A control apparatus for an automatic-driving vehicle includes a fall detecting unit and a collision preventing unit. The fall detecting unit detects that a load of the automatic-driving vehicle has fallen onto a road. The collision preventing unit performs a collision prevention process that is a process to prevent another vehicle from colliding with the load when the fall detecting unit detects that the load has fallen onto the road.

A vehicle controller device includes a first processor. The first processor is configured to acquire biometric information of an occupant, in cases in which the compromised state is determined to have arisen, to communicate check-up information to check the well-being of the occupant using a report device provided inside the vehicle, to receive a response from the occupant whose well-being is being checked, to notify switchover information to an operation device in order to switch the vehicle from manual driving to remote driving in which the vehicle travels based on operation information in cases in which the response has not been received, and to perform the remote driving in cases in which the switchover information has been received by the operation device and operation-ready information indicating that the remote driving is possible has subsequently been received from the operation device.

Embodiments of the present disclosure relate to generating velocity profiles for an autonomous vehicle (101). An ECU (107) of the autonomous vehicle (101) receives road information from one or more sensors (106) associated with the autonomous vehicle (101). One or more parameters related to smooth movement of the autonomous vehicle on the road is determined from the road information. Further, a first velocity profile is produced using an AI model and a second velocity profile is produced using a hierarchical model, based on the one or more parameters. Furthermore, one of the first and the second velocity profile is selected by comparing the first and the second velocity profiles. The selected velocity profile has a lower value of velocity value compared to the other velocity profile. The selected velocity profile is provided to the autonomous vehicle (101) for navigating on the road (102) smoothly.

Systems and methods for detecting out-of-bounds behavior of an agent in a simulation including multiple agents and an ego vehicle may include: providing a scenario governing behavior of an agent in the simulation; operating the simulation in accordance with the scenario across multiple occurrences; collecting data regarding behavior of the agent in the scenario for each occurrence; comparing collected data regarding behavior of the agent in the scenario with a governing data set for the scenario; and reporting an out-of-bounds condition to a system user when the results of the comparison indicate that the behavior of the agent in the scenario deviates from the agent behavior in the governing data set scenario by more than a predetermined amount.

Four-wheel steering of a vehicle, e.g., in which leading wheels and trailing wheels are steered independently of each other, can provide improved maneuverability and stability. A first vehicle model may be used to determine trajectories for execution by a vehicle equipped with four-wheel steering. A second vehicle model may be used to control the vehicle relative to the determined trajectories. For instance, the second vehicle model can determine leading wheels steering angles for steering leading wheels of the vehicle and trailing wheels steering angles for steering trailing wheels of the vehicle, independently of the leading wheels.

A traffic planning method for controlling a plurality of vehicles is disclosed, wherein each vehicle occupies one node in a shared set of planning nodes and is movable to other nodes along predefined edges between pairs of the nodes in accordance with a finite set of motion commands. The method comprises: obtaining initial node occupancies of the vehicles; from said finite set of motion commands, determining a mean number of feasible motion commands in a neighborhood of the initial node occupancies; determining a search depth d which makes optimal use of a predefined computational budget; and determining a suitable sequence of motion commands by means of an optimization process which considers the search depth d.

A vehicle includes: a memory; and a processor coupled to the memory, the processor being configured to: in a case in which it has been determined that washing of the vehicle is to be carried out, transition a state of the vehicle from a current mode to a vehicle washing mode that stops a power source of the vehicle, stops operation of a first equipment that is provided at the vehicle and that displaces a movable portion of the vehicle, and permits operation of a second equipment that is provided at the vehicle and that is related to comfort within a vehicle cabin of the vehicle.

The present disclosure provides a method including determining a dispatch state of a vehicle traversing a current route based on data available from a fleet management system associated with the vehicle; selecting a driving dynamics mode for the vehicle based on the determined dispatch state, wherein the driving dynamics mode determines at least one of a route the vehicle is directed to traverse and a driving behavior of the vehicle; and operating the vehicle in the selected driving dynamics mode.

A trailer controller for mounting on a trailer, wherein the trailer controller has at least one position-determining unit which is designed to detect a current position of at least two regions predefined on a trailer, in order to determine a current position and an alignment. In addition, the invention relates to a trailer having a trailer controller and a coupling system, as well as to a method for carrying out a coupling process between a tractor unit and a trailer with a coupling system.

A Remote Trailer Maneuvering (RTM) system includes a mobile device app that identifies and processes environmental information from a photo, to identify obstacles such as walls, surrounding objects, etc. When the RTM system detects a boat, a trailer, and a boat ramp, it calculates a travel distance to drive the trailer into the water for unloading the boat. The RTM system identifies obstructions, outputs information and warnings, and prompts for information and user control feedback. Once the photo is processed, the RTM system sends control instructions to an RTM controller onboard the vehicle. The vehicle may send a responsive message to the mobile device indicating that the RTM system is ready to proceed with the maneuvering function. The RTM controller maneuvers the trailer, using an autonomous vehicle controller, to the target position using the control instructions.