A system for managing a fleet of vehicles having autonomous or semi-autonomous control based upon real-time data analysis of live driving conditions including traffic conditions, weather conditions, pollution conditions, vehicle operations, vehicle emissions, legal restrictions, and fleet conditions. Older vehicles may be retrofitted with partial or total compatibility with the system via an aftermarket dongle that connects to the vehicle through a diagnostic port.

An autonomous vehicle (AV) maintenance system engages a user of an AV to assist in maintenance tasks. A sensor interface receives data captured by a sensor of the AV. An issue detector processes the sensor data to detect an issue in the AV. A user interface module instructs a user to perform a maintenance task addressing the detected issue. A verification module verifies that the user successfully completed the maintenance task.

The disclosure generally pertains to systems and methods for assisting a driver to travel through a drive-through lane of an establishment in an autonomous mode of operation. In an example method, a processor in a vehicle determines a location of the vehicle in a drive-through lane in various ways such as, for example, based on objects located in the vicinity of the drive-through lane, based on location coordinates, and/or based on a geofence defined around the establishment. The processor may then place the vehicle in an autonomous mode of operation and instruct the driver of the vehicle to refrain from touching vehicle driving control components such as the steering wheel, the brake pedal, and the accelerator pedal. The vehicle autonomously then moves through the drive-through lane in a stop-and-go mode of movement at a controlled speed while executing lane-centering and collision avoidance.

Operating an autonomous machine using analysis of machine vibration while it is operational. Accelerometers are used to measure the machines vibrations while it is being operated. If the vibrations exceed a predetermined acceleration a controller adjust the velocity of the machine to prevent/reduce further vibrations.

Techniques are provided for autonomous vehicle operation using linear temporal logic. The techniques include using one or more processors of a vehicle to store a linear temporal logic expression defining an operating constraint for operating the vehicle. The vehicle is located at a first spatiotemporal location. The one or more processors are used to receive a second spatiotemporal location for the vehicle. The one or more processors are used to identify a motion segment for operating the vehicle from the first spatiotemporal location to the second spatiotemporal location. The one or more processors are used to determine a value of the linear temporal logic expression based on the motion segment. The one or more processors are used to generate an operational metric for operating the vehicle in accordance with the motion segment based on the determined value of the linear temporal logic expression.

Techniques relating to monitoring map consistency are described. In an example, a monitoring component associated with a vehicle can receive sensor data associated with an environment in which the vehicle is positioned. The monitoring component can generate, based at least in part on the sensor data, an estimated map of the environment, wherein the estimated map is encoded with policy information for driving within the environment. The monitoring component can then compare first information associated with a stored map of the environment with second information associated with the estimated map to determine whether the estimated map and the stored map are consistent. Component(s) associated with the vehicle can then control the object based at least in part on results of the comparing.

An autonomous vehicle uses machine learning based models such as neural networks to predict hidden context attributes associated with traffic entities. The hidden context represents behavior of the traffic entities in the traffic. The machine learning based model is configured to receive a video frame as input and output likelihoods of receiving user responses having particular ordinal values. The system uses a loss function based on cumulative histogram of user responses corresponding to various ordinal values. The system identifies user responses that are unlikely to be valid user responses to generate training data for training the machine learning mode. The system identifies invalid user responses based on response time of the user responses.

The present disclosure discloses an electric mobile apparatus, a charging station and a method for controlling an electric mobile apparatus. The An electric mobile apparatus includes an apparatus body; a first sensing module arranged on the apparatus body for sensing an electromagnetic signal transmitted from a positioning coil and outputting a first electromagnetic sensing signal; a second sensing module arranged on the apparatus body for sensing the electromagnetic signal transmitted from the positioning coil and outputting a second electromagnetic sensing signal; and a control module connected to the first sensing module and the second sensing module respectively for determining a relative position between the apparatus body and the positioning coil based on the first electromagnetic sensing signal and the second electromagnetic sensing signal, and controlling the apparatus body to move based on the relative position until the apparatus body moves to a target position.

Improved passenger access and suspension systems for passenger buses, and controllers configured for use therewith; and passenger buses incorporating such systems and controllers. Integration of access ramp, door, suspension, charge interface, and other systems provides improvements in fully- or semi-automatic deployment of features such as passenger doors and ramps, and charge interface components, as well as navigation to passenger access points.

A battery distribution method, a device, a system, equipment and a medium. The method comprises: generating a benchmark for transportation and battery swapping of a vehicle according to transportation history information and battery swapping history information of the vehicle; updating the battery swapping information of a road port according to the transportation plan of the vehicle and the benchmark; and generating a battery distribution plan of the road port according to the number of available batteries at the road port and the battery swapping information of the road port. The available batteries after the distribution in each road port can meet the actual battery swapping demand. Also, the battery distribution mode in road ports during the vehicle transportation is simplified and automated, and the accuracy and the high efficiency of battery distribution in road ports are improved.