edges between pairs of the nodes, wherein the output data indicates respective planned node occupancies of the vehicles for a sequence of time steps, the method comprising: obtaining a graphical representation of the planning nodes; and in the graphical representation, indicating each vehicle's planned movements between the nodes by linear graphical elements, wherein an appearance parameter of the linear graphical elements has a time variation with respect to the time step, which time variation is common to all vehicles. The appearance parameter may be a line width, line style, color or texture.

A drive device for a PTO shaft includes a parking switch to detect a parking state of a vehicle body, a permission switch constituted of a self-returning switch and connected to the parking switch, the permission switch being configured to be switched to permit driving a PTO shaft, the PTO shaft being configured to be driven by a power of a prime mover disposed on the vehicle body, and a first switch device to be switched to enable the PTO shaft to be driven when the permission switch is switched to permit driving the PTO shaft under a state where the parking switch detects the parking state of the vehicle body.

A motor vehicle includes a body defining a passenger compartment and having opposing driver and passenger sides. The vehicle includes driver and passenger side mirrors. The driver side mirror has a sweep angle (α) and an elevation angle (γ). The passenger side mirror has a sweep angle (β). The side mirrors are separated from each other by a distance (D). An adjustable driver seat has a height (H). An electronic controller, in response to position signals inclusive of angles (α), (β), and (γ), the distance (D), and the height (H), calculates a three-dimensional (3D) driver head position of a driver of the vehicle, and thereafter uses the 3D driver head position to improve performance of a driver assist system device. Functions of the controller may be implemented as a method or recorded on a computer readable medium for execution by a processor.

An example operation includes one or more of determining a portion of memory in a transport to store data, establishing a timeframe when the data may be accessed based on a type of the data, and clearing the data from the portion of memory after the timeframe. The type of the data may be related to one or more of the transport and an occupant of the transport. A length of the timeframe is inversely proportional to a criticality of the type of the data.

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.

Transportation systems have artificial intelligence including neural networks for recognition and classification of objects and behavior including natural language processing and computer vision systems. The transportation systems involve sets of complex chemical processes, mechanical systems, and interactions with behaviors of operators. System-level interactions and behaviors are classified, predicted and optimized using neural networks and other artificial intelligence systems through selective deployment, as well as hybrids and combinations of the artificial intelligence systems, neural networks, expert systems, cognitive systems, genetic algorithms and deep learning.