An arrival time of a primary vehicle to arrive at a stopping location is predicted based a current path of the primary vehicle. A predicted transit time period of a secondary vehicle to move to the stopping location is received. Upon determining that a current time is a time that is the predicted transit time period subtracted from the arrival time, a message is sent from the primary vehicle to the secondary vehicle instructing the secondary vehicle to move to the stopping location.

A driving takeover control apparatus is equipped in a vehicle capable of travelling by automated driving and controls a driving takeover operation at a transition from high automated driving to low automated driving. The apparatus includes a driver operation device, a vehicle driving device, an alertness detecting device, a computation control device, and an output limiting device. A driver operates the driver operation device to steer the vehicle and adjust a vehicle speed. The vehicle driving device steers the vehicle and adjusts the vehicle speed based on an operation amount given to the driver operation device. The alertness detecting device is detects an alertness level of the driver at the transition. The computation control device drives the output limiting device to perform an output limitation operation if the alertness level is a certain level or lower. The output limiting device limits an output of the vehicle driving device.

The present disclosure provides a method and apparatus of monitoring a sensor of a driverless vehicle, a device and a storage medium, wherein the method comprises: monitoring a physical state of a to-be-monitored sensor; monitoring a data transmission state of the to-be-monitored sensor; monitoring output data of the to-be-monitored sensor, and using predetermined data to perform cross-validation for the output data; when any monitoring result gets abnormal, determining the to-be-monitored sensor as getting abnormal, and giving an alarm. The solution of the present disclosure may be applied to improve safety of the driverless vehicle.

An automotive arithmetic system includes a main arithmetic device that determines a target motion of a motor vehicle so that the motor vehicle travel on a route generated based on a vehicle external environment estimated using deep learning based on an output from a vehicle external information acquisition device; and a backup arithmetic device that determines a backup target motion for causing the motor vehicle to travel on a travel route which is generated based on the output from the vehicle external information acquisition device and which the traveling motor vehicle takes until the motor vehicle stops at a stop position that satisfies a preset criterion. The automotive arithmetic system outputs a backup control signal to actuators in preference to a control signal when the main arithmetic device fails.

A system in a vehicle can have a camera and/or a sensor. Such a camera can be configured to record a visual feature of a user in the vehicle and send data derived from the visual feature. Such a sensor can be configured to sense a non-visual feature of the user in the vehicle and send data derived from the non-visual feature. The system can have a computing system configured to receive such data from the camera and the sensor. The computing device can also be configured to determine a state of the user based on the received data derived from visual and non-visual features, as well as an AI system. The computing device can also be configured to enable or disable a function of the vehicle based at least partially on the state of the user, which the AI system can infer from the received data.

The present disclosure relates to a method performed by a trajectory monitoring system of a vehicle for assessment of a vehicle control system of an advanced driver-assistance system, ADAS, or autonomous driving, AD, system of the vehicle. The trajectory monitoring system determines in view of a reference system a planned vehicle trajectory adapted to be executed by the vehicle control system during a predeterminable time period (T). The trajectory monitoring system furthermore determines following initiation and/or completion of the time period, an actual vehicle trajectory of the vehicle during the time period in view of the reference system. Moreover, the trajectory monitoring system determines an accuracy deviation measure indicating deviation between the actual vehicle trajectory and the planned vehicle trajectory. The trajectory monitoring system further communicates acknowledgment data indicative of the accuracy deviation measure.

A redundant hardware and software architecture can be designed to enable vehicles to be operated in an autonomous mode while improving the reliability and/or safety of such vehicles. A system for redundant architecture can include a set of at least two redundant sensors coupled to a vehicle and configured to provide timestamped sensor data to each of a plurality of computing unit (CU) computers. The CU computers can process the sensor data simultaneously based on at least a time value indicative of an absolute time or a relative time and based on the timestamped sensor data. The CU computers provide to a vehicle control unit (VCU) computer at least two sets of outputs configured to instruct a plurality of devices in a vehicle and cause the vehicle to be driven.

The disclosure describes various embodiments for detecting an unexpected control state of an autonomous driving system. According to an embodiment, an exemplary method of detecting an unexpected control state of an autonomous driving system include the operations of generating environmental data of a vehicle; determining, by the autonomous driving system, a first control state based on the environmental data of the vehicle; determining, by a reference model, a second control state based on the environmental data, wherein the reference model defines at least one scenario each corresponding to a plurality of expected control states and a state switching condition, and in each of the expected control states corresponding to the scenario, an action of the vehicle in the scenario obeys a traffic rule; and determining the unexpected control state of the autonomous driving system by comparing the first control state with the second control state.

A method is disclosed for improving the permissiveness of a vehicle designed to operate within an operational design domain (“ODD”) where the vehicle has an autonomous vehicle control system capable of collecting sensor data. The method, which can be incorporated into a system or into instructions placed on storage media, includes partitioning the ODD into subsets (“micro-ODDs”) that relate to different operational situations and creating safety envelopes for those subsets. The safety envelopes are used to keep the vehicle operating safely and can be optimized to improve permissiveness of the vehicular operation.

In one embodiment, a computing system of a vehicle may receive perception data associated with a scenario encountered by a vehicle while operating in an autonomous driving mode. The system may identify the scenario based at least on the perception data. The system may generate a performance metric associated with a vehicle navigation plan to navigate the vehicle in accordance with the identified scenario. In response to a determination that the performance metric associated with the vehicle navigation plan fails to satisfy one or more criteria for navigating the vehicle in accordance with the identified scenario, the system may trigger a disengagement operation related to disengaging the vehicle from the autonomous driving mode. The system may generate a disengagement record associated with the triggered disengagement operation. The disengagement record may include information associated with the identified scenario encountered by the vehicle related to the disengagement operation.