The systems and methods disclosed herein are configured to securely deliver items to a vehicle. In particular, a delivery is authenticated before access is provided to the vehicle.

The systems and methods disclosed herein are configured to securely deliver items to a vehicle. In particular, a delivery is authenticated before access is provided to the vehicle.

An anti-theft system for a vehicle parked and engine off, wherein the antitheft system can detect a forceful opening of a toolbox in the vehicle. The anti-theft system can include a vibration sensor coupled to the body of the vehicle and configured to detect vibration in the body due to an external impact, such as the forceful opening of the toolbox. The control unit coupled to the vibration sensor can receive a signal from the vibration sensor indicative of the external impact and in turn, can raise an alarm notifying the external impact.

An anti-theft system for a vehicle parked and engine off, wherein the antitheft system can detect a forceful opening of a toolbox in the vehicle. The anti-theft system can include a vibration sensor coupled to the body of the vehicle and configured to detect vibration in the body due to an external impact, such as the forceful opening of the toolbox. The control unit coupled to the vibration sensor can receive a signal from the vibration sensor indicative of the external impact and in turn, can raise an alarm notifying the external impact.

A method and an apparatus for user authentication of a vehicle in an autonomous driving system are disclosed. The method includes determining an authentication value indicating matching accuracy of authentication data entered for a passenger of the vehicle and authentication information of a caller of the vehicle, determining a driving setting of the vehicle based on the authentication value, driving on a pre-driving route according to the driving setting, performing decryption for encrypted data blocks related to the passenger received from an infra apparatus located on the pre-driving route, using a key value of the passenger, determining a destination of the vehicle based on whether the decryption for the encrypted data blocks succeeds or fails, and controlling the vehicle to drive to the destination. An autonomous vehicle of the present invention can be associated with artificial intelligence modules, drones (unmanned aerial vehicles (UAVs)), robots, augmented reality (AR) devices, virtual reality (VR) devices, devices related to 5G service, etc.

A method and an apparatus for user authentication of a vehicle in an autonomous driving system are disclosed. The method includes determining an authentication value indicating matching accuracy of authentication data entered for a passenger of the vehicle and authentication information of a caller of the vehicle, determining a driving setting of the vehicle based on the authentication value, driving on a pre-driving route according to the driving setting, performing decryption for encrypted data blocks related to the passenger received from an infra apparatus located on the pre-driving route, using a key value of the passenger, determining a destination of the vehicle based on whether the decryption for the encrypted data blocks succeeds or fails, and controlling the vehicle to drive to the destination. An autonomous vehicle of the present invention can be associated with artificial intelligence modules, drones (unmanned aerial vehicles (UAVs)), robots, augmented reality (AR) devices, virtual reality (VR) devices, devices related to 5G service, etc.

An unsupervised learning-based detection method according to one technical aspect of the present disclosure relates to an unsupervised learning-based detection method using a supervised-learned model, and includes: generating a first plurality of matrix data on the basis of driving data; generating encoding information by encoding the first plurality of matrix data using a convolutional neural network; modeling a time series feature of the encoding information by using a long short-term memory (LSTM) network, so as to derive a correlation between variables according to a time series; re-implementing a second plurality of matrix data through a deconvolution calculation of the correlation between the variables according to the time series; and determining whether the driving data corresponds to a pre-supervised learned driver profile, on the basis of a difference between the first plurality of matrix data and the second plurality of matrix data.

An unsupervised learning-based detection method according to one technical aspect of the present disclosure relates to an unsupervised learning-based detection method using a supervised-learned model, and includes: generating a first plurality of matrix data on the basis of driving data; generating encoding information by encoding the first plurality of matrix data using a convolutional neural network; modeling a time series feature of the encoding information by using a long short-term memory (LSTM) network, so as to derive a correlation between variables according to a time series; re-implementing a second plurality of matrix data through a deconvolution calculation of the correlation between the variables according to the time series; and determining whether the driving data corresponds to a pre-supervised learned driver profile, on the basis of a difference between the first plurality of matrix data and the second plurality of matrix data.

An encrypted intelligence networking system that takes car to car communication/C-V2X to the next stage of vehicle safety by enhancing how auto owners report vehicles stolen, while affording law enforcement tools that allow disabling and faster detection of stolen vehicles. Secured Network Intelligence That Contacts Help (SNITCH) system, designed for installation on vehicles which require DMV registration, would rely on 4G LTE and or 5G networks to quickly transmit data through secured bluetooth features, enabling stolen vehicles to release stolen status info and other vehicle information (e.g. VIN, direction of travel, owner contact information, etc.) exclusively to law enforcement vehicles within the desired range for communication. Upon detection of a stolen vehicle, officers would have the option to activate Offender Apprehension Mode (OAM), a feature designed to disable accelerating components and prevent high-speed chases that often end with deadly crashes.

Methods and systems for autonomous vehicle recharging or refueling are disclosed. Autonomous vehicles may be automatically refueled by routing the vehicles to available fueling stations when not in operation, according to methods described herein. A fuel level within a tank of an autonomous vehicle may be monitored until it reaches a refueling threshold, at which point an on-board computer may generate a predicted use profile for the vehicle. Based upon the predicted use profile, a time and location for the vehicle to refuel the vehicle may be determined. In some embodiments, the vehicle may be controlled to automatically travel to a fueling station, refill a fuel tank, and return to its starting location in order to refuel when not in use.