A vehicle control system comprising: a driving control device that includes a first processor and controls a driving section at a vehicle; a control instructing device includes a second processor and controls the driving control device by giving instructions by communication to the driving control device; and a communication path connects a plurality of control devices, including the control instructing and the driving control devices, the plurality of control devices communicate with one another, wherein the vehicle control system is structured wherein the first and second processors respectively compare a communication period of communication with another control device via the communication path, and a reference period that is stored in advance, based on results of comparisons on the communication period and the reference period that have been carried out by the first processor and the second processor, the second processor judges that there is an attack on the communication path.

A vehicle control system comprising: a driving control device that includes a first processor and controls a driving section at a vehicle; a control instructing device includes a second processor and controls the driving control device by giving instructions by communication to the driving control device; and a communication path connects a plurality of control devices, including the control instructing and the driving control devices, the plurality of control devices communicate with one another, wherein the vehicle control system is structured wherein the first and second processors respectively compare a communication period of communication with another control device via the communication path, and a reference period that is stored in advance, based on results of comparisons on the communication period and the reference period that have been carried out by the first processor and the second processor, the second processor judges that there is an attack on the communication path.

A mobile object control method including: recognizing physical objects near a mobile object and a route shape; generating a target trajectory based on a result of the recognition and cause the mobile object to travel autonomously along the target trajectory; and determining that an abnormality has occurred in a control system for causing the mobile object to travel autonomously by performing the recognition when a time period from a timing when a degree of deviation between a reference target trajectory determined by the route shape and serving as a reference for generating the target trajectory and a position of the mobile object is greater than or equal to a predetermined degree to a timing when the degree of deviation is less than the predetermined degree is greater than or equal to a first predetermined time period and output a determination result.

Techniques for generating simulations for evaluating a performance of a controller of an autonomous vehicle are described. A computing system may evaluate the performance of the controller to navigate the simulation and respond to actions of one or more objects (e.g., other vehicles, bicyclists, pedestrians, etc.) in a simulation. Actions of the objects in the simulation may be controlled by the computing system (e.g., by an artificial intelligence) and/or one or more users inputting object controls, such as via a user interface. The computing system may calculate performance metrics associated with the actions performed by the vehicle in the simulation as directed by the autonomous controller. The computing system may utilize the performance metrics to verify parameters of the autonomous controller (e.g., validate the autonomous controller) and/or to train the autonomous controller utilizing machine learning techniques to bias toward preferred actions.

The invention relates to a control device for a driver assistance system, wherein the control device comprises a sensor interface via which the control device can be connected to at least one sensor module to receive data from the at least one sensor module, a power processor which is adapted to detect objects and to provide object data based on the data from the at least one sensor module, and a system interface via which the control device can be connected to a higher-level control device of the driver assistance system for forwarding object data provided by the power processor.

A platooning system for causing multiple vehicles to travel in a platoon includes: a management device configured to set an order of the vehicles in the platoon; a transmission device mounted on each vehicle and configured to transmit driving information regarding a driving state of the vehicle; a reception device mounted on each vehicle and configured to receive the driving information of at least one of the vehicles; and a control device mounted on each vehicle and configured to control an operation of the vehicle based on the driving information, wherein the management device is configured to arrange the vehicles from a front end to a rear end with respect to a travel direction in an ascending order of driving performances of the vehicles.

A computer-implemented method for safeguarding a system against false negatives. The method includes: receiving a time series of a criticality, the system including a functionality that is triggered when the criticality meets a first predetermined criterion; computing a time series of a reference, the reference being a comparison criticality for triggering the functionality; computing a time series of an error measure based on the time series of the criticality and the time series of the reference, a non-triggering of the functionality being classified as a false negative when a portion of the time series of the error measure meets a second predetermined criterion; and identifying at least one near-false negative, a non-triggering of the functionality of the system being classified as a near-false negative when a portion of the time series of the error measure meets a third predetermined criterion, but not the second predetermined criterion.

Systems and methods are provided for vehicle navigation. In one implementation, a system may comprise at least one processor. The processor may be programmed to receive images representative of an environment of the host vehicle and analyze the images to identify a first object and a second object. The processor may determine a first predefined navigational constraint implicated by the first object and a second predefined navigational constraint implicated by the second object, wherein the first and second predefined navigational constraints cannot both be satisfied, and the second predefined navigational constraint has a priority higher than the first predefined navigational constraint. The processor may determine a navigational action for the host vehicle satisfying the second predefined navigational constraint, but not satisfying the first predefined navigational constraint and, cause an adjustment of a navigational actuator of the host vehicle in response to the determined navigational action.

A method for predicting a failure of a power control unit of a vehicle is provided. The method includes obtaining data from a plurality of sensors of the power control unit of a vehicle subject to simulated multi-load conditions, implementing a machine learning algorithm on the data to obtain machine learning data, obtaining new data from the plurality of sensors of power control unit of the vehicle subject to real multi-load conditions, implementing the machine learning algorithm on the new data to obtain test data, predicting a failure of the power control unit based on a comparison between the test data and the machine learning data.

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.