An example operation includes one or more of saving a hash of data including occupant data and transport data on a transport in response to the transport is started, performing a security check that validates the hash of data against the data in response to the transport is started, and providing a resolution when the security check fails.

A Scalable service-Oriented MiddlewarE over IP (SOME/IP)-based debug system and method are provided. The SOME/IP-based debug system includes: a debug device configured to determine a debug instruction based on an input of a user; and a plurality of vehicle-mounted ECUs, where each of the vehicle-mounted ECUs includes a lower debug service configured to establish an SOME/IP communication connection with the debug device, receive the debug instruction via the SOME/IP communication connection, and execute a corresponding debug action based on the debug instruction. According to the technical solution of the present disclosure, debugging functions of vehicle-mounted ECUs can be implemented.

A Scalable service-Oriented MiddlewarE over IP (SOME/IP)-based debug system and method are provided. The SOME/IP-based debug system includes: a debug device configured to determine a debug instruction based on an input of a user; and a plurality of vehicle-mounted ECUs, where each of the vehicle-mounted ECUs includes a lower debug service configured to establish an SOME/IP communication connection with the debug device, receive the debug instruction via the SOME/IP communication connection, and execute a corresponding debug action based on the debug instruction. According to the technical solution of the present disclosure, debugging functions of vehicle-mounted ECUs can be implemented.

Methods and systems of enabling a transportation mode on a telematics device coupled to a vehicle are provided. One method includes detecting a first event or receiving a command for enabling a transportation mode, running a transportation mode power-saving scheme in response to receiving the first event or the command, and exiting the transportation mode power-saving scheme in response to detecting a second event.

A transmission includes an input shaft coupled to a prime mover, a countershaft, main shaft, and an output shaft, with gears between the countershaft and the main shaft. A shift actuator selectively couples the input shaft to the main shaft by rotatably coupling gears between the countershaft and the main shaft. The shift actuator is mounted on an exterior wall of a housing including the countershaft and the main shaft. An integrated actuator housing includes a single external power access for the shift actuator. A controller interprets a shaft displacement angle, determines if the transmission is in an imminent zero or zero torque region, and performs a transmission operation in response to the transmission in the imminent zero or zero torque region.

Upon determining a vehicle is in an off-road area based on sensor data, an off-road operation mode is enabled to an enabled state. Then, upon receiving a first user input selecting the off-road operation mode, one or more assist features are represented on a display in the vehicle. Then at least one of the assist features is selected based on a second user input. Then, after a key cycle initiated by a user that engages the vehicle from an off state to an on state, the selected assist feature is deactivated to a deactivated state.

Methods and systems for controlling an autonomous vehicle. The method includes receiving sensor data from a plurality of sensors, determining, a plurality of probability hypotheses based upon the sensor data, and receiving metadata from at least one sensor of the plurality of sensors. An integrity level of at least one of the plurality of probability hypotheses is determined based upon the received metadata and at least one action is determined based upon the determined integrity level and at least one probability hypothesis of the plurality of probability hypotheses. The at least one action is then initiated by an electronic controller for the vehicle.

A sensor aiming device includes: a target positional relationship processing unit for outputting positional relationship information of first and second targets; a sensor observation information processing unit configured to convert the observation result of the first and second targets into a predetermined unified coordinate system according to a coordinate conversion parameter, perform time synchronization at a predetermined timing, and extract first target information indicating a position of the first target and second target information indicating a position of the second target; a position estimation unit configured to estimate a position of the second target using the first target information, the second target information, and the positional relationship information; and a sensor correction amount estimation unit configured to calculate a deviation amount of the second sensor using the second target information and an estimated position of the second target and estimate a correction amount.

System and techniques for test scenario verification, for a simulation of an autonomous vehicle safety action, are described. In an example, measuring performance of a test scenario used in testing an autonomous driving safety requirement includes: defining a test environment for a test scenario that tests compliance with a safety requirement including a minimum safe distance requirement; identifying test procedures to use in the test scenario that define actions for testing the minimum safe distance requirement; identifying test parameters to use with the identified test procedures, such as velocity, amount of braking, timing of braking, and rate of acceleration or deceleration; and creating the test scenario for use in an autonomous driving test simulator. Use of the test scenario includes applying the identified test procedures and the identified test parameters to identify a response of a test vehicle to the minimum safe distance requirement.

This disclosure describes systems and methods for training a neural network with a training data set including data items labeled at different granularities. During training, each item within the training data set can be fed through the neural network. For items with labels of a higher granularity, weights of the network can be adjusted based on a comparison between the output of the network and the label of the item. For items with labels of a lower granularity, an output of the network can be fed through a conversion function that convers the output from the higher granularity to the lower granularity. The weights of the network can then be adjusted based on a comparison between the converted output and the label of the item.