Abstract: Systems and methods for random vehicle movement to prevent theft are disclosed. The vehicle system may automatically reposition to make it seem like the user is home and to reduce battery drain and flat spots on the tire. The system may randomize the start time based on the preferences of the user. At the randomly decided time, the vehicle may start up on its own and reposition itself. In addition, the system may learn a driving behavior pattern of the vehicle via machine learning based on driving behavior associated with the vehicle, such that the vehicle may perform vehicle movement based on the learned driving behavior pattern.

Abstract: Example embodiments described in this disclosure are generally directed to systems and methods for preventing vehicular mishaps. In an example method, an object detector mounted on a building or a roadside fixture, detects an object in a detection coverage area of the object detector. The object is undetectable by a collision avoidance system of a vehicle. The object detector conveys information about the object to a supervisory computer. The information can include, for example, a location and/or a direction of travel of the object (if the object is moving). The supervisory computer evaluates the information and transmits an alert to the collision avoidance system of the vehicle, so as to prevent a collision between the vehicle and the object. In an example situation, where the object is a pedestrian, the supervisory computer may also transmit a warning alert to a personal communication device of the pedestrian.

Abstract: A first image is obtained prior to a second image. Upon dividing the second image into a plurality of zones, light characteristic values for each of the plurality of zones are determined based on at least one of a brightness value, a contrast value, and an intensity value for the respective zones. A first difference score for one of the zones is determined based on a difference between corresponding light characteristic values for the one zone in the first and second images. Upon identifying an object in the one zone of the first and second images, a second difference score for the one zone is determined based on determining a difference between respective confidence scores associated with the corresponding identified objects. Upon determining at least one of the first or second difference score is greater than a respective threshold, the vehicle is operated based on the first image.

Abstract: A first image is obtained prior to a second image. Upon dividing the second image into a plurality of zones, light characteristic values for each of the plurality of zones are determined based on at least one of a brightness value, a contrast value, and an intensity value for the respective zones. A first difference score for one of the zones is determined based on a difference between corresponding light characteristic values for the one zone in the first and second images. Upon identifying an object in the one zone of the first and second images, a second difference score for the one zone is determined based on determining a difference between respective confidence scores associated with the corresponding identified objects. Upon determining at least one of the first or second difference score is greater than a respective threshold, the vehicle is operated based on the first image.

Abstract: The present disclosure relates to optimized recharging of autonomous vehicle. A device may request permission to recharge a vehicle during a trip. The device may receive, for a geographical zone, a first location associated with a first recharging station and a second location associated with a second recharging station. The device may generate a first driving route from a pick-up location with a stop at the first recharging station, and a second driving route from the pick-up location with a stop at the second recharging station. The device may assign a first value to the first driving route and a second value to the second driving route by way of an objective cost function. The device may generate driving directions corresponding to the first driving route.

Abstract: Vehicle damage identification and incident management systems and methods are provided herein. An example method can include determining occurrence of a damage event for a first vehicle based on a vehicle sensor signal, determining a location on the first vehicle where damage has occurred using the vehicle sensor signal, activating a camera on a side of the first vehicle corresponding with, or adjacent to, the location on the first vehicle where damage has occurred, determining, from camera images, identifying information related to an object captured in the camera images, and transmitting a message to a recipient that includes the identifying information.

Abstract: Systems and methods for random vehicle movement to prevent theft are disclosed. The vehicle system may automatically reposition to make it seem like the user is home and to reduce battery drain and flat spots on the tire. The system may randomize the start time based on the preferences of the user. At the randomly decided time, the vehicle may start up on its own and reposition itself. In addition, the system may learn a driving behavior pattern of the vehicle via machine learning based on driving behavior associated with the vehicle, such that the vehicle may perform vehicle movement based on the learned driving behavior pattern.

Abstract: Example embodiments described in this disclosure are generally directed to systems and methods for preventing vehicular mishaps. In an example method, an object detector mounted on a building or a roadside fixture, detects an object in a detection coverage area of the object detector. The object is undetectable by a collision avoidance system of a vehicle. The object detector conveys information about the object to a supervisory computer. The information can include, for example, a location and/or a direction of travel of the object (if the object is moving). The supervisory computer evaluates the information and transmits an alert to the collision avoidance system of the vehicle, so as to prevent a collision between the vehicle and the object. In an example situation, where the object is a pedestrian, the supervisory computer may also transmit a warning alert to a personal communication device of the pedestrian.

Abstract: Upon determining a first vehicle is moving on a two-way road in a first direction of travel in an only lane of the two-way road, vehicle sensor data is input to a first neural network that identifies a yield area along the two-way road via image segmentation. A second vehicle is detected traveling toward the first vehicle on the only lane of the two-way road. Then, upon determining the first vehicle is to yield to the second vehicle, one or more vehicle components are actuated to move the first vehicle to the yield area.

Abstract: Vehicle damage identification and incident management systems and methods are provided herein. An example method can include determining occurrence of a damage event for a first vehicle based on a vehicle sensor signal, determining a location on the first vehicle where damage has occurred using the vehicle sensor signal, activating a camera on a side of the first vehicle corresponding with, or adjacent to, the location on the first vehicle where damage has occurred, determining, from camera images, identifying information related to an object captured in the camera images, and transmitting a message to a recipient that includes the identifying information.

Abstract: Upon determining a first vehicle is moving on a two-way road in a first direction of travel in an only lane of the two-way road, vehicle sensor data is input to a first neural network that identifies a yield area along the two-way road via image segmentation. A second vehicle is detected traveling toward the first vehicle on the only lane of the two-way road. Then, upon determining the first vehicle is to yield to the second vehicle, one or more vehicle components are actuated to move the first vehicle to the yield area.

Abstract: The present disclosure relates to optimized recharging of autonomous vehicle. A device may request permission to recharge a vehicle during a trip. The device may receive, for a geographical zone, a first location associated with a first recharging station and a second location associated with a second recharging station. The device may generate a first driving route from a pick-up location with a stop at the first recharging station, and a second driving route from the pick-up location with a stop at the second recharging station. The device may assign a first value to the first driving route and a second value to the second driving route by way of an objective cost function. The device may generate driving directions corresponding to the first driving route.

Abstract: Embodiments described herein are directed to locating the position of a mobile device without an Internet or Global Positioning Service (GPS) connection using a rideshare application that utilizes multiple low-energy communication signals from nearby devices to triangulate position. A first device seeking a rideshare arrangement transmits a low-energy signal as a beacon probe. A rideshare application running on the first device determines a relative position of the first device with respect to two or more other devices using signal triangulation methods. One of the cooperating devices encodes an encrypted communication channel and establishes an encrypted link with at least the first device. The connected devices share user identification information, and the first device transmits payment information to one of the two or more devices for online or offline payment of the rideshare.