A computer-implemented method for operating an autonomous or semi-autonomous vehicle may include identifying a vehicle operator and retrieving an associated vehicle operator profile. Operating data regarding operation of the autonomous or semi-autonomous vehicle may be received that includes data from sensors disposed within the vehicle. When a request to enable an autonomous operation feature is received, (i) autonomous operation risk levels associated with vehicle operation by the autonomous operation feature based upon the received operating data, and (ii) operator risk levels associated with vehicle operation by the vehicle operator based upon the vehicle operator profile are determined. Autonomous operation feature enablement may be allowed based upon a comparison of (i) autonomous operation risk levels with (ii) operator risk levels. As a result, only safe autonomous feature engagement may be facilitated, and risk averse vehicle owners may receive insurance discounts based upon this safe autonomous feature engagement functionality.

A server device can obtain historical location data, concerning a vehicle, captured by a global positioning system (GPS) device of the vehicle and historical engine control unit (ECU) data concerning the vehicle captured by an ECU of the vehicle. The server device can process the historical location data and the historical ECU data to train a machine learning model to determine a relationship between the historical location data and the historical ECU data. The server device can receive location data and ECU data concerning the vehicle and update the machine learning model based on the location data and the ECU data. The server device can receive real-time location data concerning the vehicle and derive an equivalent real-time ECU speed using the machine learning model. The server device can generate a message regarding the equivalent real-time ECU speed of the vehicle and send the message to a remote device for display.

This disclosure relates to method and system for detecting and compensating for mechanical fault in autonomous ground vehicle (AGV). For each of a set of trajectory plan segments along a base path during real-time navigation of the AGV, the method may include receiving a plurality of vehicle displacement parameters along a given trajectory plan segment. and determining an optimal velocity twist of the AGV in the given trajectory plan segment using an artificial intelligence (AI) model, based on the plurality of vehicle displacement parameters and a weight of the AGV. The method may further include determining the mechanical fault in the AGV based on a comparison of an actual velocity twist of the AGV in the given trajectory plan segment and the optimal velocity twist of the AGV in the given trajectory plan segment for each of the set of trajectory plan segments.

The disclosure includes embodiments that provide a digital twin-based edge server switching decision. A method includes causing a sensor set of a connected vehicle to determine a current driving context of the connected vehicle. The method includes comparing the current driving context to a set of digital twin data to determine a predicted latency for using offboard computing resources of an edge server. The method includes determining that the predicted latency for using the offboard computing resources satisfies a threshold for the predicted latency. The method includes executing a switching decision that includes deciding to use the offboard computing resources of the edge server based on the comparing of the current driving context to the set of digital twin data and the determining that the threshold for the predicted latency is satisfied.

A battery starting and charging system that monitors battery and other sensor readings; tracks vehicle state, determines a charging voltage based on battery temperature and vehicle state; sets the alternator to charge the battery with the charging voltage; determines current collected parameters based on the battery and other sensor readings; and makes vehicle start predictions based on the current collected parameters. The system can also determine whether the vehicle actually started; add the current collected parameters to a set of start events if it started, and to a set of no-start events if it didn't start. The start prediction can also be based on the sets of start and no-start events for one or multiple vehicles. The collected parameters and start predictions can also be based on collected weather data. The system can use a local interconnect network (LIN) alternator with a LIN network.

A system includes one or more processors configured to track working hours of one or more crew members of a vehicle system as the vehicle system travels. The one or more processors are configured to control the vehicle system to one or more of stop movement of the vehicle system, slow movement of the vehicle system, prevent movement of the vehicle system from a stationary position, or control movement of the vehicle system to an alternative location, responsive to one or more of (i) determining that the working hours of the one or more crew members exceed a prescribed service limit, (ii) determining that the working hours are projected to exceed the prescribed service limit before the vehicle system reaches a designated location, or (iii) determining that the working hours are within a designated time window before the prescribed service limit.

The present disclosure provides a method for piloting an electric scooter, the electric scooter and a storage medium. The method includes: receiving a driving instruction; and in response to the driving instruction, piloting the electric scooter to a target charging device according to a navigation path at least in one of the following manners: lowering a gravity center of the electric scooter; and under a condition in which an obstacle is detected, controlling the electric scooter to avoid the obstacle or wait for a predetermined period of time.

According to one embodiment, by way of example, a vehicle control device includes: an acquisition unit configured to acquire map data indicating an absolute azimuth of a linear road surface sign disposed on a road surface on which a vehicle travels, and image data obtained by imaging surroundings of the vehicle with a vehicle-mounted camera included in the vehicle; and a correction unit configured to detect the road surface sign from the acquired image data, calculate a relative azimuth of the road surface sign with respect to the vehicle on the image data, calculate a shift of an azimuth in which the vehicle moves based on the calculated relative azimuth and the absolute azimuth of the detected road surface sign indicated by the map data, and correct the azimuth in which the vehicle moves based on the calculated shift.

Methods and systems are proposed for a communication network. In one example, a system for a vehicle comprises a controller of the vehicle comprising instructions stored in memory thereof that when executed enable the controller to gather data related to a vehicle event at a location and store the data in memory in response to the location being free of a road-side unit (RSU), where the controller is further configured to establish a vehicle-to-infrastructure network in wireless communication with the vehicle at a second location different from the location.

Methods and systems for monitoring use, determining risk, and pricing insurance policies for a vehicle having one or more autonomous or semi-autonomous operation features are provided. According to certain aspects, with the customer's permission, a vehicle operator in an autonomous or semi-autonomous vehicle may be determined by a sensor disposed within the autonomous or semi-autonomous vehicle or a mobile computing device associated with the vehicle operator. A determination may be made as to whether the vehicle operator is authorized to operate the vehicle based upon the determined identity of the vehicle operator; and if the vehicle operator is not authorized, an alert may be generated and/or the vehicle may be caused to not operate. Alternatively, autonomous operation features may be automatically engaged to automatically drive the autonomous or semi-autonomous vehicle to a safe location when the operator is not authorized. Insurance discounts may be provided based upon the anti-theft functionality.