The invention relates to a system for testing a driver assistance system of a vehicle, where the driver assistance system has at least one interior sensor and is designed to process sensor signals of the at least one interior sensor for monitoring a driver of the vehicle, the system comprising: simulation means for simulating at least one physical property of the driver which characterizes a physiological condition of the driver, in particular the driver's attentiveness, activity, fatigue, mood, state of health, and/or drug influence, and is able to be detected by the at least one interior sensor such that it can generate sensor signals as a function of the at least one simulated physical property; and an interface which interacts with the driver assistance system such that sensor signals are provided the driver assistance system as a function of the at least one simulated physical property. The invention further relates to a corresponding method.

Disclosed embodiments relate to adjusting vehicle Electronic Control Unit (ECU) software versions. Operations may include receiving a prompt to adjust an ECU of a vehicle from executing a first version of ECU software to a second version of ECU software; configuring, in response to the prompt and based on a delta file corresponding to the second version of ECU software, the second version of ECU software on the ECU in the vehicle for execution; and configuring, in response to the prompt, the first version of ECU software on the ECU in the vehicle to become non-executable.

Disclosed embodiments relate to adjusting vehicle Electronic Control Unit (ECU) software versions. Operations may include receiving a prompt to adjust an ECU of a vehicle from executing a first version of ECU software to a second version of ECU software; configuring, in response to the prompt and based on a delta file corresponding to the second version of ECU software, the second version of ECU software on the ECU in the vehicle for execution; and configuring, in response to the prompt, the first version of ECU software on the ECU in the vehicle to become non-executable.

A method of determining a target path according to a source electronic control unit (ECU) mounted on a vehicle is provided. The method includes obtaining state information of a plurality of paths connecting the source ECU with a destination ECU, selecting the target path for target data from among the plurality of paths based on the state information, and transmitting the target data to the destination ECU through an ECU located on the selected target path, the state information including information about at least one of power consumption of an ECU located on the paths, a temperature of the ECU located on the paths, a latency of the paths, and a transmission success rate of the paths.

According to one example there is provided a method comprising selecting a first location from a set of locations and analysing, by a processor, data collected from a first vehicle located within a first distance of the first location. A first value representative of a first performance parameter of the first vehicle is generated. A second value representative of a second performance parameter of the first vehicle is generated. At least one of the first and second values is compared with a first threshold and, when one of the first and second values is greater than the first threshold, a safety alert is issued greater than (in some examples, less than).

Disclosed are systems and techniques for using blockchain technology to maintain and validate a vehicle ledger. The technique includes receiving, at a master node in the system, a request to update a vehicle ledger associated with a first vehicle node comprising the system. If first criteria are met, the system updates the vehicle ledger, including: generating an updated version of the vehicle ledger using vehicle data stored in a master ledger associated with the master node, and transmitting the updated version of the vehicle ledger to the first vehicle node. If the first criteria are not met, the system forgoes updating the vehicle ledger. The vehicle data corresponds to a first vehicle associated with the first vehicle node. The master ledger is implemented using a blockchain that contains vehicle records for vehicles associated with the system. The blockchain includes a first block including vehicle data corresponding to the first vehicle.

Disclosed are systems and techniques for using blockchain technology to maintain and validate a vehicle ledger. The technique includes receiving, at a master node in the system, a request to update a vehicle ledger associated with a first vehicle node comprising the system. If first criteria are met, the system updates the vehicle ledger, including: generating an updated version of the vehicle ledger using vehicle data stored in a master ledger associated with the master node, and transmitting the updated version of the vehicle ledger to the first vehicle node. If the first criteria are not met, the system forgoes updating the vehicle ledger. The vehicle data corresponds to a first vehicle associated with the first vehicle node. The master ledger is implemented using a blockchain that contains vehicle records for vehicles associated with the system. The blockchain includes a first block including vehicle data corresponding to the first vehicle.

A method can include perturbing an electric motor of a hybrid powerplant having the electric motor and a fuel powered engine. The method can include measuring a frequency response of the powerplant due to the perturbing of the electric motor to determine a health of the powerplant and/or one or more components thereof.

Systems and methods are directed to an autonomy computing system of an autonomous vehicle. The autonomy computing system can include first functional circuitry configured to generate a first output associated with a first autonomous compute function of the autonomous vehicle based on sensor data using first neural networks. The autonomy computing system can include second functional circuitry configured to generate a second output associated with the first autonomous compute function of the autonomous vehicle based on the sensor data and neural networks. The autonomy computing system can include monitoring circuitry configured to determine a difference between the first output of the first functional circuitry and the second output of the second functional circuitry. The autonomy computing system can include a vehicle control system configured to generate vehicle control signals for the autonomous vehicle based on the outputs.

Systems and methods are directed to an autonomy computing system of an autonomous vehicle. The autonomy computing system can include first functional circuitry configured to generate a first output associated with a first autonomous compute function of the autonomous vehicle based on sensor data using first neural networks. The autonomy computing system can include second functional circuitry configured to generate a second output associated with the first autonomous compute function of the autonomous vehicle based on the sensor data and neural networks. The autonomy computing system can include monitoring circuitry configured to determine a difference between the first output of the first functional circuitry and the second output of the second functional circuitry. The autonomy computing system can include a vehicle control system configured to generate vehicle control signals for the autonomous vehicle based on the outputs.