B60S13/02

Vehicle turntable

Embodiments include a vehicle turntable comprising a central hub communicatively coupled to a control unit for receiving control signals; a plurality of wedge-shaped panels radially coupled to the central hub so as to form a circular surface; a plurality of wheels coupled to a number of the plurality of panels; and at least one motor configured to drive a corresponding one of the plurality of wheels and to receive the control signals from the central hub. Embodiments also include a system comprising a first turntable hub configured to control operation of a first turntable; and a control unit communicatively coupled to the first turntable hub to provide control signals to the first turntable hub.

Vehicle turntable

Embodiments include a vehicle turntable comprising a central hub communicatively coupled to a control unit for receiving control signals; a plurality of wedge-shaped panels radially coupled to the central hub so as to form a circular surface; a plurality of wheels coupled to a number of the plurality of panels; and at least one motor configured to drive a corresponding one of the plurality of wheels and to receive the control signals from the central hub. Embodiments also include a system comprising a first turntable hub configured to control operation of a first turntable; and a control unit communicatively coupled to the first turntable hub to provide control signals to the first turntable hub.

Vehicle rotation assembly
10377352 · 2019-08-13 ·

A vehicle rotation assembly for selectively rotating a vehicle includes a base that may be positioned on a support surface. A rotation unit is coupled to the base and the rotation selectively rotates on the base. A panel is coupled to the rotation unit such that the panel is selectively rotatable on the base. A vehicle is selectively positioned on the panel thereby facilitating the vehicle to be selectively rotated.

Vehicle rotation assembly
10377352 · 2019-08-13 ·

A vehicle rotation assembly for selectively rotating a vehicle includes a base that may be positioned on a support surface. A rotation unit is coupled to the base and the rotation selectively rotates on the base. A panel is coupled to the rotation unit such that the panel is selectively rotatable on the base. A vehicle is selectively positioned on the panel thereby facilitating the vehicle to be selectively rotated.

Cognitive-based driving anomaly detection based on spatio-temporal landscape-specific driving models

Methods, systems, and computer program products for driving anomaly detection based on spatio-temporal landscape-specific driving models are provided herein. A method includes generating, for each of multiple users, a temporally-related driving skill model pertaining to one or more landscapes, wherein the model is based on temporally-related driving data associated with the users and landscape-related information of trips driven by the users; monitoring the users participating in a ride-sharing trip in a vehicle by analyzing ride-sharing trip data; detecting driving-related anomalies attributed to the monitored users by comparing the ride-sharing trip data and the respective temporally-related driving skill model for each monitored user; updating a schedule for the trip based on the detected anomalies and estimated conditions attributed to remaining portions of the trip by modifying an assignment of selected users to drive the vehicle during the remaining portions of the trip; and outputting the updated schedule to the selected users.

Cognitive-based driving anomaly detection based on spatio-temporal landscape-specific driving models

Methods, systems, and computer program products for driving anomaly detection based on spatio-temporal landscape-specific driving models are provided herein. A method includes generating, for each of multiple users, a temporally-related driving skill model pertaining to one or more landscapes, wherein the model is based on temporally-related driving data associated with the users and landscape-related information of trips driven by the users; monitoring the users participating in a ride-sharing trip in a vehicle by analyzing ride-sharing trip data; detecting driving-related anomalies attributed to the monitored users by comparing the ride-sharing trip data and the respective temporally-related driving skill model for each monitored user; updating a schedule for the trip based on the detected anomalies and estimated conditions attributed to remaining portions of the trip by modifying an assignment of selected users to drive the vehicle during the remaining portions of the trip; and outputting the updated schedule to the selected users.

COMB TOOTH TYPE TRANSPORT MECHANISM
20190055749 · 2019-02-21 ·

A comb tooth type transport mechanism includes a turntable, a rotating device disposed at the bottom of the turntable so as to drive the turntable, first comb teeth, a lifting platform, a lifting device and a positioning roller. The turntable is provided with an opening to receive the lifting platform, the lifting platform and the lifting device are disposed on the turntable, the lifting platform is lifted or lowered by the lifting device, therefore the car be jacked up and carried by carrier. The first comb teeth, which prevent the car parked on it from slipping, are fixed on the turntable and parallelly arranged at opposite sides of the lifting platform. The positioning roller is disposed on the turntable along a length direction of the first comb teeth and located at an end of the first comb teeth. The car will be turned around by the turntable for leaving easily.

INTEGRAL ROTARY TRANSPORT MECHANISM
20190055748 · 2019-02-21 ·

An integral rotary transport mechanism includes a turntable, a loading board, a lifting platform, two vehicle centering devices, a rotating device and two positioning members. A central portion of the turntable is provided with a first groove, by narrowing and extending which a second groove is formed. The lifting platform is liftable and can be lowered to a bottom of the second groove to form an activity space between the lifting platform and the loading board. The rotating device is disposed at a bottom of the turntable driven to be rotated 180 degrees. The vehicle centering device, through which cars can be accurately adjusted to middle of the loading board placed in the first groove, is disposed on the turntable and located at opposite sides of the loading board. The positioning member is disposed on the turntable and located at the other opposite sides of the loading board.

Cognitive-Based Driving Anomaly Detection Based on Spatio-Temporal Landscape-Specific Driving Models

Methods, systems, and computer program products for driving anomaly detection based on spatio-temporal landscape-specific driving models are provided herein. A method includes generating, for each of multiple users, a temporally-related driving skill model pertaining to one or more landscapes, wherein the model is based on temporally-related driving data associated with the users and landscape-related information of trips driven by the users; monitoring the users participating in a ride-sharing trip in a vehicle by analyzing ride-sharing trip data; detecting driving-related anomalies attributed to the monitored users by comparing the ride-sharing trip data and the respective temporally-related driving skill model for each monitored user; updating a schedule for the trip based on the detected anomalies and estimated conditions attributed to remaining portions of the trip by modifying an assignment of selected users to drive the vehicle during the remaining portions of the trip; and outputting the updated schedule to the selected users.

Cognitive-Based Driving Anomaly Detection Based on Spatio-Temporal Landscape-Specific Driving Models

Methods, systems, and computer program products for driving anomaly detection based on spatio-temporal landscape-specific driving models are provided herein. A method includes generating, for each of multiple users, a temporally-related driving skill model pertaining to one or more landscapes, wherein the model is based on temporally-related driving data associated with the users and landscape-related information of trips driven by the users; monitoring the users participating in a ride-sharing trip in a vehicle by analyzing ride-sharing trip data; detecting driving-related anomalies attributed to the monitored users by comparing the ride-sharing trip data and the respective temporally-related driving skill model for each monitored user; updating a schedule for the trip based on the detected anomalies and estimated conditions attributed to remaining portions of the trip by modifying an assignment of selected users to drive the vehicle during the remaining portions of the trip; and outputting the updated schedule to the selected users.