In some examples, a controller receives information of a route of a vehicle, and selects a first parameter set from among a plurality of parameter sets based on the route of the vehicle, the plurality of parameter sets corresponding to different conditions of usage of the vehicle, where each parameter set of the plurality of parameter sets includes one or more parameters that control adjustment of one or more respective adjustable elements of the vehicle. The controller causes application of the first parameter set to control a setting of the one or more adjustable elements of the vehicle.

An autonomous driving controller includes a plurality of parallel processors operating on common input data received from the plurality of autonomous driving sensors. Each of the plurality of parallel processors includes communication circuitry, a general processor, a security processor subsystem (SCS), and a safety subsystem (SMS). The communication circuitry supports communications between the plurality of parallel processors, including inter-processor communications between the general processors of the plurality of parallel processors, communications between the SCSs of the plurality of parallel processors using SCS cryptography, and communications between the SMSs of the plurality of parallel processors using SMS cryptography, the SMS cryptography differing from the SCS cryptography. The SCS and/or the SMS may each include dedicated hardware and/or memory to support the communications.

Some embodiments are directed to methods and apparatus for predicting traffic conditions and controlling a host vehicle based on the predicted conditions for travel along a path. The methods and apparatus can perform the following: accessing current path data relevant to a location and speed the host vehicle; detecting data, from a vehicular communications network, from a merging vehicle intending to merge into the path of the host vehicle; detecting data, from the vehicular communications network, of preceding traffic in the path of the host vehicle; determining a speed and location of the merging vehicle from the data transmitted over the vehicle communications network; determining a speed and location of preceding traffic on the path of the host vehicle from the data transmitted over the vehicular communications network; and predicting whether the speed of the preceding traffic or the speed of the merging vehicle will slow down during the merge.

The present disclosure relates to a method for determining a state of a vehicle on a road portion having two or more lanes. The method comprises obtaining map data associated with the road portion and positioning data indicating a position of the vehicle on the road and a sensor data from a sensor system of the vehicle. The method further comprises initializing a filter per lane of the road portion based on the obtained map data, the obtained positioning data, and the obtained sensor data, wherein each filter indicates an estimated state of the vehicle on the road portion. Then, selecting one of the initialized filters using a trained machine-learning algorithm, configured to use the obtained map data, the positioning data, the sensor data, and each estimated state as indicated by each filter as input and to output a current state of the vehicle on the road portion.

Systems and/or methods for controlling dual motor-dual clutch powertrains for HEV and PHEV vehicles are disclosed. In one embodiment, a method is disclosed comprising: determining the state of charge (SOC) of said batteries; determining the speed of the vehicle; if the SOC is greater than a given first threshold, selecting a charge-depleting operational mode of said vehicle; during operation of said vehicle, if the SOC is less than a given second threshold, selecting a charge-sustaining operating mode of said vehicle. In another embodiment, a system having a controller that operates the powertrain according to various embodiments is disclosed.

Disclosed herein are systems and method including a method for managing an autonomous vehicle. The method includes obtaining labels associated with various aspects of right-of-way interactions between an autonomous vehicle and an agent, wherein the right-of-way interactions occur when a human driver takes over for the autonomous vehicle and performs the right-of-way interactions, running an autonomous vehicle stack that is untrained for processing right-of-way interactions between the autonomous vehicle and the agent, injecting the labels into the autonomous vehicle stack and determining, based on the injecting of the labels into the autonomous vehicle stack, a performance of the autonomous vehicle stack.

A vehicle hitch detection system is provided. The vehicle hitch detection system includes a camera arranged to capture images of a vehicle hitch and a controller processing the images to detect a powered hitch ornament connected to the hitch based on the processed images when an electrical hitch connection is detected. The controller may further control a driver assistance system based on the detected hitch ornament to enable or disable the system.

A vehicle control device for a vehicle includes an engine; and a power converter, so that the vehicle travels by selectively executing one of a first traveling mode, a second traveling mode, and a third traveling mode in which the vehicle travels by a torque of both the engine and the rotation electric machine. Further, in a case where an air pressure around the vehicle and a system voltage are in a predetermined area of a preset map indicating a relationship between the air pressure and the system voltage, the vehicle control device is configured to reduce a switching speed of a switching element of the power converter to be lower than a normal speed, and cause the engine to operate, limit a torque of the rotation electric machine, and execute the third traveling mode.

A system for notifying emergency services of a vehicular crash may (i) receive sensor data of a vehicular crash from at least one mobile device associated with a user; (ii) generate a scenario model of the vehicular crash based upon the received sensor data; (iii) store the scenario model; and/or (iv) transmit a message to one or more emergency services based upon the scenario model. As a result, the speed and accuracy of deploying emergency services to the vehicular crash location is increased. The system may also utilize vehicle occupant positional data, and internal and external sensor data to detect potential imminent vehicle collisions, take corrective actions, automatically engage autonomous or semi-autonomous vehicle features, and/or generate virtual reconstructions of the vehicle collision.

A method of coordinating road users via a server device which determines instruction data by a central model and transmits the instruction data as action instruction to respective control circuits of the road user, the central model being a virtual image of a real environment created by the server device from environment data, includes when a road user reaches the real environment a local model is created by onboard sensor data and delta data relating to the central model is determined. The delta data is transmitted to the server device by the control circuit of the road user which creates an updated central model by the delta data and the central model. The server device determines and transmits updated instruction data to the road user as readjustment of the instruction data.