In a self-driving autonomous vehicle, a controller architecture includes multiple processors within the same box. Each processor monitors the others and takes appropriate safe action when needed, Some processors may run dormant or low priority redundant functions that become active when another processor is detected to have failed. The processors are independently powered and independently execute redundant algorithms from sensor data processing to actuation commands using different hardware capabilities (GPUs, processing cores, different input signals, etc.). Intentional hardware and software diversity improves fault tolerance. The resulting fault-tolerant/fail-operational system meets ISO26262 ASIL-D specifications based on a single electronic controller unit platform that can be used for self-driving vehicles.

The manual drive changing notification apparatus for a vehicle includes a communication unit configured to receive transmission data from a neighboring vehicle, a communication packet analyzer configured to analyze a communication packet of the transmission data to set a data format change level, a manual drive mode determination unit configured to analyze an autonomous driving environment of the neighboring vehicle based on the data format change level to determine a manual drive mode of a subject vehicle, a driver alarm strength setting unit configured to set a driver alarm strength for changing to the manual drive mode according to determination of the manual drive mode, and an alarm output unit configured to output an alarm according to the driver alarm strength.

A vehicle computing system onboard an autonomous vehicle can include one or more processors and one or more non-transitory computer-readable media that store instructions that, when executed by the one or more processors, cause the computing system to perform operations. The operations can include obtaining sensor data from one or more sensors of the autonomous vehicle; applying lossy compression to the sensor data to generate compressed sensor data; storing data describing the compressed sensor data; decompressing the compressed sensor data to generate decompressed sensor data; and inputting data describing the decompressed sensor data into an autonomy system comprising one or more machine-learned models. The autonomy system can be configured to control operations of the autonomous vehicle based on the decompressed sensor data.

A method for evaluating a first method for a control of an at least semi-automated mobile platform in surroundings of the mobile platform. The method includes: determining a control action using the first method for a setting of the surroundings;

determining a confidence value for the determination of the control action using the first method; determining a representation of the setting of the surroundings of the mobile platform, if the determined confidence value is lower than a trust level, in order to evaluate the first method using this setting.

According to one aspect, a method includes obtaining, at a first unit, a first signal, and storing an indication of an arrival time of the first signal in a register of the first unit. The method also includes obtaining a digital signal to be provided to a controller area network (CAN) circuit system, the CAN circuit system being included in the first unit, adding the indication to the digital signal, wherein adding the indication to the digital signal creates an updated digital signal, and providing the updated digital signal to the CAN circuit system.

A fusion system for a motor vehicle includes at least two environment sensors, a neural network coupled to the environment sensors for fusing environment information from the environment sensors, a fusion apparatus for fusing environment information from the environment sensors, and a control device coupled to the neural network and the fusion apparatus. The control device is set up to adapt the environment information fused via the neural network, depending on the environment information fused by the fusion apparatus, and to provide the adapted environment information to a driver assistance system of the motor vehicle.

A method determines at least one system state of a system using a Kalman filter. At least one measured value, measured by at least one sensor of the system, is supplied to the Kalman filter. The method includes estimating the at least one system state using the Kalman filter. An estimation result and at least one associated item of information relating to a reliability of the estimation result are output. The method further includes determining a discrepancy between at least one model value associated with the estimation result and at least one measured value associated with the estimation result, and correcting the at least one associated item of information relating to the reliability of the estimation result using the determined discrepancy.

Examples disclosed herein provide mechanisms for controlling a sensor in a multiple System on Chip (SoC) environment that allows one of the multiple System on Chips to be selected as a host System on Chip. The host System on Chip may lock the sensor to apply setting updates only from the host System on Chip that has locked the sensor. This lock may be broadcast to all sensors over an embedded data channel sent to all System on Chips receiving the sensor data. In addition, a safety monitor may be included to detect if the host System on Chip is functioning properly so that another System on Chip may be selected as a new host System on Chip.

A method for operating a control unit, in particular for a motor vehicle, the control unit including at least one execution unit for executing task programs, a first task program and a second task program being executed at least intermittently, the first task program providing data for the second task program at the end of a first predefined time interval, wherein a transfer of the data from the first task program to the second task program only takes place after a particular last execution of the first task program within a predefined second time interval for the execution of the second task program, the second time interval being longer than the first time interval.

Method for monitoring the operation of a power steering system including a steering wheel, a servo motor and a monitoring module, characterized in that said method involves a step of determining at least one temporary limit value of a parameter, a step of estimating an additional value which an assistance functionality contributes to a setpoint value of the parameter, and a step of correcting the at least one temporary limit value by the additional value.