A transmission includes an input shaft and an output shaft, the input shaft selectively accepting a torque input from a prime mover, and the output shaft selectively providing torque output to a driveline. A controller determines a shaft displacement angle representing an angle value of rotational displacement difference between at least two shafts of the transmission, and performs a transmission operation responsive to the shaft displacement angle.

An autonomous driving system for an autonomous vehicle includes a plurality of on-board autonomous sensors that sense data related to operation of the autonomous vehicle and a surrounding environment and an automated driving controller in electronic communication with the plurality of on-board autonomous sensors. The automated driving controller is instructed to receive an indication one or more of the plurality of on-board autonomous sensors are non-functional and a secondary autonomous sensor system including one or more replacement sensors are installed. The automated driving controller is instructed to verify the secondary autonomous sensor system based on a security check and perform a redundancy check between the one or more replacement sensors and the plurality of on-board autonomous sensors. In response to determining the one or more replacement sensors are valid based on the redundancy check, the automated driving controller operates the autonomous vehicle in a limp home mode.

An autonomous vehicle which includes multiple independent control systems that provide redundancy as to specific and critical safety situations which may be encountered when the autonomous vehicle is in operation.

System, methods, and other embodiments described herein relate to improving the prediction efficiency of autonomous/semi-autonomous vehicles. In one embodiment, the system generates a provisional prediction according to sensor data from at least one sensor of a subject vehicle. The prediction can be associated with an aspect relating to operating the subject vehicle along a path. The system analyzes the provisional prediction in relation to a subsequent prediction about the aspect to determine a correspondence between the provisional prediction and the subsequent prediction. In response to determining that the correspondence satisfies an inaccuracy threshold, the system can store the provisional prediction and the sensor data associated with the provisional prediction to log potential inaccuracies in generating predictions based, at least in part, on the sensor data.

The vehicle assistance system includes an infrastructure sensor installed in a predetermined place to detect an obstacle, an onboard apparatus connected to an onboard sensor which is mounted on the vehicle and which detects an obstacle, and a server installed in a place different from that of the infrastructure sensor. In the infrastructure sensor, the abnormality determination unit compares the first obstacle information related to the obstacle detected by the sensor unit and the second obstacle information related to the obstacle detected by the onboard sensor existing in a location different from that of the sensor unit, and determines whether or not the sensor unit or the onboard sensor is abnormal based on the result of this comparison.

The present teaching relates to method, system, medium, and implementations for sensor calibration. An ego vehicle determines whether a sensor deployed on the ego vehicle to facilitate autonomous driving of the ego vehicle needs to be calibrated and sends, if it is determined that the sensor needs to be calibrated, a request for assistance in collaborative calibration of the sensor, with a first position of the ego vehicle or a first configuration of the sensor with respect to the ego vehicle. When a response of the request is received, an assisting vehicle is indicated to travel to be near the ego vehicle to facilitate the calibration of the sensor by collaborating with the moving ego vehicle and the ego vehicle coordinates with the assisting vehicle to enable the sensor to acquire information of a target present on the assisting vehicle for the collaborative calibration of the sensor.

Example embodiments relate to identification of proxy calibration targets for a fleet of sensors. An example method includes collecting, using a sensor coupled to a vehicle, data about one or more objects within an environment of the vehicle. The sensor has been calibrated using a ground-truth calibration target. The method also includes identifying, based on the collected data, at least one candidate object, from among the one or more objects, to be used as a proxy calibration target for other sensors coupled to vehicles within a fleet of vehicles. Further, the method includes providing, by the vehicle, data about the candidate object for use by one or more vehicles within the fleet of vehicles.

Performing in-vehicle network diagnostics is provided. A cloud system receives wiring diagnostic data from a vehicle. The wiring diagnostic data includes information with respect to electrical operation of a plurality of segments of wiring of the vehicle. A machine-learning model of the cloud system is utilized to analyze the wiring diagnostic data. Responsive to the machine-learning model identifying an issue with the electrical operation based on the wiring diagnostic data, a response is sent from the cloud system to the vehicle, the response including one or more corrective actions to be performed by the vehicle to address the issue.

In an embodiment, a method includes: receiving ambient sound; determining if the ambient sound includes a siren; in accordance with determining that the ambient sound includes a siren, determining a first location associated with the siren; receiving a camera image; determining if the camera image includes a flashing light; in accordance with determining that the camera image includes a flashing light, determining a second location associated with the flashing light; 3D data; determining if the 3D data includes an object; in accordance with determining that the 3D data includes an object, determining a third location associated with the object; determining a presence of an emergency vehicle based on the siren, detected flashing light and detected object; determining an estimated location of the emergency vehicle based on the first, second and third locations; and initiating an action related to the vehicle based on the determined presence and location.

There is provided an information processing apparatus, an information processing method, and a program, with which an abnormality of a sensor mounted on a vehicle can be detected. The information processing apparatus includes a first data acquisition unit, a second data acquisition unit, a comparison unit, and a detection unit. The first data acquisition unit acquires first data including at least one of position information of an object present in a vehicle surrounding environment or road surface tilt information and a time stamp, the position information and the road surface tilt information being generated by using sensing data of a sensor mounted on a vehicle. The second data acquisition unit acquires second data including at least one of position information of an object present in a vehicle surrounding environment or road surface tilt information and a time stamp, the position information and the road surface tilt information being generated by using sensing data of a sensor other than that of the vehicle. The comparison unit compares the first data with the second data on the basis of the time stamp included in each of the first data and the second data. The detection unit detects an abnormality related to a sensor mounted on the vehicle on the basis of a comparison result by the comparison unit.