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.

The present disclosure provides a data acquisition method, apparatus, device and computer readable storage medium. According to the embodiments of the present disclosure, determination is made as to whether a preset stable driving condition is met based on acquired driving scene data of the driving scene where the vehicle is currently located, and acquired driving behavior data; if the preset stable driving condition is met, the driving scene data and the driving behavior data are acquired at a frequency lower than a preset sampling frequency. As a result, it is possible to reduce data redundancy in similar scenes and similar diving modes, reduce the amount of data, reduce occupation of the storage resources and transmission resources and facilitate subsequent analysis, and it is possible to implement the dynamic acquisition of driving scene data and driving behavior data by scenes and modes.

A method and device for operating a vehicle comprising a step of recording environment data values, which represent an environment of the vehicle, the environment comprising at least one environmental feature; a step of determining a comparative value of a comparison between the at least one environmental feature and a map, the map comprising at least one map feature, the at least one environmental feature corresponding the at least one map feature; a step of determining an up-to-dateness of the map, based on a comparison of the comparative value with a threshold value; and a step of operating the vehicle, as a function of the up-to-dateness of the map.

A method may include obtaining one or more inputs in which each of the inputs describes at least one of: a state of an autonomous vehicle (AV) or a state of an object; and identifying a prediction context of the AV based on the inputs. The method may also include determining a relevancy of each object of a plurality of objects to the AV in relation to the prediction context; and outputting a set of relevant objects based on the relevancy determination for each of the plurality of objects. Another method may include obtaining a set of objects designated as relevant to operation of an AV; selecting a trajectory prediction approach for a given object based on context of the AV and characteristics of the given object; predicting a trajectory of the given object using the selected trajectory prediction approach; and outputting the given object and the predicted trajectory.

A method for operating an autonomous driving function of a vehicle. The vehicle includes a computer unit and sensors for detecting surroundings data. The computer unit is configured to determine a setpoint trajectory for the vehicle, based on the detected surroundings data. In step a), an actual trajectory, and distances from objects in the surroundings, are detected. In step b), an ascertainment of the quality of the autonomous driving function takes place by comparing the actual trajectory to the setpoint trajectory and monitoring the detected distances from objects in the surroundings. In step c), a control of the quality to a predefined target value takes place by selecting sensors to be used for the autonomous driving function from the plurality of sensors and/or by changing a measuring rate, at which measurements are carried out, of at least one sensor from the plurality of sensors.

Disclosed herein are systems and method including a method for managing an autonomous vehicle. The method include running an autonomous vehicle that performs right-of-way movements relative to agents, recording, for a plurality of segments of time or distance, where the autonomous vehicle and where the agents are for each tick in each of the plurality of segments, running an autolabeler module on a segment of the plurality of segments to calculate a respective value for each of a plurality of right-of-way labels and using the plurality of right-of-way labels to perform one or more of spoofing an autonomous vehicle stack or to train a right-of-way machine learning model.

An autonomous driving control apparatus includes a sensor device obtaining information around an autonomous vehicle and including a plurality of sensors, a memory storing information about a high definition map around the autonomous vehicle, and a controller classifying the sensors into at least one sensor set based on the information around the autonomous vehicle and the information about the high definition map, using a sensor set classification table, monitoring a computational resource utilization rate and a resource occupancy rate of the memory, calculating a determiner input drop rate and determining whether there is an available resource, using the monitored computational resource utilization rate and the monitored resource occupancy rate, determining whether to additionally allocate at least one determiner using the determiner input drop rate and whether there is the available resource, and changing an autonomous driving determination period.

A method may include obtaining one or more inputs in which each of the inputs describes at least one of: a state of an autonomous vehicle (AV) or a state of an object; and identifying a prediction context of the AV based on the inputs. The method may also include determining a relevancy of each object of a plurality of objects to the AV in relation to the prediction context; and outputting a set of relevant objects based on the relevancy determination for each of the plurality of objects. Another method may include obtaining a set of objects designated as relevant to operation of an AV; selecting a trajectory prediction approach for a given object based on context of the AV and characteristics of the given object; predicting a trajectory of the given object using the selected trajectory prediction approach; and outputting the given object and the predicted trajectory.

Embodiments provide a vehicle computer coupled to a vehicle. The vehicle computer may be configured to compute (e.g., generate) a first (e.g., regular, main) trajectory and a second (e.g., fail-safe, minimal risk maneuver) trajectory for the vehicle. Embodiments may provide a fault detection interval during which the second trajectory may be activated due to missing updated trajectory input. Embodiments may also provide a recovery detection interval during which an updated regular trajectory may be generated and followed instead of the fail-safe trajectory if a valid trajectory update is received. Accordingly, embodiments allow for an early activation of a fail-safe trajectory compared to conventional systems, while also allowing the system to recover (e.g., revert back to a modified version of the first trajectory) if a valid trajectory update is received during a recovery detection interval.

A vehicle control method for controlling a vehicle in which a sailing control for traveling under inertia is executed when a vehicle is traveling. Each time the sailing control is executed, a history of sailing control is stored as history information classified according to situations in which sailing control was canceled. Also, a current travel situation is specified, history information corresponding to a current travel situation is specified from the stored history information, and whether to allow or disallow sailing control is determined based on the specified history information.