Protection circuitry for a vehicle power supply with storage units includes at least a first output circuit and a second output circuit for providing power to the storage units. At least a first microcontroller and a second microcontroller are each adapted to prevent the first output circuit or the second output circuit, or both, from providing power.

Among other things, techniques are described for operating an autonomous vehicle station. One technique involves receiving information indicating arrival of a vehicle at a station designated for a primary service. The technique further involves measuring, using at least one sensor located in the station, a first parameter associated with the vehicle. Also, the technique involves performing, based on the information and the first parameter, a first action to provide the primary service to the vehicle. Additionally, the technique involves determining, while performing the first action, a secondary service to provide to the vehicle.

Described herein is a system and method for detecting malodor within a cabin of an autonomous vehicle, wherein initiation of a remediation system and/or dispatch of the autonomous vehicle to a service hub for mitigating the malodor is based upon sensor signals that monitor the cabin of the autonomous vehicle. The remediation system can include devices such as an HVAC system or a window, which are operated to reduce the concentration of airborne molecules in the cabin of the autonomous vehicle when the concentration of airborne molecules exceeds an air quality threshold. A dispatch protocol may be initiated to dispatch the autonomous vehicle to a service hub when the malodor is associated with certain predefined incidents or when remediation fails to reduce the concentration of airborne molecules below the air quality threshold.

A system and method for utilizing aggregated weather data (AWD) for deriving road surface condition (RSC) estimates. This system and method supplements road friction estimates (RFEs) made at the vehicle level with AWD in the cloud to form the RSC estimates, which are then transmitted to the vehicles such that more accurate RFEs can be made locally, and so on. Conventional RFE physics-based models are replaced with enhanced RFE trained machine learning (ML) models accordingly. Global RSC estimates are derived for each geographical region using weather and location constraints. Thus, improved autonomous driving and driver assist functions may be implemented, better driver warnings may be provided, and safer road trips may be planned in advance based on a thorough analysis of the drivable conditions.

Protection circuitry for a vehicle power supply with storage units includes at least a first output circuit and a second output circuit for providing power to the storage units. At least a first microcontroller and a second microcontroller are each adapted to prevent the first output circuit or the second output circuit, or both, from providing power.

A vehicle-mounted control apparatus comprises a controller configured to control the drive power source of the vehicle on the basis of the control signal, wherein the controller puts the drive power source into a startable state or an unstartable state in accordance with the control signal, monitors the control signal, and when a transmission source of the control signal is determined to have malfunctioned, puts the drive power source into the startable state regardless of the control signal.

Aspects of the present disclosure relate switching between autonomous and manual driving modes. In order to do so, the vehicle’s computer may conduct a series of environmental, system, and driver checks to identify certain conditions. The computer may correct some of these conditions and also provide a driver with a checklist of tasks for completion. Once the tasks have been completed and the conditions are changed, the computer may allow the driver to switch from the manual to the autonomous driving mode. The computer may also make a determination, under certain conditions, that it would be detrimental to the driver’s safety or comfort to make a switch from the autonomous driving mode to the manual driving mode.

Aspects of the present disclosure relate switching between autonomous and manual driving modes. In order to do so, the vehicle's computer may conduct a series of environmental, system, and driver checks to identify certain conditions. The computer may correct some of these conditions and also provide a driver with a checklist of tasks for completion. Once the tasks have been completed and the conditions are changed, the computer may allow the driver to switch from the manual to the autonomous driving mode. The computer may also make a determination, under certain conditions, that it would be detrimental to the driver's safety or comfort to make a switch from the autonomous driving mode to the manual driving mode.

A control device includes a control unit and a communication unit. The control unit is configured to calculate a set value that is the set value of an air conditioner provided in the destination facility of a vehicle and is to be used by the occupants of the vehicle, based on information on the air conditioning environment in the vehicle and is configured to send the calculated set value of the air conditioner to a predetermined sending destination via the communication unit.

A vehicle system includes an electronic control unit. The electronic control unit is configured to execute a first program, a second program, and a third program. The first program is configured to recognize an object present around a vehicle, the second program is configured to store information related to the recognized object as time-series map data, and the third program is configured to predict a future position of the object based on the stored time-series map data. The first program and the third program are configured to be (i) first, individually optimized based on first training data corresponding to output of the first program and second training data corresponding to output of the third program, and (ii) then, collectively optimized based on the second training data corresponding to the output of the third program.